mirror of
https://github.com/modelscope/DiffSynth-Studio.git
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Merge branch 'npu_fused' of https://github.com/Feng0w0/DiffSynth-Studio into npu_fused
This commit is contained in:
feng0w0
28
docs/en/.readthedocs.yaml
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28
docs/en/.readthedocs.yaml
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@@ -0,0 +1,28 @@
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||||
# .readthedocs.yaml
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# Read the Docs configuration file
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||||
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
|
||||
|
||||
# Required
|
||||
version: 2
|
||||
|
||||
# Set the OS, Python version and other tools you might need
|
||||
build:
|
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os: ubuntu-22.04
|
||||
tools:
|
||||
python: "3.10"
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||||
# Build documentation in the "docs/" directory with Sphinx
|
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sphinx:
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configuration: docs/en/conf.py
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# Optionally build your docs in additional formats such as PDF and ePub
|
||||
# formats:
|
||||
# - pdf
|
||||
# - epub
|
||||
|
||||
# Optional but recommended, declare the Python requirements required
|
||||
# to build your documentation
|
||||
# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
|
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python:
|
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install:
|
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- requirements: docs/requirements.txt
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@@ -1,6 +1,6 @@
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# `diffsynth.core.attention`: Attention Mechanism Implementation
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`diffsynth.core.attention` provides routing mechanisms for attention mechanism implementations, automatically selecting efficient attention implementations based on available packages in the `Python` environment and [environment variables](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_attention_implementation).
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`diffsynth.core.attention` provides routing mechanisms for attention mechanism implementations, automatically selecting efficient attention implementations based on available packages in the `Python` environment and [environment variables](../../Pipeline_Usage/Environment_Variables.md#diffsynth_attention_implementation).
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## Attention Mechanism
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@@ -46,7 +46,7 @@ Note that the dimension of the Attention Score in the attention mechanism ( $\te
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* xFormers: [GitHub](https://github.com/facebookresearch/xformers), [Documentation](https://facebookresearch.github.io/xformers/components/ops.html#module-xformers.ops)
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* PyTorch: [GitHub](https://github.com/pytorch/pytorch), [Documentation](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)
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|
||||
To call attention implementations other than `PyTorch`, please follow the instructions on their GitHub pages to install the corresponding packages. `DiffSynth-Studio` will automatically route to the corresponding implementation based on available packages in the Python environment, or can be controlled through [environment variables](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_attention_implementation).
|
||||
To call attention implementations other than `PyTorch`, please follow the instructions on their GitHub pages to install the corresponding packages. `DiffSynth-Studio` will automatically route to the corresponding implementation based on available packages in the Python environment, or can be controlled through [environment variables](../../Pipeline_Usage/Environment_Variables.md#diffsynth_attention_implementation).
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```python
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from diffsynth.core.attention import attention_forward
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@@ -8,9 +8,9 @@ This document introduces the model download and loading functionalities in `diff
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### Downloading and Loading Models from Remote Sources
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Taking the model [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny) as an example, after filling in `model_id` and `origin_file_pattern` in `ModelConfig`, the model can be automatically downloaded. By default, it downloads to the `./models` path, which can be modified through the [environment variable DIFFSYNTH_MODEL_BASE_PATH](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path).
|
||||
Taking the model [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny) as an example, after filling in `model_id` and `origin_file_pattern` in `ModelConfig`, the model can be automatically downloaded. By default, it downloads to the `./models` path, which can be modified through the [environment variable DIFFSYNTH_MODEL_BASE_PATH](../../Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path).
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By default, even if the model has already been downloaded, the program will still query the remote for any missing files. To completely disable remote requests, set the [environment variable DIFFSYNTH_SKIP_DOWNLOAD](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) to `True`.
|
||||
By default, even if the model has already been downloaded, the program will still query the remote for any missing files. To completely disable remote requests, set the [environment variable DIFFSYNTH_SKIP_DOWNLOAD](../../Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) to `True`.
|
||||
|
||||
```python
|
||||
from diffsynth.core import ModelConfig
|
||||
@@ -51,7 +51,7 @@ config = ModelConfig(path=[
|
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|
||||
### VRAM Management Configuration
|
||||
|
||||
`ModelConfig` also contains VRAM management configuration information. See [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md#more-usage-methods) for details.
|
||||
`ModelConfig` also contains VRAM management configuration information. See [VRAM Management](../../Pipeline_Usage/VRAM_management.md#more-usage-methods) for details.
|
||||
|
||||
## Model File Loading
|
||||
|
||||
@@ -103,11 +103,11 @@ print(hash_model_file([
|
||||
|
||||
The model hash value is only related to the keys and tensor shapes in the state dict of the model file, and is unrelated to the numerical values of the model parameters, file saving time, and other information. When calculating the model hash value of `.safetensors` format files, `hash_model_file` is almost instantly completed without reading the model parameters. However, when calculating the model hash value of `.bin`, `.pth`, `.ckpt`, and other binary files, all model parameters need to be read, so **we do not recommend developers to continue using these formats of files.**
|
||||
|
||||
By [writing model Config](/docs/en/Developer_Guide/Integrating_Your_Model.md#step-3-writing-model-config) and filling in model hash value and other information into `diffsynth/configs/model_configs.py`, developers can let `DiffSynth-Studio` automatically identify the model type and load it.
|
||||
By [writing model Config](../../Developer_Guide/Integrating_Your_Model.md#step-3-writing-model-config) and filling in model hash value and other information into `diffsynth/configs/model_configs.py`, developers can let `DiffSynth-Studio` automatically identify the model type and load it.
|
||||
|
||||
## Model Loading
|
||||
|
||||
`load_model` is the external entry for loading models in `diffsynth.core.loader`. It will call [skip_model_initialization](/docs/en/API_Reference/core/vram.md#skipping-model-parameter-initialization) to skip model parameter initialization. If [Disk Offload](/docs/en/Pipeline_Usage/VRAM_management.md#disk-offload) is enabled, it calls [DiskMap](/docs/en/API_Reference/core/vram.md#state-dict-disk-mapping) for lazy loading. If Disk Offload is not enabled, it calls [load_state_dict](#model-file-loading) to load model parameters. If necessary, it will also call [state dict converter](/docs/en/Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) for model format conversion. Finally, it calls `model.eval()` to switch to inference mode.
|
||||
`load_model` is the external entry for loading models in `diffsynth.core.loader`. It will call [skip_model_initialization](../../API_Reference/core/vram.md#skipping-model-parameter-initialization) to skip model parameter initialization. If [Disk Offload](../../Pipeline_Usage/VRAM_management.md#disk-offload) is enabled, it calls [DiskMap](../../API_Reference/core/vram.md#state-dict-disk-mapping) for lazy loading. If Disk Offload is not enabled, it calls [load_state_dict](#model-file-loading) to load model parameters. If necessary, it will also call [state dict converter](../../Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) for model format conversion. Finally, it calls `model.eval()` to switch to inference mode.
|
||||
|
||||
Here is a usage example with Disk Offload enabled:
|
||||
|
||||
|
||||
@@ -31,7 +31,7 @@ state_dict = load_state_dict(path, device="cpu")
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model.load_state_dict(state_dict, assign=True)
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```
|
||||
|
||||
In `DiffSynth-Studio`, all pretrained models follow this loading logic. After developers [integrate models](/docs/en/Developer_Guide/Integrating_Your_Model.md), they can directly load models quickly using this approach.
|
||||
In `DiffSynth-Studio`, all pretrained models follow this loading logic. After developers [integrate models](../../Developer_Guide/Integrating_Your_Model.md), they can directly load models quickly using this approach.
|
||||
|
||||
## State Dict Disk Mapping
|
||||
|
||||
@@ -57,10 +57,10 @@ state_dict = DiskMap(path, device="cpu") # Fast
|
||||
print(state_dict["img_in.weight"])
|
||||
```
|
||||
|
||||
`DiskMap` is the basic component of Disk Offload in `DiffSynth-Studio`. After developers [configure fine-grained VRAM management schemes](/docs/en/Developer_Guide/Enabling_VRAM_management.md), they can directly enable Disk Offload.
|
||||
`DiskMap` is the basic component of Disk Offload in `DiffSynth-Studio`. After developers [configure fine-grained VRAM management schemes](../../Developer_Guide/Enabling_VRAM_management.md), they can directly enable Disk Offload.
|
||||
|
||||
`DiskMap` is a functionality implemented using the characteristics of `.safetensors` files. Therefore, when using `.bin`, `.pth`, `.ckpt`, and other binary files, model parameters are fully loaded, which causes Disk Offload to not support these formats of files. **We do not recommend developers to continue using these formats of files.**
|
||||
|
||||
## Replacable Modules for VRAM Management
|
||||
|
||||
When `DiffSynth-Studio`'s VRAM management is enabled, the modules inside the model will be replaced with replacable modules in `diffsynth.core.vram.layers`. For usage, see [Fine-grained VRAM Management Scheme](/docs/en/Developer_Guide/Enabling_VRAM_management.md#writing-fine-grained-vram-management-schemes).
|
||||
When `DiffSynth-Studio`'s VRAM management is enabled, the modules inside the model will be replaced with replacable modules in `diffsynth.core.vram.layers`. For usage, see [Fine-grained VRAM Management Scheme](../../Developer_Guide/Enabling_VRAM_management.md#writing-fine-grained-vram-management-schemes).
|
||||
@@ -1,6 +1,6 @@
|
||||
# Building a Pipeline
|
||||
|
||||
After [integrating the required models for the Pipeline](/docs/en/Developer_Guide/Integrating_Your_Model.md), you also need to build a `Pipeline` for model inference. This document provides a standardized process for building a `Pipeline`. Developers can also refer to existing `Pipeline` implementations for construction.
|
||||
After [integrating the required models for the Pipeline](../Developer_Guide/Integrating_Your_Model.md), you also need to build a `Pipeline` for model inference. This document provides a standardized process for building a `Pipeline`. Developers can also refer to existing `Pipeline` implementations for construction.
|
||||
|
||||
The `Pipeline` implementation is located in `diffsynth/pipelines`. Each `Pipeline` contains the following essential key components:
|
||||
|
||||
@@ -79,7 +79,7 @@ This includes the following parts:
|
||||
return pipe
|
||||
```
|
||||
|
||||
Developers need to implement the logic for fetching models. The corresponding model names are the `"model_name"` in the [model Config filled in during model integration](/docs/en/Developer_Guide/Integrating_Your_Model.md#step-3-writing-model-config).
|
||||
Developers need to implement the logic for fetching models. The corresponding model names are the `"model_name"` in the [model Config filled in during model integration](../Developer_Guide/Integrating_Your_Model.md#step-3-writing-model-config).
|
||||
|
||||
Some models also need to load `tokenizer`. Extra `tokenizer_config` parameters can be added to `from_pretrained` as needed, and this part can be implemented after fetching the models.
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
# Fine-Grained VRAM Management Scheme
|
||||
|
||||
This document introduces how to write reasonable fine-grained VRAM management schemes for models, and how to use the VRAM management functions in `DiffSynth-Studio` for other external code libraries. Before reading this document, please read the document [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md).
|
||||
This document introduces how to write reasonable fine-grained VRAM management schemes for models, and how to use the VRAM management functions in `DiffSynth-Studio` for other external code libraries. Before reading this document, please read the document [VRAM Management](../Pipeline_Usage/VRAM_management.md).
|
||||
|
||||
## How Much VRAM Does a 20B Model Need?
|
||||
|
||||
@@ -124,7 +124,7 @@ module_map={
|
||||
}
|
||||
```
|
||||
|
||||
In addition, `vram_config` and `vram_limit` are also required, which have been introduced in [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md#more-usage-methods).
|
||||
In addition, `vram_config` and `vram_limit` are also required, which have been introduced in [VRAM Management](../Pipeline_Usage/VRAM_management.md#more-usage-methods).
|
||||
|
||||
Call `enable_vram_management` to enable VRAM management. Note that the `device` when loading the model is `cpu`, consistent with `offload_device`:
|
||||
|
||||
@@ -171,7 +171,7 @@ The above code only requires 2G VRAM to run the `forward` of a 20B model.
|
||||
|
||||
## Disk Offload
|
||||
|
||||
[Disk Offload](/docs/en/Pipeline_Usage/VRAM_management.md#disk-offload) is a special VRAM management scheme that needs to be enabled during the model loading process, not after the model is loaded. Usually, when the above code can run smoothly, Disk Offload can be directly enabled:
|
||||
[Disk Offload](../Pipeline_Usage/VRAM_management.md#disk-offload) is a special VRAM management scheme that needs to be enabled during the model loading process, not after the model is loaded. Usually, when the above code can run smoothly, Disk Offload can be directly enabled:
|
||||
|
||||
```python
|
||||
from diffsynth.core import load_model, enable_vram_management, AutoWrappedLinear, AutoWrappedModule
|
||||
@@ -212,7 +212,7 @@ with torch.no_grad():
|
||||
output = model(**inputs)
|
||||
```
|
||||
|
||||
Disk Offload is an extremely special VRAM management scheme. It only supports `.safetensors` format files, not binary files such as `.bin`, `.pth`, `.ckpt`, and does not support [state dict converter](/docs/en/Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) with Tensor reshape.
|
||||
Disk Offload is an extremely special VRAM management scheme. It only supports `.safetensors` format files, not binary files such as `.bin`, `.pth`, `.ckpt`, and does not support [state dict converter](../Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) with Tensor reshape.
|
||||
|
||||
If there are situations where Disk Offload cannot run normally but non-Disk Offload can run normally, please submit an issue to us on GitHub.
|
||||
|
||||
@@ -227,7 +227,7 @@ To make it easier for users to use the VRAM management function, we write the fi
|
||||
}
|
||||
```# Fine-Grained VRAM Management Scheme
|
||||
|
||||
This document introduces how to write reasonable fine-grained VRAM management schemes for models, and how to use the VRAM management functions in `DiffSynth-Studio` for other external code libraries. Before reading this document, please read the document [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md).
|
||||
This document introduces how to write reasonable fine-grained VRAM management schemes for models, and how to use the VRAM management functions in `DiffSynth-Studio` for other external code libraries. Before reading this document, please read the document [VRAM Management](../Pipeline_Usage/VRAM_management.md).
|
||||
|
||||
## How Much VRAM Does a 20B Model Need?
|
||||
|
||||
@@ -351,7 +351,7 @@ module_map={
|
||||
}
|
||||
```
|
||||
|
||||
In addition, `vram_config` and `vram_limit` are also required, which have been introduced in [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md#more-usage-methods).
|
||||
In addition, `vram_config` and `vram_limit` are also required, which have been introduced in [VRAM Management](../Pipeline_Usage/VRAM_management.md#more-usage-methods).
|
||||
|
||||
Call `enable_vram_management` to enable VRAM management. Note that the `device` when loading the model is `cpu`, consistent with `offload_device`:
|
||||
|
||||
@@ -398,7 +398,7 @@ The above code only requires 2G VRAM to run the `forward` of a 20B model.
|
||||
|
||||
## Disk Offload
|
||||
|
||||
[Disk Offload](/docs/en/Pipeline_Usage/VRAM_management.md#disk-offload) is a special VRAM management scheme that needs to be enabled during the model loading process, not after the model is loaded. Usually, when the above code can run smoothly, Disk Offload can be directly enabled:
|
||||
[Disk Offload](../Pipeline_Usage/VRAM_management.md#disk-offload) is a special VRAM management scheme that needs to be enabled during the model loading process, not after the model is loaded. Usually, when the above code can run smoothly, Disk Offload can be directly enabled:
|
||||
|
||||
```python
|
||||
from diffsynth.core import load_model, enable_vram_management, AutoWrappedLinear, AutoWrappedModule
|
||||
@@ -439,7 +439,7 @@ with torch.no_grad():
|
||||
output = model(**inputs)
|
||||
```
|
||||
|
||||
Disk Offload is an extremely special VRAM management scheme. It only supports `.safetensors` format files, not binary files such as `.bin`, `.pth`, `.ckpt`, and does not support [state dict converter](/docs/en/Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) with Tensor reshape.
|
||||
Disk Offload is an extremely special VRAM management scheme. It only supports `.safetensors` format files, not binary files such as `.bin`, `.pth`, `.ckpt`, and does not support [state dict converter](../Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) with Tensor reshape.
|
||||
|
||||
If there are situations where Disk Offload cannot run normally but non-Disk Offload can run normally, please submit an issue to us on GitHub.
|
||||
|
||||
|
||||
@@ -183,4 +183,4 @@ Loaded model: {
|
||||
|
||||
## Step 5: Writing Model VRAM Management Scheme
|
||||
|
||||
`DiffSynth-Studio` supports complex VRAM management. See [Enabling VRAM Management](/docs/en/Developer_Guide/Enabling_VRAM_management.md) for details.
|
||||
`DiffSynth-Studio` supports complex VRAM management. See [Enabling VRAM Management](../Developer_Guide/Enabling_VRAM_management.md) for details.
|
||||
@@ -1,6 +1,6 @@
|
||||
# Integrating Model Training
|
||||
|
||||
After [integrating models](/docs/en/Developer_Guide/Integrating_Your_Model.md) and [implementing Pipeline](/docs/en/Developer_Guide/Building_a_Pipeline.md), the next step is to integrate model training functionality.
|
||||
After [integrating models](../Developer_Guide/Integrating_Your_Model.md) and [implementing Pipeline](../Developer_Guide/Building_a_Pipeline.md), the next step is to integrate model training functionality.
|
||||
|
||||
## Training-Inference Consistent Pipeline Modification
|
||||
|
||||
|
||||
20
docs/en/Makefile
Normal file
20
docs/en/Makefile
Normal file
@@ -0,0 +1,20 @@
|
||||
# Minimal makefile for Sphinx documentation
|
||||
#
|
||||
|
||||
# You can set these variables from the command line, and also
|
||||
# from the environment for the first two.
|
||||
SPHINXOPTS ?=
|
||||
SPHINXBUILD ?= sphinx-build
|
||||
SOURCEDIR = .
|
||||
BUILDDIR = _build
|
||||
|
||||
# Put it first so that "make" without argument is like "make help".
|
||||
help:
|
||||
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
||||
|
||||
.PHONY: help Makefile
|
||||
|
||||
# Catch-all target: route all unknown targets to Sphinx using the new
|
||||
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
|
||||
%: Makefile
|
||||
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
||||
@@ -14,7 +14,7 @@ cd DiffSynth-Studio
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
|
||||
For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
|
||||
|
||||
## Quick Start
|
||||
|
||||
@@ -81,31 +81,31 @@ graph LR;
|
||||
|
||||
| Model ID | Extra Parameters | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
|
||||
| - | - | - | - | - | - | - | - |
|
||||
| [black-forest-labs/FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev) | | [code](/examples/flux/model_inference/FLUX.1-dev.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev.py) | [code](/examples/flux/model_training/full/FLUX.1-dev.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev.py) |
|
||||
| [black-forest-labs/FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev) | | [code](/examples/flux/model_inference/FLUX.1-Krea-dev.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py) | [code](/examples/flux/model_training/full/FLUX.1-Krea-dev.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py) | [code](/examples/flux/model_training/lora/FLUX.1-Krea-dev.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py) |
|
||||
| [black-forest-labs/FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev) | `kontext_images` | [code](/examples/flux/model_inference/FLUX.1-Kontext-dev.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py) | [code](/examples/flux/model_training/full/FLUX.1-Kontext-dev.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py) | [code](/examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py) |
|
||||
| [alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta) | `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py) |
|
||||
| [InstantX/FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha) | `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py) |
|
||||
| [jasperai/Flux.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler) | `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py) |
|
||||
| [InstantX/FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter) | `ipadapter_images`, `ipadapter_scale` | [code](/examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py) |
|
||||
| [ByteDance/InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou) | `infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py) |
|
||||
| [DiffSynth-Studio/Eligen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen) | `eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint` | [code](/examples/flux/model_inference/FLUX.1-dev-EliGen.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py) | - | - | [code](/examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py) |
|
||||
| [DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev) | `lora_encoder_inputs`, `lora_encoder_scale` | [code](/examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py) | - | - |
|
||||
| [DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev) | | [code](/examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py) | - | - | - | - | - |
|
||||
| [stepfun-ai/Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit) | `step1x_reference_image` | [code](/examples/flux/model_inference/Step1X-Edit.py) | [code](/examples/flux/model_inference_low_vram/Step1X-Edit.py) | [code](/examples/flux/model_training/full/Step1X-Edit.sh) | [code](/examples/flux/model_training/validate_full/Step1X-Edit.py) | [code](/examples/flux/model_training/lora/Step1X-Edit.sh) | [code](/examples/flux/model_training/validate_lora/Step1X-Edit.py) |
|
||||
| [ostris/Flex.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview) | `flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop` | [code](/examples/flux/model_inference/FLEX.2-preview.py) | [code](/examples/flux/model_inference_low_vram/FLEX.2-preview.py) | [code](/examples/flux/model_training/full/FLEX.2-preview.sh) | [code](/examples/flux/model_training/validate_full/FLEX.2-preview.py) | [code](/examples/flux/model_training/lora/FLEX.2-preview.sh) | [code](/examples/flux/model_training/validate_lora/FLEX.2-preview.py) |
|
||||
| [DiffSynth-Studio/Nexus-GenV2](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2) | `nexus_gen_reference_image` | [code](/examples/flux/model_inference/Nexus-Gen-Editing.py) | [code](/examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py) | [code](/examples/flux/model_training/full/Nexus-Gen.sh) | [code](/examples/flux/model_training/validate_full/Nexus-Gen.py) | [code](/examples/flux/model_training/lora/Nexus-Gen.sh) | [code](/examples/flux/model_training/validate_lora/Nexus-Gen.py) |
|
||||
| [black-forest-labs/FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev.py) |
|
||||
| [black-forest-labs/FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-Krea-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-Krea-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-Krea-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py) |
|
||||
| [black-forest-labs/FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev) | `kontext_images` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-Kontext-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-Kontext-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py) |
|
||||
| [alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta) | `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py) |
|
||||
| [InstantX/FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha) | `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py) |
|
||||
| [jasperai/Flux.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler) | `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py) |
|
||||
| [InstantX/FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter) | `ipadapter_images`, `ipadapter_scale` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py) |
|
||||
| [ByteDance/InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou) | `infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py) |
|
||||
| [DiffSynth-Studio/Eligen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen) | `eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-EliGen.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py) |
|
||||
| [DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev) | `lora_encoder_inputs`, `lora_encoder_scale` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py) | - | - |
|
||||
| [DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py) | - | - | - | - | - |
|
||||
| [stepfun-ai/Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit) | `step1x_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/Step1X-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/Step1X-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/Step1X-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/Step1X-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/Step1X-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/Step1X-Edit.py) |
|
||||
| [ostris/Flex.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview) | `flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLEX.2-preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLEX.2-preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLEX.2-preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLEX.2-preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLEX.2-preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLEX.2-preview.py) |
|
||||
| [DiffSynth-Studio/Nexus-GenV2](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2) | `nexus_gen_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/Nexus-Gen-Editing.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/Nexus-Gen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/Nexus-Gen.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/Nexus-Gen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/Nexus-Gen.py) |
|
||||
|
||||
Special Training Scripts:
|
||||
|
||||
* Differential LoRA Training: [doc](/docs/en/Training/Differential_LoRA.md), [code](/examples/flux/model_training/special/differential_training/)
|
||||
* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md), [code](/examples/flux/model_training/special/fp8_training/)
|
||||
* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md), [code](/examples/flux/model_training/special/split_training/)
|
||||
* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md), [code](/examples/flux/model_training/lora/FLUX.1-dev-Distill-LoRA.sh)
|
||||
* Differential LoRA Training: [doc](../Training/Differential_LoRA.md)
|
||||
* FP8 Precision Training: [doc](../Training/FP8_Precision.md)
|
||||
* Two-stage Split Training: [doc](../Training/Split_Training.md)
|
||||
* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md)
|
||||
|
||||
## Model Inference
|
||||
|
||||
Models are loaded via `FluxImagePipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
|
||||
Models are loaded via `FluxImagePipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).
|
||||
|
||||
Input parameters for `FluxImagePipeline` inference include:
|
||||
|
||||
@@ -143,11 +143,11 @@ Input parameters for `FluxImagePipeline` inference include:
|
||||
* `flex_control_stop`: Flex model control stop timestep.
|
||||
* `nexus_gen_reference_image`: Nexus-Gen model reference image.
|
||||
|
||||
If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
|
||||
If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
|
||||
|
||||
## Model Training
|
||||
|
||||
FLUX series models are uniformly trained through [`examples/flux/model_training/train.py`](/examples/flux/model_training/train.py), and the script parameters include:
|
||||
FLUX series models are uniformly trained through [`examples/flux/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/train.py), and the script parameters include:
|
||||
|
||||
* General Training Parameters
|
||||
* Dataset Basic Configuration
|
||||
@@ -198,4 +198,4 @@ We have built a sample image dataset for your testing. You can download this dat
|
||||
modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
|
||||
```
|
||||
|
||||
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
|
||||
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
|
||||
|
||||
@@ -21,7 +21,7 @@ cd DiffSynth-Studio
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
|
||||
For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
|
||||
|
||||
## Quick Start
|
||||
|
||||
@@ -61,22 +61,22 @@ image.save("image.jpg")
|
||||
|
||||
| Model ID | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
|
||||
| - | - | - | - | - | - | - |
|
||||
|[black-forest-labs/FLUX.2-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-dev)|[code](/examples/flux2/model_inference/FLUX.2-dev.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-dev.py)|-|-|[code](/examples/flux2/model_training/lora/FLUX.2-dev.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-dev.py)|
|
||||
|[black-forest-labs/FLUX.2-klein-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B)|[code](/examples/flux2/model_inference/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-4B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-4B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-4B.py)|
|
||||
|[black-forest-labs/FLUX.2-klein-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-9B)|[code](/examples/flux2/model_inference/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-9B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-9B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-9B.py)|
|
||||
|[black-forest-labs/FLUX.2-klein-base-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-4B)|[code](/examples/flux2/model_inference/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-base-4B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-base-4B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-4B.py)|
|
||||
|[black-forest-labs/FLUX.2-klein-base-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-9B)|[code](/examples/flux2/model_inference/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-9B.py)|
|
||||
|[black-forest-labs/FLUX.2-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-dev)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference/FLUX.2-dev.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference_low_vram/FLUX.2-dev.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/lora/FLUX.2-dev.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_lora/FLUX.2-dev.py)|
|
||||
|[black-forest-labs/FLUX.2-klein-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference/FLUX.2-klein-4B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference_low_vram/FLUX.2-klein-4B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/full/FLUX.2-klein-4B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_full/FLUX.2-klein-4B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/lora/FLUX.2-klein-4B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_lora/FLUX.2-klein-4B.py)|
|
||||
|[black-forest-labs/FLUX.2-klein-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-9B)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference/FLUX.2-klein-9B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference_low_vram/FLUX.2-klein-9B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/full/FLUX.2-klein-9B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_full/FLUX.2-klein-9B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/lora/FLUX.2-klein-9B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_lora/FLUX.2-klein-9B.py)|
|
||||
|[black-forest-labs/FLUX.2-klein-base-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-4B)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference/FLUX.2-klein-base-4B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-4B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/full/FLUX.2-klein-base-4B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_full/FLUX.2-klein-base-4B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/lora/FLUX.2-klein-base-4B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-4B.py)|
|
||||
|[black-forest-labs/FLUX.2-klein-base-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-9B)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference/FLUX.2-klein-base-9B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-9B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/full/FLUX.2-klein-base-9B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_full/FLUX.2-klein-base-9B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/lora/FLUX.2-klein-base-9B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-9B.py)|
|
||||
|
||||
Special Training Scripts:
|
||||
|
||||
* Differential LoRA Training: [doc](/docs/en/Training/Differential_LoRA.md)
|
||||
* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md)
|
||||
* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md)
|
||||
* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md)
|
||||
* Differential LoRA Training: [doc](../Training/Differential_LoRA.md)
|
||||
* FP8 Precision Training: [doc](../Training/FP8_Precision.md)
|
||||
* Two-stage Split Training: [doc](../Training/Split_Training.md)
|
||||
* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md)
|
||||
|
||||
## Model Inference
|
||||
|
||||
Models are loaded via `Flux2ImagePipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
|
||||
Models are loaded via `Flux2ImagePipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).
|
||||
|
||||
Input parameters for `Flux2ImagePipeline` inference include:
|
||||
|
||||
@@ -95,11 +95,11 @@ Input parameters for `Flux2ImagePipeline` inference include:
|
||||
* `tile_stride`: Tile stride during VAE encoding/decoding stages, default is 64, only effective when `tiled=True`, must be less than or equal to `tile_size`.
|
||||
* `progress_bar_cmd`: Progress bar, default is `tqdm.tqdm`. Can be disabled by setting to `lambda x:x`.
|
||||
|
||||
If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
|
||||
If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
|
||||
|
||||
## Model Training
|
||||
|
||||
FLUX.2 series models are uniformly trained through [`examples/flux2/model_training/train.py`](/examples/flux2/model_training/train.py), and the script parameters include:
|
||||
FLUX.2 series models are uniformly trained through [`examples/flux2/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/train.py), and the script parameters include:
|
||||
|
||||
* General Training Parameters
|
||||
* Dataset Basic Configuration
|
||||
@@ -148,4 +148,4 @@ We have built a sample image dataset for your testing. You can download this dat
|
||||
modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
|
||||
```
|
||||
|
||||
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
|
||||
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
|
||||
|
||||
116
docs/en/Model_Details/LTX-2.md
Normal file
116
docs/en/Model_Details/LTX-2.md
Normal file
@@ -0,0 +1,116 @@
|
||||
# LTX-2
|
||||
|
||||
LTX-2 is a series of audio-video generation models developed by Lightricks.
|
||||
|
||||
## Installation
|
||||
|
||||
Before using this project for model inference and training, please install DiffSynth-Studio first.
|
||||
|
||||
```shell
|
||||
git clone https://github.com/modelscope/DiffSynth-Studio.git
|
||||
cd DiffSynth-Studio
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
For more information about installation, please refer to [Installation Dependencies](../Pipeline_Usage/Setup.md).
|
||||
|
||||
## Quick Start
|
||||
|
||||
Run the following code to quickly load the [Lightricks/LTX-2](https://www.modelscope.cn/models/Lightricks/LTX-2) model and perform inference. VRAM management has been enabled, and the framework will automatically control model parameter loading based on remaining VRAM. It can run with a minimum of 8GB VRAM.
|
||||
|
||||
```python
|
||||
import torch
|
||||
from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig
|
||||
from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2
|
||||
|
||||
vram_config = {
|
||||
"offload_dtype": torch.float8_e5m2,
|
||||
"offload_device": "cpu",
|
||||
"onload_dtype": torch.float8_e5m2,
|
||||
"onload_device": "cpu",
|
||||
"preparing_dtype": torch.float8_e5m2,
|
||||
"preparing_device": "cuda",
|
||||
"computation_dtype": torch.bfloat16,
|
||||
"computation_device": "cuda",
|
||||
}
|
||||
pipe = LTX2AudioVideoPipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config),
|
||||
ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors", **vram_config),
|
||||
],
|
||||
tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"),
|
||||
vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
|
||||
)
|
||||
prompt = "A girl is very happy, she is speaking: \"I enjoy working with Diffsynth-Studio, it's a perfect framework.\""
|
||||
negative_prompt = "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts."
|
||||
height, width, num_frames = 512, 768, 121
|
||||
video, audio = pipe(
|
||||
prompt=prompt,
|
||||
negative_prompt=negative_prompt,
|
||||
seed=43,
|
||||
height=height,
|
||||
width=width,
|
||||
num_frames=num_frames,
|
||||
tiled=True,
|
||||
)
|
||||
write_video_audio_ltx2(
|
||||
video=video,
|
||||
audio=audio,
|
||||
output_path='ltx2_onestage.mp4',
|
||||
fps=24,
|
||||
audio_sample_rate=24000,
|
||||
)
|
||||
```
|
||||
|
||||
## Model Overview
|
||||
|Model ID|Additional Parameters|Inference|Low VRAM Inference|Full Training|Validation After Full Training|LoRA Training|Validation After LoRA Training|
|
||||
|-|-|-|-|-|-|-|-|
|
||||
|[Lightricks/LTX-2: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-OneStage.py)|-|-|-|-|
|
||||
|[Lightricks/LTX-2: TwoStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-TwoStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-TwoStage.py)|-|-|-|-|
|
||||
|[Lightricks/LTX-2: DistilledPipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-DistilledPipeline.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-DistilledPipeline.py)|-|-|-|-|
|
||||
|[Lightricks/LTX-2: OneStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-I2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-OneStage.py)|-|-|-|-|
|
||||
|[Lightricks/LTX-2: TwoStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-I2AV-TwoStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-TwoStage.py)|-|-|-|-|
|
||||
|[Lightricks/LTX-2: DistilledPipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-I2AV-DistilledPipeline.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-DistilledPipeline.py)|-|-|-|-|
|
||||
|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-In](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-In)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-In.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-In.py)|-|-|-|-|
|
||||
|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Out](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Out)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Out.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Out.py)|-|-|-|-|
|
||||
|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Left](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Left)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Left.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Left.py)|-|-|-|-|
|
||||
|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Right](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Right)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Right.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Right.py)|-|-|-|-|
|
||||
|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Up](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Up)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Jib-Up.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Jib-Up.py)|-|-|-|-|
|
||||
|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Down](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Down)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Jib-Down.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Jib-Down.py)|-|-|-|-|
|
||||
|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Static](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Static)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Static.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Static.py)|-|-|-|-|
|
||||
|
||||
## Model Inference
|
||||
|
||||
Models are loaded through `LTX2AudioVideoPipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models) for details.
|
||||
|
||||
Input parameters for `LTX2AudioVideoPipeline` inference include:
|
||||
|
||||
* `prompt`: Prompt describing the content appearing in the video.
|
||||
* `negative_prompt`: Negative prompt describing content that should not appear in the video, default value is `""`.
|
||||
* `cfg_scale`: Classifier-free guidance parameter, default value is 3.0.
|
||||
* `input_images`: List of input images for image-to-video generation.
|
||||
* `input_images_indexes`: Frame index list of input images in the video.
|
||||
* `input_images_strength`: Strength of input images, default value is 1.0.
|
||||
* `denoising_strength`: Denoising strength, range is 0～1, default value is 1.0.
|
||||
* `seed`: Random seed. Default is `None`, which means completely random.
|
||||
* `rand_device`: Computing device for generating random Gaussian noise matrix, default is `"cpu"`. When set to `cuda`, different results will be generated on different GPUs.
|
||||
* `height`: Video height, must be a multiple of 32 (single-stage) or 64 (two-stage).
|
||||
* `width`: Video width, must be a multiple of 32 (single-stage) or 64 (two-stage).
|
||||
* `num_frames`: Number of video frames, default value is 121, must be a multiple of 8 + 1.
|
||||
* `num_inference_steps`: Number of inference steps, default value is 40.
|
||||
* `tiled`: Whether to enable VAE tiling inference, default is `True`. When set to `True`, it can significantly reduce VRAM usage during VAE encoding/decoding stages, with slight errors and minor inference time extension.
|
||||
* `tile_size_in_pixels`: Pixel tiling size during VAE encoding/decoding stages, default is 512.
|
||||
* `tile_overlap_in_pixels`: Pixel tiling overlap size during VAE encoding/decoding stages, default is 128.
|
||||
* `tile_size_in_frames`: Frame tiling size during VAE encoding/decoding stages, default is 128.
|
||||
* `tile_overlap_in_frames`: Frame tiling overlap size during VAE encoding/decoding stages, default is 24.
|
||||
* `use_two_stage_pipeline`: Whether to use two-stage pipeline, default is `False`.
|
||||
* `use_distilled_pipeline`: Whether to use distilled pipeline, default is `False`.
|
||||
* `progress_bar_cmd`: Progress bar, default is `tqdm.tqdm`. Can be set to `lambda x:x` to hide the progress bar.
|
||||
|
||||
If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the previous "Supported Inference Scripts" section.
|
||||
|
||||
## Model Training
|
||||
|
||||
The LTX-2 series models currently do not support training functionality. We will add related support as soon as possible.
|
||||
@@ -2,7 +2,7 @@
|
||||
|
||||
## Qwen-Image
|
||||
|
||||
Documentation: [./Qwen-Image.md](/docs/en/Model_Details/Qwen-Image.md)
|
||||
Documentation: [./Qwen-Image.md](../Model_Details/Qwen-Image.md)
|
||||
|
||||
<details>
|
||||
|
||||
@@ -69,23 +69,23 @@ graph LR;
|
||||
|
||||
| Model ID | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
|
||||
| - | - | - | - | - | - | - |
|
||||
| [Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image) | [code](/examples/qwen_image/model_inference/Qwen-Image.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image.py) |
|
||||
| [Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit) | [code](/examples/qwen_image/model_inference/Qwen-Image-Edit.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py) |
|
||||
| [Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509) | [code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen) | [code](/examples/qwen_image/model_inference/Qwen-Image-EliGen.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) | [code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster) | [code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full) | [code](/examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA) | [code](/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny) | [code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth) | [code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint) | [code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union) | [code](/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix) | [code](/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py) | - | - | - | - |
|
||||
| [Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image.py) |
|
||||
| [Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py) |
|
||||
| [Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-EliGen.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py) | - | - | - | - |
|
||||
|
||||
## FLUX Series
|
||||
|
||||
Documentation: [./FLUX.md](/docs/en/Model_Details/FLUX.md)
|
||||
Documentation: [./FLUX.md](../Model_Details/FLUX.md)
|
||||
|
||||
<details>
|
||||
|
||||
@@ -149,24 +149,24 @@ graph LR;
|
||||
|
||||
| Model ID | Extra Parameters | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
|
||||
| - | - | - | - | - | - | - | - |
|
||||
| [black-forest-labs/FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev) | | [code](/examples/flux/model_inference/FLUX.1-dev.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev.py) | [code](/examples/flux/model_training/full/FLUX.1-dev.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev.py) |
|
||||
| [black-forest-labs/FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev) | | [code](/examples/flux/model_inference/FLUX.1-Krea-dev.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py) | [code](/examples/flux/model_training/full/FLUX.1-Krea-dev.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py) | [code](/examples/flux/model_training/lora/FLUX.1-Krea-dev.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py) |
|
||||
| [black-forest-labs/FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev) | `kontext_images` | [code](/examples/flux/model_inference/FLUX.1-Kontext-dev.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py) | [code](/examples/flux/model_training/full/FLUX.1-Kontext-dev.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py) | [code](/examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py) |
|
||||
| [alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta) | `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py) |
|
||||
| [InstantX/FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha) | `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py) |
|
||||
| [jasperai/Flux.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler) | `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py) |
|
||||
| [InstantX/FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter) | `ipadapter_images`, `ipadapter_scale` | [code](/examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py) |
|
||||
| [ByteDance/InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou) | `infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py) |
|
||||
| [DiffSynth-Studio/Eligen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen) | `eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint` | [code](/examples/flux/model_inference/FLUX.1-dev-EliGen.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py) | - | - | [code](/examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py) |
|
||||
| [DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev) | `lora_encoder_inputs`, `lora_encoder_scale` | [code](/examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py) | - | - |
|
||||
| [DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev) | | [code](/examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py) | - | - | - | - | - |
|
||||
| [stepfun-ai/Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit) | `step1x_reference_image` | [code](/examples/flux/model_inference/Step1X-Edit.py) | [code](/examples/flux/model_inference_low_vram/Step1X-Edit.py) | [code](/examples/flux/model_training/full/Step1X-Edit.sh) | [code](/examples/flux/model_training/validate_full/Step1X-Edit.py) | [code](/examples/flux/model_training/lora/Step1X-Edit.sh) | [code](/examples/flux/model_training/validate_lora/Step1X-Edit.py) |
|
||||
| [ostris/Flex.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview) | `flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop` | [code](/examples/flux/model_inference/FLEX.2-preview.py) | [code](/examples/flux/model_inference_low_vram/FLEX.2-preview.py) | [code](/examples/flux/model_training/full/FLEX.2-preview.sh) | [code](/examples/flux/model_training/validate_full/FLEX.2-preview.py) | [code](/examples/flux/model_training/lora/FLEX.2-preview.sh) | [code](/examples/flux/model_training/validate_lora/FLEX.2-preview.py) |
|
||||
| [DiffSynth-Studio/Nexus-GenV2](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2) | `nexus_gen_reference_image` | [code](/examples/flux/model_inference/Nexus-Gen-Editing.py) | [code](/examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py) | [code](/examples/flux/model_training/full/Nexus-Gen.sh) | [code](/examples/flux/model_training/validate_full/Nexus-Gen.py) | [code](/examples/flux/model_training/lora/Nexus-Gen.sh) | [code](/examples/flux/model_training/validate_lora/Nexus-Gen.py) |
|
||||
| [black-forest-labs/FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev.py) |
|
||||
| [black-forest-labs/FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-Krea-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-Krea-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-Krea-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py) |
|
||||
| [black-forest-labs/FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev) | `kontext_images` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-Kontext-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-Kontext-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py) |
|
||||
| [alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta) | `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py) |
|
||||
| [InstantX/FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha) | `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py) |
|
||||
| [jasperai/Flux.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler) | `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py) |
|
||||
| [InstantX/FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter) | `ipadapter_images`, `ipadapter_scale` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py) |
|
||||
| [ByteDance/InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou) | `infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py) |
|
||||
| [DiffSynth-Studio/Eligen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen) | `eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-EliGen.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py) |
|
||||
| [DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev) | `lora_encoder_inputs`, `lora_encoder_scale` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py) | - | - |
|
||||
| [DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py) | - | - | - | - | - |
|
||||
| [stepfun-ai/Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit) | `step1x_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/Step1X-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/Step1X-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/Step1X-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/Step1X-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/Step1X-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/Step1X-Edit.py) |
|
||||
| [ostris/Flex.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview) | `flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLEX.2-preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLEX.2-preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLEX.2-preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLEX.2-preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLEX.2-preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLEX.2-preview.py) |
|
||||
| [DiffSynth-Studio/Nexus-GenV2](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2) | `nexus_gen_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/Nexus-Gen-Editing.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/Nexus-Gen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/Nexus-Gen.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/Nexus-Gen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/Nexus-Gen.py) |
|
||||
|
||||
## Wan Series
|
||||
|
||||
Documentation: [./Wan.md](/docs/en/Model_Details/Wan.md)
|
||||
Documentation: [./Wan.md](../Model_Details/Wan.md)
|
||||
|
||||
<details>
|
||||
|
||||
@@ -254,38 +254,38 @@ graph LR;
|
||||
|
||||
| Model ID | Extra Parameters | Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
|
||||
| - | - | - | - | - | - | - |
|
||||
| [Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) | | [code](/examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py) |
|
||||
| [Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) | | [code](/examples/wanvideo/model_inference/Wan2.1-T2V-14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py) |
|
||||
| [Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py) |
|
||||
| [Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py) |
|
||||
| [Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py) |
|
||||
| [iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py) |
|
||||
| [Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py) |
|
||||
| [Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-VACE-14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py) |
|
||||
| [PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control) | `control_video` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control) | `control_video` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control) | `control_video`, `reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control) | `control_video`, `reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera) | `control_camera_video`, `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera) | `control_camera_video`, `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py) |
|
||||
| [DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1) | `motion_bucket_id` | [code](/examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py) |
|
||||
| [krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video) | | [code](/examples/wanvideo/model_inference/krea-realtime-video.py) | [code](/examples/wanvideo/model_training/full/krea-realtime-video.sh) | [code](/examples/wanvideo/model_training/validate_full/krea-realtime-video.py) | [code](/examples/wanvideo/model_training/lora/krea-realtime-video.sh) | [code](/examples/wanvideo/model_training/validate_lora/krea-realtime-video.py) |
|
||||
| [meituan-longcat/LongCat-Video](https://www.modelscope.cn/models/meituan-longcat/LongCat-Video) | `longcat_video` | [code](/examples/wanvideo/model_inference/LongCat-Video.py) | [code](/examples/wanvideo/model_training/full/LongCat-Video.sh) | [code](/examples/wanvideo/model_training/validate_full/LongCat-Video.py) | [code](/examples/wanvideo/model_training/lora/LongCat-Video.sh) | [code](/examples/wanvideo/model_training/validate_lora/LongCat-Video.py) |
|
||||
| [ByteDance/Video-As-Prompt-Wan2.1-14B](https://modelscope.cn/models/ByteDance/Video-As-Prompt-Wan2.1-14B) | `vap_video`, `vap_prompt` | [code](/examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py) | [code](/examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py) | [code](/examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py) |
|
||||
| [Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B) | | [code](/examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py) |
|
||||
| [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py) |
|
||||
| [Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py) |
|
||||
| [Wan-AI/Wan2.2-Animate-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-Animate-14B) | `input_image`, `animate_pose_video`, `animate_face_video`, `animate_inpaint_video`, `animate_mask_video` | [code](/examples/wanvideo/model_inference/Wan2.2-Animate-14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py) |
|
||||
| [Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B) | `input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video` | [code](/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-S2V-14B.py) |
|
||||
| [PAI/Wan2.2-VACE-Fun-A14B](https://www.modelscope.cn/models/PAI/Wan2.2-VACE-Fun-A14B) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py) |
|
||||
| [PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py) |
|
||||
| [PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control) | `control_video`, `reference_image` | [code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py) |
|
||||
| [PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera) | `control_camera_video`, `input_image` | [code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py) |
|
||||
| [Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py) |
|
||||
| [Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-T2V-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py) |
|
||||
| [Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py) |
|
||||
| [Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py) |
|
||||
| [Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py) |
|
||||
| [iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py) |
|
||||
| [Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py) |
|
||||
| [Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-VACE-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py) |
|
||||
| [PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control) | `control_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control) | `control_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control) | `control_video`, `reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control) | `control_video`, `reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera) | `control_camera_video`, `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera) | `control_camera_video`, `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py) |
|
||||
| [DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1) | `motion_bucket_id` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py) |
|
||||
| [krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/krea-realtime-video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/krea-realtime-video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/krea-realtime-video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/krea-realtime-video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/krea-realtime-video.py) |
|
||||
| [meituan-longcat/LongCat-Video](https://www.modelscope.cn/models/meituan-longcat/LongCat-Video) | `longcat_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/LongCat-Video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/LongCat-Video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/LongCat-Video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/LongCat-Video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/LongCat-Video.py) |
|
||||
| [ByteDance/Video-As-Prompt-Wan2.1-14B](https://modelscope.cn/models/ByteDance/Video-As-Prompt-Wan2.1-14B) | `vap_video`, `vap_prompt` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py) |
|
||||
| [Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py) |
|
||||
| [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py) |
|
||||
| [Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py) |
|
||||
| [Wan-AI/Wan2.2-Animate-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-Animate-14B) | `input_image`, `animate_pose_video`, `animate_face_video`, `animate_inpaint_video`, `animate_mask_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Animate-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py) |
|
||||
| [Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B) | `input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-S2V-14B.py) |
|
||||
| [PAI/Wan2.2-VACE-Fun-A14B](https://www.modelscope.cn/models/PAI/Wan2.2-VACE-Fun-A14B) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py) |
|
||||
| [PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py) |
|
||||
| [PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control) | `control_video`, `reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py) |
|
||||
| [PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera) | `control_camera_video`, `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py) |
|
||||
|
||||
* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md), [code](/examples/wanvideo/model_training/special/fp8_training/)
|
||||
* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md), [code](/examples/wanvideo/model_training/special/split_training/)
|
||||
* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md), [code](/examples/wanvideo/model_training/special/direct_distill/)
|
||||
* FP8 Precision Training: [doc](../Training/FP8_Precision.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo/model_training/special/fp8_training/)
|
||||
* Two-stage Split Training: [doc](../Training/Split_Training.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo/model_training/special/split_training/)
|
||||
* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo/model_training/special/direct_distill/)
|
||||
|
||||
@@ -14,7 +14,7 @@ cd DiffSynth-Studio
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
|
||||
For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
|
||||
|
||||
## Quick Start
|
||||
|
||||
@@ -80,35 +80,41 @@ graph LR;
|
||||
|
||||
| Model ID | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
|
||||
| - | - | - | - | - | - | - |
|
||||
| [Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image) | [code](/examples/qwen_image/model_inference/Qwen-Image.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image.py) |
|
||||
|[Qwen/Qwen-Image-2512](https://www.modelscope.cn/models/Qwen/Qwen-Image-2512)|[code](/examples/qwen_image/model_inference/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-2512.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-2512.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-2512.py)|
|
||||
| [Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit) | [code](/examples/qwen_image/model_inference/Qwen-Image-Edit.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py) |
|
||||
| [Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509) | [code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py) |
|
||||
|[Qwen/Qwen-Image-Edit-2511](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2511)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py)|
|
||||
|[Qwen/Qwen-Image-Layered](https://www.modelscope.cn/models/Qwen/Qwen-Image-Layered)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered.py)|
|
||||
|[DiffSynth-Studio/Qwen-Image-Layered-Control](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control.py)|
|
||||
| [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen) | [code](/examples/qwen_image/model_inference/Qwen-Image-EliGen.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) | [code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster) | [code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full) | [code](/examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA) | [code](/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny) | [code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth) | [code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint) | [code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union) | [code](/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix) | [code](/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py) | - | - | - | - |
|
||||
|[DiffSynth-Studio/Qwen-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-i2L)|[code](/examples/qwen_image/model_inference/Qwen-Image-i2L.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-i2L.py)|-|-|-|-|
|
||||
| [Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image.py) |
|
||||
|[Qwen/Qwen-Image-2512](https://www.modelscope.cn/models/Qwen/Qwen-Image-2512)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-2512.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-2512.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-2512.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-2512.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-2512.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-2512.py)|
|
||||
| [Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py) |
|
||||
| [Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py) |
|
||||
|[Qwen/Qwen-Image-Edit-2511](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2511)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py)|
|
||||
|[lightx2v/Qwen-Image-Edit-2511-Lightning](https://modelscope.cn/models/lightx2v/Qwen-Image-Edit-2511-Lightning)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-Lightning.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511-Lightning.py)|-|-|-|-|
|
||||
|[Qwen/Qwen-Image-Layered](https://www.modelscope.cn/models/Qwen/Qwen-Image-Layered)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Layered.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Layered.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Layered.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered.py)|
|
||||
|[DiffSynth-Studio/Qwen-Image-Layered-Control](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Layered-Control.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Layered-Control.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered-Control.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control.py)|
|
||||
| [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-EliGen.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py) |
|
||||
| [DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py) | - | - | - | - |
|
||||
|[DiffSynth-Studio/Qwen-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-i2L)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-i2L.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-i2L.py)|-|-|-|-|
|
||||
|
||||
Special Training Scripts:
|
||||
|
||||
* Differential LoRA Training: [doc](/docs/en/Training/Differential_LoRA.md), [code](/examples/qwen_image/model_training/special/differential_training/)
|
||||
* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md), [code](/examples/qwen_image/model_training/special/fp8_training/)
|
||||
* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md), [code](/examples/qwen_image/model_training/special/split_training/)
|
||||
* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md), [code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)
|
||||
* Differential LoRA Training: [doc](../Training/Differential_LoRA.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/qwen_image/model_training/special/differential_training/)
|
||||
* FP8 Precision Training: [doc](../Training/FP8_Precision.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/qwen_image/model_training/special/fp8_training/)
|
||||
* Two-stage Split Training: [doc](../Training/Split_Training.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/qwen_image/model_training/special/split_training/)
|
||||
* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md), [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)
|
||||
|
||||
DeepSpeed ZeRO Stage 3 Training: The Qwen-Image series models support DeepSpeed ZeRO Stage 3 training, which partitions the model across multiple GPUs. Taking full parameter training of the Qwen-Image model as an example, the following modifications are required:
|
||||
|
||||
* `--config_file examples/qwen_image/model_training/full/accelerate_config_zero3.yaml`
|
||||
* `--initialize_model_on_cpu`
|
||||
|
||||
## Model Inference
|
||||
|
||||
Models are loaded via `QwenImagePipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
|
||||
Models are loaded via `QwenImagePipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).
|
||||
|
||||
Input parameters for `QwenImagePipeline` inference include:
|
||||
|
||||
@@ -139,11 +145,11 @@ Input parameters for `QwenImagePipeline` inference include:
|
||||
* `tile_stride`: Tile stride during VAE encoding/decoding stages, default is 64, only effective when `tiled=True`, must be less than or equal to `tile_size`.
|
||||
* `progress_bar_cmd`: Progress bar, default is `tqdm.tqdm`. Can be disabled by setting to `lambda x:x`.
|
||||
|
||||
If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
|
||||
If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
|
||||
|
||||
## Model Training
|
||||
|
||||
Qwen-Image series models are uniformly trained through [`examples/qwen_image/model_training/train.py`](/examples/qwen_image/model_training/train.py), and the script parameters include:
|
||||
Qwen-Image series models are uniformly trained through [`examples/qwen_image/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/train.py), and the script parameters include:
|
||||
|
||||
* General Training Parameters
|
||||
* Dataset Basic Configuration
|
||||
@@ -193,4 +199,4 @@ We have built a sample image dataset for your testing. You can download this dat
|
||||
modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
|
||||
```
|
||||
|
||||
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
|
||||
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
|
||||
|
||||
@@ -14,7 +14,7 @@ cd DiffSynth-Studio
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
|
||||
For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
|
||||
|
||||
## Quick Start
|
||||
|
||||
@@ -106,45 +106,50 @@ graph LR;
|
||||
|
||||
| Model ID | Extra Parameters | Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
|
||||
| - | - | - | - | - | - | - |
|
||||
| [Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) | | [code](/examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py) |
|
||||
| [Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) | | [code](/examples/wanvideo/model_inference/Wan2.1-T2V-14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py) |
|
||||
| [Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py) |
|
||||
| [Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py) |
|
||||
| [Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py) |
|
||||
| [iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py) |
|
||||
| [Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py) |
|
||||
| [Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-VACE-14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py) |
|
||||
| [PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control) | `control_video` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control) | `control_video` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control) | `control_video`, `reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control) | `control_video`, `reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera) | `control_camera_video`, `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera) | `control_camera_video`, `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py) |
|
||||
| [DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1) | `motion_bucket_id` | [code](/examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py) |
|
||||
| [krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video) | | [code](/examples/wanvideo/model_inference/krea-realtime-video.py) | [code](/examples/wanvideo/model_training/full/krea-realtime-video.sh) | [code](/examples/wanvideo/model_training/validate_full/krea-realtime-video.py) | [code](/examples/wanvideo/model_training/lora/krea-realtime-video.sh) | [code](/examples/wanvideo/model_training/validate_lora/krea-realtime-video.py) |
|
||||
| [meituan-longcat/LongCat-Video](https://www.modelscope.cn/models/meituan-longcat/LongCat-Video) | `longcat_video` | [code](/examples/wanvideo/model_inference/LongCat-Video.py) | [code](/examples/wanvideo/model_training/full/LongCat-Video.sh) | [code](/examples/wanvideo/model_training/validate_full/LongCat-Video.py) | [code](/examples/wanvideo/model_training/lora/LongCat-Video.sh) | [code](/examples/wanvideo/model_training/validate_lora/LongCat-Video.py) |
|
||||
| [ByteDance/Video-As-Prompt-Wan2.1-14B](https://modelscope.cn/models/ByteDance/Video-As-Prompt-Wan2.1-14B) | `vap_video`, `vap_prompt` | [code](/examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py) | [code](/examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py) | [code](/examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py) |
|
||||
| [Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B) | | [code](/examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py) |
|
||||
| [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py) |
|
||||
| [Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py) |
|
||||
| [Wan-AI/Wan2.2-Animate-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-Animate-14B) | `input_image`, `animate_pose_video`, `animate_face_video`, `animate_inpaint_video`, `animate_mask_video` | [code](/examples/wanvideo/model_inference/Wan2.2-Animate-14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py) |
|
||||
| [Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B) | `input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video` | [code](/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-S2V-14B.py) |
|
||||
| [PAI/Wan2.2-VACE-Fun-A14B](https://www.modelscope.cn/models/PAI/Wan2.2-VACE-Fun-A14B) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py) |
|
||||
| [PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py) |
|
||||
| [PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control) | `control_video`, `reference_image` | [code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py) |
|
||||
| [PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera) | `control_camera_video`, `input_image` | [code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py) |
|
||||
| [Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py) |
|
||||
| [Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-T2V-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py) |
|
||||
| [Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py) |
|
||||
| [Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py) |
|
||||
| [Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py) |
|
||||
| [iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py) |
|
||||
| [Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py) |
|
||||
| [Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-VACE-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py) |
|
||||
| [PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control) | `control_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control) | `control_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control) | `control_video`, `reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control) | `control_video`, `reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera) | `control_camera_video`, `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) |
|
||||
| [PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera) | `control_camera_video`, `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py) |
|
||||
| [DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1) | `motion_bucket_id` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py) |
|
||||
| [krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/krea-realtime-video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/krea-realtime-video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/krea-realtime-video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/krea-realtime-video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/krea-realtime-video.py) |
|
||||
| [meituan-longcat/LongCat-Video](https://www.modelscope.cn/models/meituan-longcat/LongCat-Video) | `longcat_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/LongCat-Video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/LongCat-Video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/LongCat-Video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/LongCat-Video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/LongCat-Video.py) |
|
||||
| [ByteDance/Video-As-Prompt-Wan2.1-14B](https://modelscope.cn/models/ByteDance/Video-As-Prompt-Wan2.1-14B) | `vap_video`, `vap_prompt` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py) |
|
||||
| [Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py) |
|
||||
| [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py) |
|
||||
| [Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py) |
|
||||
| [Wan-AI/Wan2.2-Animate-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-Animate-14B) | `input_image`, `animate_pose_video`, `animate_face_video`, `animate_inpaint_video`, `animate_mask_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Animate-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py) |
|
||||
| [Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B) | `input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-S2V-14B.py) |
|
||||
| [PAI/Wan2.2-VACE-Fun-A14B](https://www.modelscope.cn/models/PAI/Wan2.2-VACE-Fun-A14B) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py) |
|
||||
| [PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py) |
|
||||
| [PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control) | `control_video`, `reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py) |
|
||||
| [PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera) | `control_camera_video`, `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py) |
|
||||
|
||||
* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md), [code](/examples/wanvideo/model_training/special/fp8_training/)
|
||||
* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md), [code](/examples/wanvideo/model_training/special/split_training/)
|
||||
* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md), [code](/examples/wanvideo/model_training/special/direct_distill/)
|
||||
* FP8 Precision Training: [doc](../Training/FP8_Precision.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo/model_training/special/fp8_training/)
|
||||
* Two-stage Split Training: [doc](../Training/Split_Training.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo/model_training/special/split_training/)
|
||||
* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo/model_training/special/direct_distill/)
|
||||
|
||||
DeepSpeed ZeRO Stage 3 Training: The Wan series models support DeepSpeed ZeRO Stage 3 training, which partitions the model across multiple GPUs. Taking full parameter training of the Wan2.1-T2V-14B model as an example, the following modifications are required:
|
||||
|
||||
* `--config_file examples/wanvideo/model_training/full/accelerate_config_zero3.yaml`
|
||||
* `--initialize_model_on_cpu`
|
||||
|
||||
## Model Inference
|
||||
|
||||
Models are loaded via `WanVideoPipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
|
||||
Models are loaded via `WanVideoPipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).
|
||||
|
||||
Input parameters for `WanVideoPipeline` inference include:
|
||||
|
||||
@@ -194,11 +199,11 @@ Input parameters for `WanVideoPipeline` inference include:
|
||||
* `tea_cache_model_id`: Model ID used by TeaCache.
|
||||
* `progress_bar_cmd`: Progress bar, default is `tqdm.tqdm`. Can be disabled by setting to `lambda x:x`.
|
||||
|
||||
If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
|
||||
If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
|
||||
|
||||
## Model Training
|
||||
|
||||
Wan series models are uniformly trained through [`examples/wanvideo/model_training/train.py`](/examples/wanvideo/model_training/train.py), and the script parameters include:
|
||||
Wan series models are uniformly trained through [`examples/wanvideo/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/train.py), and the script parameters include:
|
||||
|
||||
* General Training Parameters
|
||||
* Dataset Basic Configuration
|
||||
@@ -249,4 +254,4 @@ We have built a sample video dataset for your testing. You can download this dat
|
||||
modelscope download --dataset DiffSynth-Studio/example_video_dataset --local_dir ./data/example_video_dataset
|
||||
```
|
||||
|
||||
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
|
||||
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
|
||||
|
||||
@@ -12,7 +12,7 @@ cd DiffSynth-Studio
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
|
||||
For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
|
||||
|
||||
## Quick Start
|
||||
|
||||
@@ -50,18 +50,23 @@ image.save("image.jpg")
|
||||
|
||||
## Model Overview
|
||||
|
||||
| Model ID | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
|
||||
| - | - | - | - | - | - | - |
|
||||
| [Tongyi-MAI/Z-Image-Turbo](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo) | [code](/examples/z_image/model_inference/Z-Image-Turbo.py) | [code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo.py) | [code](/examples/z_image/model_training/full/Z-Image-Turbo.sh) | [code](/examples/z_image/model_training/validate_full/Z-Image-Turbo.py) | [code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh) | [code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo.py) |
|
||||
|Model ID|Inference|Low VRAM Inference|Full Training|Validation After Full Training|LoRA Training|Validation After LoRA Training|
|
||||
|-|-|-|-|-|-|-|
|
||||
|[Tongyi-MAI/Z-Image](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference/Z-Image.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference_low_vram/Z-Image.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/full/Z-Image.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_full/Z-Image.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_lora/Z-Image.py)|
|
||||
|[DiffSynth-Studio/Z-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference/Z-Image-i2L.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference_low_vram/Z-Image-i2L.py)|-|-|-|-|
|
||||
|[Tongyi-MAI/Z-Image-Turbo](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference/Z-Image-Turbo.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference_low_vram/Z-Image-Turbo.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/full/Z-Image-Turbo.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_full/Z-Image-Turbo.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_lora/Z-Image-Turbo.py)|
|
||||
|[PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|
|
||||
|[PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|
|
||||
|[PAI/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|
|
||||
|
||||
Special Training Scripts:
|
||||
|
||||
* Differential LoRA Training: [doc](/docs/en/Training/Differential_LoRA.md), [code](/examples/z_image/model_training/special/differential_training/)
|
||||
* Trajectory Imitation Distillation Training (Experimental Feature): [code](/examples/z_image/model_training/special/trajectory_imitation/)
|
||||
* Differential LoRA Training: [doc](../Training/Differential_LoRA.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/z_image/model_training/special/differential_training/)
|
||||
* Trajectory Imitation Distillation Training (Experimental Feature): [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/z_image/model_training/special/trajectory_imitation/)
|
||||
|
||||
## Model Inference
|
||||
|
||||
Models are loaded via `ZImagePipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
|
||||
Models are loaded via `ZImagePipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).
|
||||
|
||||
Input parameters for `ZImagePipeline` inference include:
|
||||
|
||||
@@ -75,12 +80,15 @@ Input parameters for `ZImagePipeline` inference include:
|
||||
* `seed`: Random seed. Default is `None`, meaning completely random.
|
||||
* `rand_device`: Computing device for generating random Gaussian noise matrix, default is `"cpu"`. When set to `cuda`, different GPUs will produce different generation results.
|
||||
* `num_inference_steps`: Number of inference steps, default value is 8.
|
||||
* `controlnet_inputs`: Inputs for ControlNet models.
|
||||
* `edit_image`: Edit images for image editing models, supporting multiple images.
|
||||
* `positive_only_lora`: LoRA weights used only in positive prompts.
|
||||
|
||||
If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
|
||||
If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
|
||||
|
||||
## Model Training
|
||||
|
||||
Z-Image series models are uniformly trained through [`examples/z_image/model_training/train.py`](/examples/z_image/model_training/train.py), and the script parameters include:
|
||||
Z-Image series models are uniformly trained through [`examples/z_image/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/train.py), and the script parameters include:
|
||||
|
||||
* General Training Parameters
|
||||
* Dataset Basic Configuration
|
||||
@@ -129,13 +137,13 @@ We have built a sample image dataset for your testing. You can download this dat
|
||||
modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
|
||||
```
|
||||
|
||||
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
|
||||
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
|
||||
|
||||
Training Tips:
|
||||
|
||||
* [Tongyi-MAI/Z-Image-Turbo](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo) is a distilled acceleration model. Therefore, direct training will quickly cause the model to lose its acceleration capability. The effect of inference with "acceleration configuration" (`num_inference_steps=8`, `cfg_scale=1`) becomes worse, while the effect of inference with "no acceleration configuration" (`num_inference_steps=30`, `cfg_scale=2`) becomes better. The following training and inference schemes can be adopted:
|
||||
* Standard SFT Training ([code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)) + No Acceleration Configuration Inference
|
||||
* Differential LoRA Training ([code](/examples/z_image/model_training/special/differential_training/)) + Acceleration Configuration Inference
|
||||
* Standard SFT Training ([code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo.sh)) + No Acceleration Configuration Inference
|
||||
* Differential LoRA Training ([code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/z_image/model_training/special/differential_training/)) + Acceleration Configuration Inference
|
||||
* An additional LoRA needs to be loaded in differential LoRA training, e.g., [ostris/zimage_turbo_training_adapter](https://www.modelscope.cn/models/ostris/zimage_turbo_training_adapter)
|
||||
* Standard SFT Training ([code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)) + Trajectory Imitation Distillation Training ([code](/examples/z_image/model_training/special/trajectory_imitation/)) + Acceleration Configuration Inference
|
||||
* Standard SFT Training ([code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)) + Load Distillation Acceleration LoRA During Inference ([model](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-Turbo-DistillPatch)) + Acceleration Configuration Inference
|
||||
* Standard SFT Training ([code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo.sh)) + Trajectory Imitation Distillation Training ([code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/z_image/model_training/special/trajectory_imitation/)) + Acceleration Configuration Inference
|
||||
* Standard SFT Training ([code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo.sh)) + Load Distillation Acceleration LoRA During Inference ([model](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-Turbo-DistillPatch)) + Acceleration Configuration Inference
|
||||
|
||||
@@ -28,7 +28,7 @@ Model download root directory. Can be set to any local path. If `local_model_pat
|
||||
|
||||
## `DIFFSYNTH_ATTENTION_IMPLEMENTATION`
|
||||
|
||||
Attention mechanism implementation method. Can be set to `flash_attention_3`, `flash_attention_2`, `sage_attention`, `xformers`, or `torch`. See [`./core/attention.md`](/docs/en/API_Reference/core/attention.md) for details.
|
||||
Attention mechanism implementation method. Can be set to `flash_attention_3`, `flash_attention_2`, `sage_attention`, `xformers`, or `torch`. See [`./core/attention.md`](../API_Reference/core/attention.md) for details.
|
||||
|
||||
## `DIFFSYNTH_DISK_MAP_BUFFER_SIZE`
|
||||
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
|
||||
`DiffSynth-Studio` supports various GPUs and NPUs. This document explains how to run model inference and training on these devices.
|
||||
|
||||
Before you begin, please follow the [Installation Guide](/docs/en/Pipeline_Usage/Setup.md) to install the required GPU/NPU dependencies.
|
||||
Before you begin, please follow the [Installation Guide](../Pipeline_Usage/Setup.md) to install the required GPU/NPU dependencies.
|
||||
|
||||
## NVIDIA GPU
|
||||
|
||||
@@ -58,6 +58,14 @@ video = pipe(
|
||||
save_video(video, "video.mp4", fps=15, quality=5)
|
||||
```
|
||||
|
||||
#### USP(Unified Sequence Parallel)
|
||||
If you want to use this feature on NPU, please install additional third-party libraries as follows:
|
||||
```shell
|
||||
pip install git+https://github.com/feifeibear/long-context-attention.git
|
||||
pip install git+https://github.com/xdit-project/xDiT.git
|
||||
```
|
||||
|
||||
|
||||
### Training
|
||||
NPU startup script samples have been added for each type of model,the scripts are stored in the `examples/xxx/special/npu_training`, for example `examples/wanvideo/model_training/special/npu_training/Wan2.2-T2V-A14B-NPU.sh`.
|
||||
|
||||
@@ -82,4 +90,5 @@ Set 0 or not set: indicates not enabling the binding function
|
||||
| Model | Parameter | Note |
|
||||
|----------------|---------------------------|-------------------|
|
||||
| Wan 14B series | --initialize_model_on_cpu | The 14B model needs to be initialized on the CPU |
|
||||
| Qwen-Image series | --initialize_model_on_cpu | The model needs to be initialized on the CPU |
|
||||
| Z-Image series | --enable_npu_patch | Using NPU fusion operator to replace the corresponding operator in Z-image model to improve the performance of the model on NPU |
|
||||
@@ -22,7 +22,7 @@ pipe = QwenImagePipeline.from_pretrained(
|
||||
)
|
||||
```
|
||||
|
||||
Where `torch_dtype` and `device` are computation precision and computation device (not model precision and device). `model_configs` can be configured in multiple ways for model paths. For how models are loaded internally in this project, please refer to [`diffsynth.core.loader`](/docs/en/API_Reference/core/loader.md).
|
||||
Where `torch_dtype` and `device` are computation precision and computation device (not model precision and device). `model_configs` can be configured in multiple ways for model paths. For how models are loaded internally in this project, please refer to [`diffsynth.core.loader`](../API_Reference/core/loader.md).
|
||||
|
||||
<details>
|
||||
|
||||
@@ -34,7 +34,7 @@ Where `torch_dtype` and `device` are computation precision and computation devic
|
||||
> ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
|
||||
> ```
|
||||
>
|
||||
> Model files are downloaded to the `./models` path by default, which can be modified through [environment variable DIFFSYNTH_MODEL_BASE_PATH](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path).
|
||||
> Model files are downloaded to the `./models` path by default, which can be modified through [environment variable DIFFSYNTH_MODEL_BASE_PATH](../Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path).
|
||||
|
||||
</details>
|
||||
|
||||
@@ -61,7 +61,7 @@ Where `torch_dtype` and `device` are computation precision and computation devic
|
||||
|
||||
</details>
|
||||
|
||||
By default, even after models have been downloaded, the program will still query remotely for missing files. To completely disable remote requests, set [environment variable DIFFSYNTH_SKIP_DOWNLOAD](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) to `True`.
|
||||
By default, even after models have been downloaded, the program will still query remotely for missing files. To completely disable remote requests, set [environment variable DIFFSYNTH_SKIP_DOWNLOAD](../Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) to `True`.
|
||||
|
||||
```shell
|
||||
import os
|
||||
@@ -69,7 +69,7 @@ os.environ["DIFFSYNTH_SKIP_DOWNLOAD"] = "True"
|
||||
import diffsynth
|
||||
```
|
||||
|
||||
To download models from [HuggingFace](https://huggingface.co/), set [environment variable DIFFSYNTH_DOWNLOAD_SOURCE](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_download_source) to `huggingface`.
|
||||
To download models from [HuggingFace](https://huggingface.co/), set [environment variable DIFFSYNTH_DOWNLOAD_SOURCE](../Pipeline_Usage/Environment_Variables.md#diffsynth_download_source) to `huggingface`.
|
||||
|
||||
```shell
|
||||
import os
|
||||
@@ -102,4 +102,65 @@ image.save("image.jpg")
|
||||
|
||||
Each model `Pipeline` has different input parameters. Please refer to the documentation for each model.
|
||||
|
||||
If the model parameters are too large, causing insufficient VRAM, please enable [VRAM management](/docs/en/Pipeline_Usage/VRAM_management.md).
|
||||
If the model parameters are too large, causing insufficient VRAM, please enable [VRAM management](../Pipeline_Usage/VRAM_management.md).
|
||||
|
||||
## Loading LoRA
|
||||
|
||||
LoRA is a lightweight model training method that produces a small number of parameters to extend model capabilities. DiffSynth-Studio supports two ways to load LoRA: cold loading and hot loading.
|
||||
|
||||
* Cold loading: When the base model does not have [VRAM management](../Pipeline_Usage/VRAM_management.md) enabled, LoRA will be fused into the base model weights. In this case, inference speed remains unchanged, but LoRA cannot be unloaded after loading.
|
||||
|
||||
```python
|
||||
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
|
||||
import torch
|
||||
|
||||
pipe = QwenImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
|
||||
)
|
||||
lora = ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-LoRA-ArtAug-v1", origin_file_pattern="model.safetensors")
|
||||
pipe.load_lora(pipe.dit, lora, alpha=1)
|
||||
prompt = "Exquisite portrait, underwater girl, blue dress flowing, hair floating, translucent light, bubbles surrounding, peaceful face, intricate details, dreamy and ethereal."
|
||||
image = pipe(prompt, seed=0, num_inference_steps=40)
|
||||
image.save("image.jpg")
|
||||
```
|
||||
|
||||
* Hot loading: When the base model has [VRAM management](../Pipeline_Usage/VRAM_management.md) enabled, LoRA will not be fused into the base model weights. In this case, inference speed will be slower, but LoRA can be unloaded through `pipe.clear_lora()` after loading.
|
||||
|
||||
```python
|
||||
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
|
||||
import torch
|
||||
|
||||
vram_config = {
|
||||
"offload_dtype": torch.bfloat16,
|
||||
"offload_device": "cuda",
|
||||
"onload_dtype": torch.bfloat16,
|
||||
"onload_device": "cuda",
|
||||
"preparing_dtype": torch.bfloat16,
|
||||
"preparing_device": "cuda",
|
||||
"computation_dtype": torch.bfloat16,
|
||||
"computation_device": "cuda",
|
||||
}
|
||||
pipe = QwenImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
|
||||
)
|
||||
lora = ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-LoRA-ArtAug-v1", origin_file_pattern="model.safetensors")
|
||||
pipe.load_lora(pipe.dit, lora, alpha=1)
|
||||
prompt = "Exquisite portrait, underwater girl, blue dress flowing, hair floating, translucent light, bubbles surrounding, peaceful face, intricate details, dreamy and ethereal."
|
||||
image = pipe(prompt, seed=0, num_inference_steps=40)
|
||||
image.save("image.jpg")
|
||||
pipe.clear_lora()
|
||||
```
|
||||
|
||||
@@ -65,7 +65,7 @@ image_1.jpg,"a dog"
|
||||
image_2.jpg,"a cat"
|
||||
```
|
||||
|
||||
We have built sample datasets for your testing. To understand how the universal dataset architecture is implemented, please refer to [`diffsynth.core.data`](/docs/en/API_Reference/core/data.md).
|
||||
We have built sample datasets for your testing. To understand how the universal dataset architecture is implemented, please refer to [`diffsynth.core.data`](../API_Reference/core/data.md).
|
||||
|
||||
<details>
|
||||
|
||||
@@ -93,7 +93,7 @@ We have built sample datasets for your testing. To understand how the universal
|
||||
|
||||
## Loading Models
|
||||
|
||||
Similar to [model loading during inference](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models), we support multiple ways to configure model paths, and the two methods can be mixed.
|
||||
Similar to [model loading during inference](../Pipeline_Usage/Model_Inference.md#loading-models), we support multiple ways to configure model paths, and the two methods can be mixed.
|
||||
|
||||
<details>
|
||||
|
||||
@@ -115,9 +115,9 @@ Similar to [model loading during inference](/docs/en/Pipeline_Usage/Model_Infere
|
||||
> --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors"
|
||||
> ```
|
||||
>
|
||||
> Model files are downloaded to the `./models` path by default, which can be modified through [environment variable DIFFSYNTH_MODEL_BASE_PATH](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path).
|
||||
> Model files are downloaded to the `./models` path by default, which can be modified through [environment variable DIFFSYNTH_MODEL_BASE_PATH](../Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path).
|
||||
>
|
||||
> By default, even after models have been downloaded, the program will still query remotely for missing files. To completely disable remote requests, set [environment variable DIFFSYNTH_SKIP_DOWNLOAD](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) to `True`.
|
||||
> By default, even after models have been downloaded, the program will still query remotely for missing files. To completely disable remote requests, set [environment variable DIFFSYNTH_SKIP_DOWNLOAD](../Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) to `True`.
|
||||
|
||||
</details>
|
||||
|
||||
@@ -237,11 +237,11 @@ accelerate launch --config_file examples/qwen_image/model_training/full/accelera
|
||||
|
||||
## Training Considerations
|
||||
|
||||
* In addition to the `csv` format, dataset metadata also supports `json` and `jsonl` formats. For how to choose the best metadata format, please refer to [/docs/en/API_Reference/core/data.md#metadata](/docs/en/API_Reference/core/data.md#metadata)
|
||||
* In addition to the `csv` format, dataset metadata also supports `json` and `jsonl` formats. For how to choose the best metadata format, please refer to [../API_Reference/core/data.md#metadata](../API_Reference/core/data.md#metadata)
|
||||
* Training effectiveness is usually strongly correlated with training steps and weakly correlated with epoch count. Therefore, we recommend using the `--save_steps` parameter to save model files at training step intervals.
|
||||
* When data volume * `dataset_repeat` exceeds $10^9$, we observed that the dataset speed becomes significantly slower, which seems to be a `PyTorch` bug. We are not sure if newer versions of `PyTorch` have fixed this issue.
|
||||
* For learning rate `--learning_rate`, it is recommended to set to `1e-4` in LoRA training and `1e-5` in full training.
|
||||
* The training framework does not support batch size > 1. The reasons are complex. See [Q&A: Why doesn't the training framework support batch size > 1?](/docs/en/QA.md#why-doesnt-the-training-framework-support-batch-size--1)
|
||||
* The training framework does not support batch size > 1. The reasons are complex. See [Q&A: Why doesn't the training framework support batch size > 1?](../QA.md#why-doesnt-the-training-framework-support-batch-size--1)
|
||||
* Some models contain redundant parameters. For example, the text encoding part of the last layer of Qwen-Image's DiT part. When training these models, `--find_unused_parameters` needs to be set to avoid errors in multi-GPU training. For compatibility with community models, we do not intend to remove these redundant parameters.
|
||||
* The loss function value of Diffusion models has little relationship with actual effects. Therefore, we do not record loss function values during training. We recommend setting `--num_epochs` to a sufficiently large value, testing while training, and manually closing the training program after the effect converges.
|
||||
* `--use_gradient_checkpointing` is usually enabled unless GPU VRAM is sufficient; `--use_gradient_checkpointing_offload` is enabled as needed. See [`diffsynth.core.gradient`](/docs/en/API_Reference/core/gradient.md) for details.
|
||||
* `--use_gradient_checkpointing` is usually enabled unless GPU VRAM is sufficient; `--use_gradient_checkpointing_offload` is enabled as needed. See [`diffsynth.core.gradient`](../API_Reference/core/gradient.md) for details.
|
||||
@@ -41,7 +41,7 @@ pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm6
|
||||
# x86
|
||||
pip install -e .[npu] --extra-index-url "https://download.pytorch.org/whl/cpu"
|
||||
|
||||
When using Ascend NPU, please replace `"cuda"` with `"npu"` in your Python code. For details, see [NPU Support](/docs/en/Pipeline_Usage/GPU_support.md#ascend-npu).
|
||||
When using Ascend NPU, please replace `"cuda"` with `"npu"` in your Python code. For details, see [NPU Support](../Pipeline_Usage/GPU_support.md#ascend-npu).
|
||||
|
||||
## Other Installation Issues
|
||||
|
||||
|
||||
@@ -140,7 +140,7 @@ image.save("image.jpg")
|
||||
|
||||
In more extreme cases, when memory is also insufficient to store the entire model, the Disk Offload feature allows lazy loading of model parameters, meaning each Layer of the model only reads the corresponding parameters from disk when the forward function is called. When enabling this feature, we recommend using high-speed SSD drives.
|
||||
|
||||
Disk Offload is a very special VRAM management solution that only supports `.safetensors` format files, not `.bin`, `.pth`, `.ckpt`, or other binary files, and does not support [state dict converter](/docs/en/Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) with Tensor reshape.
|
||||
Disk Offload is a very special VRAM management solution that only supports `.safetensors` format files, not `.bin`, `.pth`, `.ckpt`, or other binary files, and does not support [state dict converter](../Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) with Tensor reshape.
|
||||
|
||||
```python
|
||||
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
|
||||
@@ -196,7 +196,7 @@ Specifically, the VRAM management module divides model Layers into the following
|
||||
* Preparing: Intermediate state between Onload and Computation. A temporary storage state when VRAM allows. This state is controlled by the VRAM management mechanism and enters this state if and only if [vram_limit is set to unlimited] or [vram_limit is set and there is spare VRAM]
|
||||
* Computation: The model is being computed. This state is controlled by the VRAM management mechanism and is temporarily entered only during `forward`
|
||||
|
||||
If you are a model developer and want to control the VRAM management granularity of a specific model, please refer to [../Developer_Guide/Enabling_VRAM_management.md](/docs/en/Developer_Guide/Enabling_VRAM_management.md).
|
||||
If you are a model developer and want to control the VRAM management granularity of a specific model, please refer to [../Developer_Guide/Enabling_VRAM_management.md](../Developer_Guide/Enabling_VRAM_management.md).
|
||||
|
||||
## Best Practices
|
||||
|
||||
|
||||
@@ -25,4 +25,11 @@ Even with suitable hardware conditions, we currently have no plans to support na
|
||||
* The main challenge of native FP8 precision training is precision overflow caused by gradient explosion. To ensure training stability, the model structure needs to be redesigned accordingly. However, no model developers are willing to do so at present.
|
||||
* Additionally, models trained with native FP8 precision can only be computed with BF16 precision during inference without Hopper architecture GPUs, theoretically resulting in generation quality inferior to FP8.
|
||||
|
||||
Therefore, native FP8 precision training technology is extremely immature. We will observe the technological developments in the open-source community.
|
||||
Therefore, native FP8 precision training technology is extremely immature. We will observe the technological developments in the open-source community.
|
||||
|
||||
## How to dynamically load LoRA models during inference?
|
||||
|
||||
We support two loading methods for LoRA models. See [LoRA Loading](./Pipeline_Usage/Model_Inference.md#loading-lora) for details:
|
||||
|
||||
* Cold Loading: When [VRAM Management](./Pipeline_Usage/VRAM_management.md) is not enabled for the base model, LoRA will be fused into the base model weights. In this case, inference speed remains unchanged, and LoRA cannot be unloaded after loading.
|
||||
* Hot Loading: When [VRAM Management](./Pipeline_Usage/VRAM_management.md) is enabled for the base model, LoRA will not be fused into the base model weights. In this case, inference speed will slow down, and LoRA can be unloaded after loading via `pipe.clear_lora()`.
|
||||
|
||||
@@ -26,58 +26,58 @@ graph LR;
|
||||
|
||||
This section introduces the basic usage of `DiffSynth-Studio`, including how to enable VRAM management for inference on GPUs with extremely low VRAM, and how to train various base models, LoRAs, ControlNets, and other models.
|
||||
|
||||
* [Installation Dependencies](/docs/en/Pipeline_Usage/Setup.md)
|
||||
* [Model Inference](/docs/en/Pipeline_Usage/Model_Inference.md)
|
||||
* [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md)
|
||||
* [Model Training](/docs/en/Pipeline_Usage/Model_Training.md)
|
||||
* [Environment Variables](/docs/en/Pipeline_Usage/Environment_Variables.md)
|
||||
* [GPU/NPU Support](/docs/en/Pipeline_Usage/GPU_support.md)
|
||||
* [Installation Dependencies](./Pipeline_Usage/Setup.md)
|
||||
* [Model Inference](./Pipeline_Usage/Model_Inference.md)
|
||||
* [VRAM Management](./Pipeline_Usage/VRAM_management.md)
|
||||
* [Model Training](./Pipeline_Usage/Model_Training.md)
|
||||
* [Environment Variables](./Pipeline_Usage/Environment_Variables.md)
|
||||
* [GPU/NPU Support](./Pipeline_Usage/GPU_support.md)
|
||||
|
||||
## Section 2: Model Details
|
||||
|
||||
This section introduces the Diffusion models supported by `DiffSynth-Studio`. Some model pipelines feature special functionalities such as controllable generation and parallel acceleration.
|
||||
|
||||
* [FLUX.1](/docs/en/Model_Details/FLUX.md)
|
||||
* [Wan](/docs/en/Model_Details/Wan.md)
|
||||
* [Qwen-Image](/docs/en/Model_Details/Qwen-Image.md)
|
||||
* [FLUX.2](/docs/en/Model_Details/FLUX2.md)
|
||||
* [Z-Image](/docs/en/Model_Details/Z-Image.md)
|
||||
* [FLUX.1](./Model_Details/FLUX.md)
|
||||
* [Wan](./Model_Details/Wan.md)
|
||||
* [Qwen-Image](./Model_Details/Qwen-Image.md)
|
||||
* [FLUX.2](./Model_Details/FLUX2.md)
|
||||
* [Z-Image](./Model_Details/Z-Image.md)
|
||||
|
||||
## Section 3: Training Framework
|
||||
|
||||
This section introduces the design philosophy of the training framework in `DiffSynth-Studio`, helping developers understand the principles of Diffusion model training algorithms.
|
||||
|
||||
* [Basic Principles of Diffusion Models](/docs/en/Training/Understanding_Diffusion_models.md)
|
||||
* [Standard Supervised Training](/docs/en/Training/Supervised_Fine_Tuning.md)
|
||||
* [Enabling FP8 Precision in Training](/docs/en/Training/FP8_Precision.md)
|
||||
* [End-to-End Distillation Accelerated Training](/docs/en/Training/Direct_Distill.md)
|
||||
* [Two-Stage Split Training](/docs/en/Training/Split_Training.md)
|
||||
* [Differential LoRA Training](/docs/en/Training/Differential_LoRA.md)
|
||||
* [Basic Principles of Diffusion Models](./Training/Understanding_Diffusion_models.md)
|
||||
* [Standard Supervised Training](./Training/Supervised_Fine_Tuning.md)
|
||||
* [Enabling FP8 Precision in Training](./Training/FP8_Precision.md)
|
||||
* [End-to-End Distillation Accelerated Training](./Training/Direct_Distill.md)
|
||||
* [Two-Stage Split Training](./Training/Split_Training.md)
|
||||
* [Differential LoRA Training](./Training/Differential_LoRA.md)
|
||||
|
||||
## Section 4: Model Integration
|
||||
|
||||
This section introduces how to integrate models into `DiffSynth-Studio` to utilize the framework's basic functions, helping developers provide support for new models in this project or perform inference and training of private models.
|
||||
|
||||
* [Integrating Model Architecture](/docs/en/Developer_Guide/Integrating_Your_Model.md)
|
||||
* [Building a Pipeline](/docs/en/Developer_Guide/Building_a_Pipeline.md)
|
||||
* [Enabling Fine-Grained VRAM Management](/docs/en/Developer_Guide/Enabling_VRAM_management.md)
|
||||
* [Model Training Integration](/docs/en/Developer_Guide/Training_Diffusion_Models.md)
|
||||
* [Integrating Model Architecture](./Developer_Guide/Integrating_Your_Model.md)
|
||||
* [Building a Pipeline](./Developer_Guide/Building_a_Pipeline.md)
|
||||
* [Enabling Fine-Grained VRAM Management](./Developer_Guide/Enabling_VRAM_management.md)
|
||||
* [Model Training Integration](./Developer_Guide/Training_Diffusion_Models.md)
|
||||
|
||||
## Section 5: API Reference
|
||||
|
||||
This section introduces the independent core module `diffsynth.core` in `DiffSynth-Studio`, explaining how internal functions are designed and operate. Developers can use these functional modules in other codebase developments if needed.
|
||||
|
||||
* [`diffsynth.core.attention`](/docs/en/API_Reference/core/attention.md): Attention mechanism implementation
|
||||
* [`diffsynth.core.data`](/docs/en/API_Reference/core/data.md): Data processing operators and general datasets
|
||||
* [`diffsynth.core.gradient`](/docs/en/API_Reference/core/gradient.md): Gradient checkpointing
|
||||
* [`diffsynth.core.loader`](/docs/en/API_Reference/core/loader.md): Model download and loading
|
||||
* [`diffsynth.core.vram`](/docs/en/API_Reference/core/vram.md): VRAM management
|
||||
* [`diffsynth.core.attention`](./API_Reference/core/attention.md): Attention mechanism implementation
|
||||
* [`diffsynth.core.data`](./API_Reference/core/data.md): Data processing operators and general datasets
|
||||
* [`diffsynth.core.gradient`](./API_Reference/core/gradient.md): Gradient checkpointing
|
||||
* [`diffsynth.core.loader`](./API_Reference/core/loader.md): Model download and loading
|
||||
* [`diffsynth.core.vram`](./API_Reference/core/vram.md): VRAM management
|
||||
|
||||
## Section 6: Academic Guide
|
||||
|
||||
This section introduces how to use `DiffSynth-Studio` to train new models, helping researchers explore new model technologies.
|
||||
|
||||
* Training models from scratch 【coming soon】
|
||||
* [Training models from scratch](./Research_Tutorial/train_from_scratch.md)
|
||||
* Inference improvement techniques 【coming soon】
|
||||
* Designing controllable generation models 【coming soon】
|
||||
* Creating new training paradigms 【coming soon】
|
||||
@@ -86,4 +86,4 @@ This section introduces how to use `DiffSynth-Studio` to train new models, helpi
|
||||
|
||||
This section summarizes common developer questions. If you encounter issues during usage or development, please refer to this section. If you still cannot resolve the problem, please submit an issue on GitHub.
|
||||
|
||||
* [Frequently Asked Questions](/docs/en/QA.md)
|
||||
* [Frequently Asked Questions](./QA.md)
|
||||
476
docs/en/Research_Tutorial/train_from_scratch.md
Normal file
476
docs/en/Research_Tutorial/train_from_scratch.md
Normal file
@@ -0,0 +1,476 @@
|
||||
# Training Models from Scratch
|
||||
|
||||
DiffSynth-Studio's training engine supports training foundation models from scratch. This article introduces how to train a small text-to-image model with only 0.1B parameters from scratch.
|
||||
|
||||
## 1. Building Model Architecture
|
||||
|
||||
### 1.1 Diffusion Model
|
||||
|
||||
From UNet [[1]](https://arxiv.org/abs/1505.04597) [[2]](https://arxiv.org/abs/2112.10752) to DiT [[3]](https://arxiv.org/abs/2212.09748) [[4]](https://arxiv.org/abs/2403.03206), the mainstream model architectures of Diffusion have undergone multiple evolutions. Typically, a Diffusion model's inputs include:
|
||||
|
||||
* Image tensor (`latents`): The encoding of images, generated by the VAE model, containing partial noise
|
||||
* Text tensor (`prompt_embeds`): The encoding of text, generated by the text encoder
|
||||
* Timestep (`timestep`): A scalar used to mark which stage of the Diffusion process we are currently at
|
||||
|
||||
The model's output is a tensor with the same shape as the image tensor, representing the denoising direction predicted by the model. For details about Diffusion model theory, please refer to [Basic Principles of Diffusion Models](../Training/Understanding_Diffusion_models.md). In this article, we build a DiT model with only 0.1B parameters: `AAADiT`.
|
||||
|
||||
<details>
|
||||
<summary>Model Architecture Code</summary>
|
||||
|
||||
```python
|
||||
import torch, accelerate
|
||||
from PIL import Image
|
||||
from typing import Union
|
||||
from tqdm import tqdm
|
||||
from einops import rearrange, repeat
|
||||
|
||||
from transformers import AutoProcessor, AutoTokenizer
|
||||
from diffsynth.core import ModelConfig, gradient_checkpoint_forward, attention_forward, UnifiedDataset, load_model
|
||||
from diffsynth.diffusion import FlowMatchScheduler, DiffusionTrainingModule, FlowMatchSFTLoss, ModelLogger, launch_training_task
|
||||
from diffsynth.diffusion.base_pipeline import BasePipeline, PipelineUnit
|
||||
from diffsynth.models.general_modules import TimestepEmbeddings
|
||||
from diffsynth.models.z_image_text_encoder import ZImageTextEncoder
|
||||
from diffsynth.models.flux2_vae import Flux2VAE
|
||||
|
||||
|
||||
class AAAPositionalEmbedding(torch.nn.Module):
|
||||
def __init__(self, height=16, width=16, dim=1024):
|
||||
super().__init__()
|
||||
self.image_emb = torch.nn.Parameter(torch.randn((1, dim, height, width)))
|
||||
self.text_emb = torch.nn.Parameter(torch.randn((dim,)))
|
||||
|
||||
def forward(self, image, text):
|
||||
height, width = image.shape[-2:]
|
||||
image_emb = self.image_emb.to(device=image.device, dtype=image.dtype)
|
||||
image_emb = torch.nn.functional.interpolate(image_emb, size=(height, width), mode="bilinear")
|
||||
image_emb = rearrange(image_emb, "B C H W -> B (H W) C")
|
||||
text_emb = self.text_emb.to(device=text.device, dtype=text.dtype)
|
||||
text_emb = repeat(text_emb, "C -> B L C", B=text.shape[0], L=text.shape[1])
|
||||
emb = torch.concat([image_emb, text_emb], dim=1)
|
||||
return emb
|
||||
|
||||
|
||||
class AAABlock(torch.nn.Module):
|
||||
def __init__(self, dim=1024, num_heads=32):
|
||||
super().__init__()
|
||||
self.norm_attn = torch.nn.RMSNorm(dim, elementwise_affine=False)
|
||||
self.to_q = torch.nn.Linear(dim, dim)
|
||||
self.to_k = torch.nn.Linear(dim, dim)
|
||||
self.to_v = torch.nn.Linear(dim, dim)
|
||||
self.to_out = torch.nn.Linear(dim, dim)
|
||||
self.norm_mlp = torch.nn.RMSNorm(dim, elementwise_affine=False)
|
||||
self.ff = torch.nn.Sequential(
|
||||
torch.nn.Linear(dim, dim*3),
|
||||
torch.nn.SiLU(),
|
||||
torch.nn.Linear(dim*3, dim),
|
||||
)
|
||||
self.to_gate = torch.nn.Linear(dim, dim * 2)
|
||||
self.num_heads = num_heads
|
||||
|
||||
def attention(self, emb, pos_emb):
|
||||
emb = self.norm_attn(emb + pos_emb)
|
||||
q, k, v = self.to_q(emb), self.to_k(emb), self.to_v(emb)
|
||||
emb = attention_forward(
|
||||
q, k, v,
|
||||
q_pattern="b s (n d)", k_pattern="b s (n d)", v_pattern="b s (n d)", out_pattern="b s (n d)",
|
||||
dims={"n": self.num_heads},
|
||||
)
|
||||
emb = self.to_out(emb)
|
||||
return emb
|
||||
|
||||
def feed_forward(self, emb, pos_emb):
|
||||
emb = self.norm_mlp(emb + pos_emb)
|
||||
emb = self.ff(emb)
|
||||
return emb
|
||||
|
||||
def forward(self, emb, pos_emb, t_emb):
|
||||
gate_attn, gate_mlp = self.to_gate(t_emb).chunk(2, dim=-1)
|
||||
emb = emb + self.attention(emb, pos_emb) * (1 + gate_attn)
|
||||
emb = emb + self.feed_forward(emb, pos_emb) * (1 + gate_mlp)
|
||||
return emb
|
||||
|
||||
|
||||
class AAADiT(torch.nn.Module):
|
||||
def __init__(self, dim=1024):
|
||||
super().__init__()
|
||||
self.pos_embedder = AAAPositionalEmbedding(dim=dim)
|
||||
self.timestep_embedder = TimestepEmbeddings(256, dim)
|
||||
self.image_embedder = torch.nn.Sequential(torch.nn.Linear(128, dim), torch.nn.LayerNorm(dim))
|
||||
self.text_embedder = torch.nn.Sequential(torch.nn.Linear(1024, dim), torch.nn.LayerNorm(dim))
|
||||
self.blocks = torch.nn.ModuleList([AAABlock(dim) for _ in range(10)])
|
||||
self.proj_out = torch.nn.Linear(dim, 128)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
latents,
|
||||
prompt_embeds,
|
||||
timestep,
|
||||
use_gradient_checkpointing=False,
|
||||
use_gradient_checkpointing_offload=False,
|
||||
):
|
||||
pos_emb = self.pos_embedder(latents, prompt_embeds)
|
||||
t_emb = self.timestep_embedder(timestep, dtype=latents.dtype).view(1, 1, -1)
|
||||
image = self.image_embedder(rearrange(latents, "B C H W -> B (H W) C"))
|
||||
text = self.text_embedder(prompt_embeds)
|
||||
emb = torch.concat([image, text], dim=1)
|
||||
for block_id, block in enumerate(self.blocks):
|
||||
emb = gradient_checkpoint_forward(
|
||||
block,
|
||||
use_gradient_checkpointing=use_gradient_checkpointing,
|
||||
use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
|
||||
emb=emb,
|
||||
pos_emb=pos_emb,
|
||||
t_emb=t_emb,
|
||||
)
|
||||
emb = emb[:, :latents.shape[-1] * latents.shape[-2]]
|
||||
emb = self.proj_out(emb)
|
||||
emb = rearrange(emb, "B (H W) C -> B C H W", W=latents.shape[-1])
|
||||
return emb
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
### 1.2 Encoder-Decoder Models
|
||||
|
||||
Besides the Diffusion model used for denoising, we also need two other models:
|
||||
|
||||
* Text Encoder: Used to encode text into tensors. We adopt the [Qwen/Qwen3-0.6B](https://modelscope.cn/models/Qwen/Qwen3-0.6B) model.
|
||||
* VAE Encoder-Decoder: The encoder part is used to encode images into tensors, and the decoder part is used to decode image tensors into images. We adopt the VAE model from [black-forest-labs/FLUX.2-klein-4B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B).
|
||||
|
||||
The architectures of these two models are already integrated in DiffSynth-Studio, located at [/diffsynth/models/z_image_text_encoder.py](https://github.com/modelscope/DiffSynth-Studio/blob/main/diffsynth/models/z_image_text_encoder.py) and [/diffsynth/models/flux2_vae.py](https://github.com/modelscope/DiffSynth-Studio/blob/main/diffsynth/models/flux2_vae.py), so we don't need to modify any code.
|
||||
|
||||
## 2. Building Pipeline
|
||||
|
||||
We introduced how to build a model Pipeline in the document [Integrating Pipeline](../Developer_Guide/Building_a_Pipeline.md). For the model in this article, we also need to build a Pipeline to connect the text encoder, Diffusion model, and VAE encoder-decoder.
|
||||
|
||||
<details>
|
||||
<summary>Pipeline Code</summary>
|
||||
|
||||
```python
|
||||
class AAAImagePipeline(BasePipeline):
|
||||
def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
|
||||
super().__init__(
|
||||
device=device, torch_dtype=torch_dtype,
|
||||
height_division_factor=16, width_division_factor=16,
|
||||
)
|
||||
self.scheduler = FlowMatchScheduler("FLUX.2")
|
||||
self.text_encoder: ZImageTextEncoder = None
|
||||
self.dit: AAADiT = None
|
||||
self.vae: Flux2VAE = None
|
||||
self.tokenizer: AutoProcessor = None
|
||||
self.in_iteration_models = ("dit",)
|
||||
self.units = [
|
||||
AAAUnit_PromptEmbedder(),
|
||||
AAAUnit_NoiseInitializer(),
|
||||
AAAUnit_InputImageEmbedder(),
|
||||
]
|
||||
self.model_fn = model_fn_aaa
|
||||
|
||||
@staticmethod
|
||||
def from_pretrained(
|
||||
torch_dtype: torch.dtype = torch.bfloat16,
|
||||
device: Union[str, torch.device] = "cuda",
|
||||
model_configs: list[ModelConfig] = [],
|
||||
tokenizer_config: ModelConfig = None,
|
||||
vram_limit: float = None,
|
||||
):
|
||||
# Initialize pipeline
|
||||
pipe = AAAImagePipeline(device=device, torch_dtype=torch_dtype)
|
||||
model_pool = pipe.download_and_load_models(model_configs, vram_limit)
|
||||
|
||||
# Fetch models
|
||||
pipe.text_encoder = model_pool.fetch_model("z_image_text_encoder")
|
||||
pipe.dit = model_pool.fetch_model("aaa_dit")
|
||||
pipe.vae = model_pool.fetch_model("flux2_vae")
|
||||
if tokenizer_config is not None:
|
||||
tokenizer_config.download_if_necessary()
|
||||
pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
|
||||
|
||||
# VRAM Management
|
||||
pipe.vram_management_enabled = pipe.check_vram_management_state()
|
||||
return pipe
|
||||
|
||||
@torch.no_grad()
|
||||
def __call__(
|
||||
self,
|
||||
# Prompt
|
||||
prompt: str,
|
||||
negative_prompt: str = "",
|
||||
cfg_scale: float = 1.0,
|
||||
# Image
|
||||
input_image: Image.Image = None,
|
||||
denoising_strength: float = 1.0,
|
||||
# Shape
|
||||
height: int = 1024,
|
||||
width: int = 1024,
|
||||
# Randomness
|
||||
seed: int = None,
|
||||
rand_device: str = "cpu",
|
||||
# Steps
|
||||
num_inference_steps: int = 30,
|
||||
# Progress bar
|
||||
progress_bar_cmd = tqdm,
|
||||
):
|
||||
self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, dynamic_shift_len=height//16*width//16)
|
||||
|
||||
# Parameters
|
||||
inputs_posi = {"prompt": prompt}
|
||||
inputs_nega = {"negative_prompt": negative_prompt}
|
||||
inputs_shared = {
|
||||
"cfg_scale": cfg_scale,
|
||||
"input_image": input_image, "denoising_strength": denoising_strength,
|
||||
"height": height, "width": width,
|
||||
"seed": seed, "rand_device": rand_device,
|
||||
"num_inference_steps": num_inference_steps,
|
||||
}
|
||||
for unit in self.units:
|
||||
inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
|
||||
|
||||
# Denoise
|
||||
self.load_models_to_device(self.in_iteration_models)
|
||||
models = {name: getattr(self, name) for name in self.in_iteration_models}
|
||||
for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
|
||||
timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
|
||||
noise_pred = self.cfg_guided_model_fn(
|
||||
self.model_fn, cfg_scale,
|
||||
inputs_shared, inputs_posi, inputs_nega,
|
||||
**models, timestep=timestep, progress_id=progress_id
|
||||
)
|
||||
inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
|
||||
|
||||
# Decode
|
||||
self.load_models_to_device(['vae'])
|
||||
image = self.vae.decode(inputs_shared["latents"])
|
||||
image = self.vae_output_to_image(image)
|
||||
self.load_models_to_device([])
|
||||
|
||||
return image
|
||||
|
||||
|
||||
class AAAUnit_PromptEmbedder(PipelineUnit):
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
seperate_cfg=True,
|
||||
input_params_posi={"prompt": "prompt"},
|
||||
input_params_nega={"prompt": "negative_prompt"},
|
||||
output_params=("prompt_embeds",),
|
||||
onload_model_names=("text_encoder",)
|
||||
)
|
||||
self.hidden_states_layers = (-1,)
|
||||
|
||||
def process(self, pipe: AAAImagePipeline, prompt):
|
||||
pipe.load_models_to_device(self.onload_model_names)
|
||||
text = pipe.tokenizer.apply_chat_template(
|
||||
[{"role": "user", "content": prompt}],
|
||||
tokenize=False,
|
||||
add_generation_prompt=True,
|
||||
enable_thinking=False,
|
||||
)
|
||||
inputs = pipe.tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128).to(pipe.device)
|
||||
output = pipe.text_encoder(**inputs, output_hidden_states=True, use_cache=False)
|
||||
prompt_embeds = torch.concat([output.hidden_states[k] for k in self.hidden_states_layers], dim=-1)
|
||||
return {"prompt_embeds": prompt_embeds}
|
||||
|
||||
|
||||
class AAAUnit_NoiseInitializer(PipelineUnit):
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
input_params=("height", "width", "seed", "rand_device"),
|
||||
output_params=("noise",),
|
||||
)
|
||||
|
||||
def process(self, pipe: AAAImagePipeline, height, width, seed, rand_device):
|
||||
noise = pipe.generate_noise((1, 128, height//16, width//16), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
|
||||
return {"noise": noise}
|
||||
|
||||
|
||||
class AAAUnit_InputImageEmbedder(PipelineUnit):
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
input_params=("input_image", "noise"),
|
||||
output_params=("latents", "input_latents"),
|
||||
onload_model_names=("vae",)
|
||||
)
|
||||
|
||||
def process(self, pipe: AAAImagePipeline, input_image, noise):
|
||||
if input_image is None:
|
||||
return {"latents": noise, "input_latents": None}
|
||||
pipe.load_models_to_device(['vae'])
|
||||
image = pipe.preprocess_image(input_image)
|
||||
input_latents = pipe.vae.encode(image)
|
||||
if pipe.scheduler.training:
|
||||
return {"latents": noise, "input_latents": input_latents}
|
||||
else:
|
||||
latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
|
||||
return {"latents": latents, "input_latents": input_latents}
|
||||
|
||||
|
||||
def model_fn_aaa(
|
||||
dit: AAADiT,
|
||||
latents=None,
|
||||
prompt_embeds=None,
|
||||
timestep=None,
|
||||
use_gradient_checkpointing=False,
|
||||
use_gradient_checkpointing_offload=False,
|
||||
**kwargs,
|
||||
):
|
||||
model_output = dit(
|
||||
latents,
|
||||
prompt_embeds,
|
||||
timestep,
|
||||
use_gradient_checkpointing=use_gradient_checkpointing,
|
||||
use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
|
||||
)
|
||||
return model_output
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## 3. Preparing Dataset
|
||||
|
||||
To quickly verify training effectiveness, we use the dataset [Pokemon-First Generation](https://modelscope.cn/datasets/DiffSynth-Studio/pokemon-gen1), which is reproduced from the open-source project [pokemon-dataset-zh](https://github.com/42arch/pokemon-dataset-zh), containing 151 first-generation Pokemon from Bulbasaur to Mew. If you want to use other datasets, please refer to the document [Preparing Datasets](../Pipeline_Usage/Model_Training.md#preparing-datasets) and [`diffsynth.core.data`](../API_Reference/core/data.md).
|
||||
|
||||
```shell
|
||||
modelscope download --dataset DiffSynth-Studio/pokemon-gen1 --local_dir ./data
|
||||
```
|
||||
|
||||
### 4. Start Training
|
||||
|
||||
The training process can be quickly implemented using Pipeline. We have placed the complete code at [../Research_Tutorial/train_from_scratch.py](https://github.com/modelscope/DiffSynth-Studio/blob/main/docs/en/Research_Tutorial/train_from_scratch.py), which can be directly started with `python docs/en/Research_Tutorial/train_from_scratch.py` for single GPU training.
|
||||
|
||||
To enable multi-GPU parallel training, please run `accelerate config` to set relevant parameters, then use the command `accelerate launch docs/en/Research_Tutorial/train_from_scratch.py` to start training.
|
||||
|
||||
This training script has no stopping condition, please manually close it when needed. The model converges after training approximately 60,000 steps, requiring 10-20 hours for single GPU training.
|
||||
|
||||
<details>
|
||||
<summary>Training Code</summary>
|
||||
|
||||
```python
|
||||
class AAATrainingModule(DiffusionTrainingModule):
|
||||
def __init__(self, device):
|
||||
super().__init__()
|
||||
self.pipe = AAAImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device=device,
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
|
||||
)
|
||||
self.pipe.dit = AAADiT().to(dtype=torch.bfloat16, device=device)
|
||||
self.pipe.freeze_except(["dit"])
|
||||
self.pipe.scheduler.set_timesteps(1000, training=True)
|
||||
|
||||
def forward(self, data):
|
||||
inputs_posi = {"prompt": data["prompt"]}
|
||||
inputs_nega = {"negative_prompt": ""}
|
||||
inputs_shared = {
|
||||
"input_image": data["image"],
|
||||
"height": data["image"].size[1],
|
||||
"width": data["image"].size[0],
|
||||
"cfg_scale": 1,
|
||||
"use_gradient_checkpointing": False,
|
||||
"use_gradient_checkpointing_offload": False,
|
||||
}
|
||||
for unit in self.pipe.units:
|
||||
inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega)
|
||||
loss = FlowMatchSFTLoss(self.pipe, **inputs_shared, **inputs_posi)
|
||||
return loss
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
accelerator = accelerate.Accelerator(gradient_accumulation_steps=1)
|
||||
dataset = UnifiedDataset(
|
||||
base_path="data/images",
|
||||
metadata_path="data/metadata_merged.csv",
|
||||
max_data_items=10000000,
|
||||
data_file_keys=("image",),
|
||||
main_data_operator=UnifiedDataset.default_image_operator(base_path="data/images", height=256, width=256)
|
||||
)
|
||||
model = AAATrainingModule(device=accelerator.device)
|
||||
model_logger = ModelLogger(
|
||||
"models/AAA/v1",
|
||||
remove_prefix_in_ckpt="pipe.dit.",
|
||||
)
|
||||
launch_training_task(
|
||||
accelerator, dataset, model, model_logger,
|
||||
learning_rate=2e-4,
|
||||
num_workers=4,
|
||||
save_steps=50000,
|
||||
num_epochs=999999,
|
||||
)
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## 5. Verifying Training Results
|
||||
|
||||
If you don't want to wait for the model training to complete, you can directly download [our pre-trained model](https://modelscope.cn/models/DiffSynth-Studio/AAAMyModel).
|
||||
|
||||
```shell
|
||||
modelscope download --model DiffSynth-Studio/AAAMyModel step-600000.safetensors --local_dir models/DiffSynth-Studio/AAAMyModel
|
||||
```
|
||||
|
||||
Loading the model
|
||||
|
||||
```python
|
||||
from diffsynth import load_model
|
||||
|
||||
pipe = AAAImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
|
||||
)
|
||||
pipe.dit = load_model(AAADiT, "models/DiffSynth-Studio/AAAMyModel/step-600000.safetensors", torch_dtype=torch.bfloat16, device="cuda")
|
||||
```
|
||||
|
||||
Model inference, generating the first-generation Pokemon "starter trio". At this point, the images generated by the model basically match the training data.
|
||||
|
||||
```python
|
||||
for seed, prompt in enumerate([
|
||||
"green, lizard, plant, Grass, Poison, seed on back, red eyes, smiling expression, short stout limbs, sharp claws",
|
||||
"orange, cream, lizard, Fire, flame on tail tip, large eyes, smiling expression, cream-colored belly patch, sharp claws",
|
||||
"blue, beige, brown, turtle, water type, shell, big eyes, short limbs, curled tail",
|
||||
]):
|
||||
image = pipe(
|
||||
prompt=prompt,
|
||||
negative_prompt=" ",
|
||||
num_inference_steps=30,
|
||||
cfg_scale=10,
|
||||
seed=seed,
|
||||
height=256, width=256,
|
||||
)
|
||||
image.save(f"image_{seed}.jpg")
|
||||
```
|
||||
|
||||
||||
|
||||
|-|-|-|
|
||||
|
||||
Model inference, generating Pokemon with "sharp claws". At this point, different random seeds can produce different image results.
|
||||
|
||||
```python
|
||||
for seed, prompt in enumerate([
|
||||
"sharp claws",
|
||||
"sharp claws",
|
||||
"sharp claws",
|
||||
]):
|
||||
image = pipe(
|
||||
prompt=prompt,
|
||||
negative_prompt=" ",
|
||||
num_inference_steps=30,
|
||||
cfg_scale=10,
|
||||
seed=seed+4,
|
||||
height=256, width=256,
|
||||
)
|
||||
image.save(f"image_sharp_claws_{seed}.jpg")
|
||||
```
|
||||
|
||||
||||
|
||||
|-|-|-|
|
||||
|
||||
Now, we have obtained a 0.1B small text-to-image model. This model can already generate 151 Pokemon, but cannot generate other image content. If you increase the amount of data, model parameters, and number of GPUs based on this, you can train a more powerful text-to-image model!
|
||||
341
docs/en/Research_Tutorial/train_from_scratch.py
Normal file
341
docs/en/Research_Tutorial/train_from_scratch.py
Normal file
@@ -0,0 +1,341 @@
|
||||
import torch, accelerate
|
||||
from PIL import Image
|
||||
from typing import Union
|
||||
from tqdm import tqdm
|
||||
from einops import rearrange, repeat
|
||||
|
||||
from transformers import AutoProcessor, AutoTokenizer
|
||||
from diffsynth.core import ModelConfig, gradient_checkpoint_forward, attention_forward, UnifiedDataset, load_model
|
||||
from diffsynth.diffusion import FlowMatchScheduler, DiffusionTrainingModule, FlowMatchSFTLoss, ModelLogger, launch_training_task
|
||||
from diffsynth.diffusion.base_pipeline import BasePipeline, PipelineUnit
|
||||
from diffsynth.models.general_modules import TimestepEmbeddings
|
||||
from diffsynth.models.z_image_text_encoder import ZImageTextEncoder
|
||||
from diffsynth.models.flux2_vae import Flux2VAE
|
||||
|
||||
|
||||
class AAAPositionalEmbedding(torch.nn.Module):
|
||||
def __init__(self, height=16, width=16, dim=1024):
|
||||
super().__init__()
|
||||
self.image_emb = torch.nn.Parameter(torch.randn((1, dim, height, width)))
|
||||
self.text_emb = torch.nn.Parameter(torch.randn((dim,)))
|
||||
|
||||
def forward(self, image, text):
|
||||
height, width = image.shape[-2:]
|
||||
image_emb = self.image_emb.to(device=image.device, dtype=image.dtype)
|
||||
image_emb = torch.nn.functional.interpolate(image_emb, size=(height, width), mode="bilinear")
|
||||
image_emb = rearrange(image_emb, "B C H W -> B (H W) C")
|
||||
text_emb = self.text_emb.to(device=text.device, dtype=text.dtype)
|
||||
text_emb = repeat(text_emb, "C -> B L C", B=text.shape[0], L=text.shape[1])
|
||||
emb = torch.concat([image_emb, text_emb], dim=1)
|
||||
return emb
|
||||
|
||||
|
||||
class AAABlock(torch.nn.Module):
|
||||
def __init__(self, dim=1024, num_heads=32):
|
||||
super().__init__()
|
||||
self.norm_attn = torch.nn.RMSNorm(dim, elementwise_affine=False)
|
||||
self.to_q = torch.nn.Linear(dim, dim)
|
||||
self.to_k = torch.nn.Linear(dim, dim)
|
||||
self.to_v = torch.nn.Linear(dim, dim)
|
||||
self.to_out = torch.nn.Linear(dim, dim)
|
||||
self.norm_mlp = torch.nn.RMSNorm(dim, elementwise_affine=False)
|
||||
self.ff = torch.nn.Sequential(
|
||||
torch.nn.Linear(dim, dim*3),
|
||||
torch.nn.SiLU(),
|
||||
torch.nn.Linear(dim*3, dim),
|
||||
)
|
||||
self.to_gate = torch.nn.Linear(dim, dim * 2)
|
||||
self.num_heads = num_heads
|
||||
|
||||
def attention(self, emb, pos_emb):
|
||||
emb = self.norm_attn(emb + pos_emb)
|
||||
q, k, v = self.to_q(emb), self.to_k(emb), self.to_v(emb)
|
||||
emb = attention_forward(
|
||||
q, k, v,
|
||||
q_pattern="b s (n d)", k_pattern="b s (n d)", v_pattern="b s (n d)", out_pattern="b s (n d)",
|
||||
dims={"n": self.num_heads},
|
||||
)
|
||||
emb = self.to_out(emb)
|
||||
return emb
|
||||
|
||||
def feed_forward(self, emb, pos_emb):
|
||||
emb = self.norm_mlp(emb + pos_emb)
|
||||
emb = self.ff(emb)
|
||||
return emb
|
||||
|
||||
def forward(self, emb, pos_emb, t_emb):
|
||||
gate_attn, gate_mlp = self.to_gate(t_emb).chunk(2, dim=-1)
|
||||
emb = emb + self.attention(emb, pos_emb) * (1 + gate_attn)
|
||||
emb = emb + self.feed_forward(emb, pos_emb) * (1 + gate_mlp)
|
||||
return emb
|
||||
|
||||
|
||||
class AAADiT(torch.nn.Module):
|
||||
def __init__(self, dim=1024):
|
||||
super().__init__()
|
||||
self.pos_embedder = AAAPositionalEmbedding(dim=dim)
|
||||
self.timestep_embedder = TimestepEmbeddings(256, dim)
|
||||
self.image_embedder = torch.nn.Sequential(torch.nn.Linear(128, dim), torch.nn.LayerNorm(dim))
|
||||
self.text_embedder = torch.nn.Sequential(torch.nn.Linear(1024, dim), torch.nn.LayerNorm(dim))
|
||||
self.blocks = torch.nn.ModuleList([AAABlock(dim) for _ in range(10)])
|
||||
self.proj_out = torch.nn.Linear(dim, 128)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
latents,
|
||||
prompt_embeds,
|
||||
timestep,
|
||||
use_gradient_checkpointing=False,
|
||||
use_gradient_checkpointing_offload=False,
|
||||
):
|
||||
pos_emb = self.pos_embedder(latents, prompt_embeds)
|
||||
t_emb = self.timestep_embedder(timestep, dtype=latents.dtype).view(1, 1, -1)
|
||||
image = self.image_embedder(rearrange(latents, "B C H W -> B (H W) C"))
|
||||
text = self.text_embedder(prompt_embeds)
|
||||
emb = torch.concat([image, text], dim=1)
|
||||
for block_id, block in enumerate(self.blocks):
|
||||
emb = gradient_checkpoint_forward(
|
||||
block,
|
||||
use_gradient_checkpointing=use_gradient_checkpointing,
|
||||
use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
|
||||
emb=emb,
|
||||
pos_emb=pos_emb,
|
||||
t_emb=t_emb,
|
||||
)
|
||||
emb = emb[:, :latents.shape[-1] * latents.shape[-2]]
|
||||
emb = self.proj_out(emb)
|
||||
emb = rearrange(emb, "B (H W) C -> B C H W", W=latents.shape[-1])
|
||||
return emb
|
||||
|
||||
|
||||
class AAAImagePipeline(BasePipeline):
|
||||
def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
|
||||
super().__init__(
|
||||
device=device, torch_dtype=torch_dtype,
|
||||
height_division_factor=16, width_division_factor=16,
|
||||
)
|
||||
self.scheduler = FlowMatchScheduler("FLUX.2")
|
||||
self.text_encoder: ZImageTextEncoder = None
|
||||
self.dit: AAADiT = None
|
||||
self.vae: Flux2VAE = None
|
||||
self.tokenizer: AutoProcessor = None
|
||||
self.in_iteration_models = ("dit",)
|
||||
self.units = [
|
||||
AAAUnit_PromptEmbedder(),
|
||||
AAAUnit_NoiseInitializer(),
|
||||
AAAUnit_InputImageEmbedder(),
|
||||
]
|
||||
self.model_fn = model_fn_aaa
|
||||
|
||||
@staticmethod
|
||||
def from_pretrained(
|
||||
torch_dtype: torch.dtype = torch.bfloat16,
|
||||
device: Union[str, torch.device] = "cuda",
|
||||
model_configs: list[ModelConfig] = [],
|
||||
tokenizer_config: ModelConfig = None,
|
||||
vram_limit: float = None,
|
||||
):
|
||||
# Initialize pipeline
|
||||
pipe = AAAImagePipeline(device=device, torch_dtype=torch_dtype)
|
||||
model_pool = pipe.download_and_load_models(model_configs, vram_limit)
|
||||
|
||||
# Fetch models
|
||||
pipe.text_encoder = model_pool.fetch_model("z_image_text_encoder")
|
||||
pipe.dit = model_pool.fetch_model("aaa_dit")
|
||||
pipe.vae = model_pool.fetch_model("flux2_vae")
|
||||
if tokenizer_config is not None:
|
||||
tokenizer_config.download_if_necessary()
|
||||
pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
|
||||
|
||||
# VRAM Management
|
||||
pipe.vram_management_enabled = pipe.check_vram_management_state()
|
||||
return pipe
|
||||
|
||||
@torch.no_grad()
|
||||
def __call__(
|
||||
self,
|
||||
# Prompt
|
||||
prompt: str,
|
||||
negative_prompt: str = "",
|
||||
cfg_scale: float = 1.0,
|
||||
# Image
|
||||
input_image: Image.Image = None,
|
||||
denoising_strength: float = 1.0,
|
||||
# Shape
|
||||
height: int = 1024,
|
||||
width: int = 1024,
|
||||
# Randomness
|
||||
seed: int = None,
|
||||
rand_device: str = "cpu",
|
||||
# Steps
|
||||
num_inference_steps: int = 30,
|
||||
# Progress bar
|
||||
progress_bar_cmd = tqdm,
|
||||
):
|
||||
self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, dynamic_shift_len=height//16*width//16)
|
||||
|
||||
# Parameters
|
||||
inputs_posi = {"prompt": prompt}
|
||||
inputs_nega = {"negative_prompt": negative_prompt}
|
||||
inputs_shared = {
|
||||
"cfg_scale": cfg_scale,
|
||||
"input_image": input_image, "denoising_strength": denoising_strength,
|
||||
"height": height, "width": width,
|
||||
"seed": seed, "rand_device": rand_device,
|
||||
"num_inference_steps": num_inference_steps,
|
||||
}
|
||||
for unit in self.units:
|
||||
inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
|
||||
|
||||
# Denoise
|
||||
self.load_models_to_device(self.in_iteration_models)
|
||||
models = {name: getattr(self, name) for name in self.in_iteration_models}
|
||||
for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
|
||||
timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
|
||||
noise_pred = self.cfg_guided_model_fn(
|
||||
self.model_fn, cfg_scale,
|
||||
inputs_shared, inputs_posi, inputs_nega,
|
||||
**models, timestep=timestep, progress_id=progress_id
|
||||
)
|
||||
inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
|
||||
|
||||
# Decode
|
||||
self.load_models_to_device(['vae'])
|
||||
image = self.vae.decode(inputs_shared["latents"])
|
||||
image = self.vae_output_to_image(image)
|
||||
self.load_models_to_device([])
|
||||
|
||||
return image
|
||||
|
||||
|
||||
class AAAUnit_PromptEmbedder(PipelineUnit):
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
seperate_cfg=True,
|
||||
input_params_posi={"prompt": "prompt"},
|
||||
input_params_nega={"prompt": "negative_prompt"},
|
||||
output_params=("prompt_embeds",),
|
||||
onload_model_names=("text_encoder",)
|
||||
)
|
||||
self.hidden_states_layers = (-1,)
|
||||
|
||||
def process(self, pipe: AAAImagePipeline, prompt):
|
||||
pipe.load_models_to_device(self.onload_model_names)
|
||||
text = pipe.tokenizer.apply_chat_template(
|
||||
[{"role": "user", "content": prompt}],
|
||||
tokenize=False,
|
||||
add_generation_prompt=True,
|
||||
enable_thinking=False,
|
||||
)
|
||||
inputs = pipe.tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128).to(pipe.device)
|
||||
output = pipe.text_encoder(**inputs, output_hidden_states=True, use_cache=False)
|
||||
prompt_embeds = torch.concat([output.hidden_states[k] for k in self.hidden_states_layers], dim=-1)
|
||||
return {"prompt_embeds": prompt_embeds}
|
||||
|
||||
|
||||
class AAAUnit_NoiseInitializer(PipelineUnit):
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
input_params=("height", "width", "seed", "rand_device"),
|
||||
output_params=("noise",),
|
||||
)
|
||||
|
||||
def process(self, pipe: AAAImagePipeline, height, width, seed, rand_device):
|
||||
noise = pipe.generate_noise((1, 128, height//16, width//16), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
|
||||
return {"noise": noise}
|
||||
|
||||
|
||||
class AAAUnit_InputImageEmbedder(PipelineUnit):
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
input_params=("input_image", "noise"),
|
||||
output_params=("latents", "input_latents"),
|
||||
onload_model_names=("vae",)
|
||||
)
|
||||
|
||||
def process(self, pipe: AAAImagePipeline, input_image, noise):
|
||||
if input_image is None:
|
||||
return {"latents": noise, "input_latents": None}
|
||||
pipe.load_models_to_device(['vae'])
|
||||
image = pipe.preprocess_image(input_image)
|
||||
input_latents = pipe.vae.encode(image)
|
||||
if pipe.scheduler.training:
|
||||
return {"latents": noise, "input_latents": input_latents}
|
||||
else:
|
||||
latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
|
||||
return {"latents": latents, "input_latents": input_latents}
|
||||
|
||||
|
||||
def model_fn_aaa(
|
||||
dit: AAADiT,
|
||||
latents=None,
|
||||
prompt_embeds=None,
|
||||
timestep=None,
|
||||
use_gradient_checkpointing=False,
|
||||
use_gradient_checkpointing_offload=False,
|
||||
**kwargs,
|
||||
):
|
||||
model_output = dit(
|
||||
latents,
|
||||
prompt_embeds,
|
||||
timestep,
|
||||
use_gradient_checkpointing=use_gradient_checkpointing,
|
||||
use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
|
||||
)
|
||||
return model_output
|
||||
|
||||
|
||||
class AAATrainingModule(DiffusionTrainingModule):
|
||||
def __init__(self, device):
|
||||
super().__init__()
|
||||
self.pipe = AAAImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device=device,
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
|
||||
)
|
||||
self.pipe.dit = AAADiT().to(dtype=torch.bfloat16, device=device)
|
||||
self.pipe.freeze_except(["dit"])
|
||||
self.pipe.scheduler.set_timesteps(1000, training=True)
|
||||
|
||||
def forward(self, data):
|
||||
inputs_posi = {"prompt": data["prompt"]}
|
||||
inputs_nega = {"negative_prompt": ""}
|
||||
inputs_shared = {
|
||||
"input_image": data["image"],
|
||||
"height": data["image"].size[1],
|
||||
"width": data["image"].size[0],
|
||||
"cfg_scale": 1,
|
||||
"use_gradient_checkpointing": False,
|
||||
"use_gradient_checkpointing_offload": False,
|
||||
}
|
||||
for unit in self.pipe.units:
|
||||
inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega)
|
||||
loss = FlowMatchSFTLoss(self.pipe, **inputs_shared, **inputs_posi)
|
||||
return loss
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
accelerator = accelerate.Accelerator(gradient_accumulation_steps=1)
|
||||
dataset = UnifiedDataset(
|
||||
base_path="data/images",
|
||||
metadata_path="data/metadata_merged.csv",
|
||||
max_data_items=10000000,
|
||||
data_file_keys=("image",),
|
||||
main_data_operator=UnifiedDataset.default_image_operator(base_path="data/images", height=256, width=256)
|
||||
)
|
||||
model = AAATrainingModule(device=accelerator.device)
|
||||
model_logger = ModelLogger(
|
||||
"models/AAA/v1",
|
||||
remove_prefix_in_ckpt="pipe.dit.",
|
||||
)
|
||||
launch_training_task(
|
||||
accelerator, dataset, model, model_logger,
|
||||
learning_rate=2e-4,
|
||||
num_workers=4,
|
||||
save_steps=50000,
|
||||
num_epochs=999999,
|
||||
)
|
||||
@@ -8,8 +8,8 @@ We were unable to identify the original proposer of differential LoRA training,
|
||||
|
||||
Assume we have two similar-content images: Image 1 and Image 2. For example, both images contain a car, but Image 1 has fewer details while Image 2 has more details. In differential LoRA training, we perform two-step training:
|
||||
|
||||
* Train LoRA 1 using Image 1 as training data with [standard supervised training](/docs/en/Training/Supervised_Fine_Tuning.md)
|
||||
* Train LoRA 2 using Image 2 as training data, after integrating LoRA 1 into the base model, with [standard supervised training](/docs/en/Training/Supervised_Fine_Tuning.md)
|
||||
* Train LoRA 1 using Image 1 as training data with [standard supervised training](../Training/Supervised_Fine_Tuning.md)
|
||||
* Train LoRA 2 using Image 2 as training data, after integrating LoRA 1 into the base model, with [standard supervised training](../Training/Supervised_Fine_Tuning.md)
|
||||
|
||||
In the first training step, since there is only one training image, the LoRA model easily overfits. Therefore, after training, LoRA 1 will cause the model to generate Image 1 without hesitation, regardless of the random seed. In the second training step, the LoRA model overfits again. Thus, after training, with the combined effect of LoRA 1 and LoRA 2, the model will generate Image 2 without hesitation. In short:
|
||||
|
||||
|
||||
@@ -44,7 +44,7 @@ Click on the model links to go to the model pages and view the model effects.
|
||||
|
||||
## Using Distillation Accelerated Training in the Training Framework
|
||||
|
||||
First, you need to generate training data. Please refer to the [Model Inference](/docs/en/Pipeline_Usage/Model_Inference.md) section to write inference code and generate training data with a sufficient number of inference steps.
|
||||
First, you need to generate training data. Please refer to the [Model Inference](../Pipeline_Usage/Model_Inference.md) section to write inference code and generate training data with a sufficient number of inference steps.
|
||||
|
||||
Taking Qwen-Image as an example, the following code can generate an image:
|
||||
|
||||
@@ -67,7 +67,7 @@ image = pipe(prompt, seed=0, num_inference_steps=40)
|
||||
image.save("image.jpg")
|
||||
```
|
||||
|
||||
Then, we compile the necessary information into [metadata files](/docs/en/API_Reference/core/data.md#metadata):
|
||||
Then, we compile the necessary information into [metadata files](../API_Reference/core/data.md#metadata):
|
||||
|
||||
```csv
|
||||
image,prompt,seed,rand_device,num_inference_steps,cfg_scale
|
||||
@@ -86,11 +86,11 @@ Then start LoRA distillation accelerated training:
|
||||
bash examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh
|
||||
```
|
||||
|
||||
Please note that in the [training script parameters](/docs/en/Pipeline_Usage/Model_Training.md#script-parameters), the image resolution setting for the dataset should avoid triggering scaling processing. When setting `--height` and `--width` to enable fixed resolution, all training data must be generated with exactly the same width and height. When setting `--max_pixels` to enable dynamic resolution, the value of `--max_pixels` must be greater than or equal to the pixel area of any training image.
|
||||
Please note that in the [training script parameters](../Pipeline_Usage/Model_Training.md#script-parameters), the image resolution setting for the dataset should avoid triggering scaling processing. When setting `--height` and `--width` to enable fixed resolution, all training data must be generated with exactly the same width and height. When setting `--max_pixels` to enable dynamic resolution, the value of `--max_pixels` must be greater than or equal to the pixel area of any training image.
|
||||
|
||||
## Framework Design Concept
|
||||
|
||||
Compared to [Standard Supervised Training](/docs/en/Training/Supervised_Fine_Tuning.md), Direct Distillation only differs in the training loss function. The loss function for Direct Distillation is `DirectDistillLoss` in `diffsynth.diffusion.loss`.
|
||||
Compared to [Standard Supervised Training](../Training/Supervised_Fine_Tuning.md), Direct Distillation only differs in the training loss function. The loss function for Direct Distillation is `DirectDistillLoss` in `diffsynth.diffusion.loss`.
|
||||
|
||||
## Future Work
|
||||
|
||||
|
||||
@@ -1,12 +1,12 @@
|
||||
# Enabling FP8 Precision in Training
|
||||
|
||||
Although `DiffSynth-Studio` supports [VRAM management](/docs/en/Pipeline_Usage/VRAM_management.md) in model inference, most of the techniques for reducing VRAM usage are not suitable for training. Offloading would cause extremely slow training processes.
|
||||
Although `DiffSynth-Studio` supports [VRAM management](../Pipeline_Usage/VRAM_management.md) in model inference, most of the techniques for reducing VRAM usage are not suitable for training. Offloading would cause extremely slow training processes.
|
||||
|
||||
FP8 precision is the only VRAM management strategy that can be enabled during training. However, this framework currently does not support native FP8 precision training. For reasons, see [Q&A: Why doesn't the training framework support native FP8 precision training?](/docs/en/QA.md#why-doesnt-the-training-framework-support-native-fp8-precision-training). It only supports storing models whose parameters are not updated by gradients (models that do not require gradient backpropagation, or whose gradients only update their LoRA) in FP8 precision.
|
||||
FP8 precision is the only VRAM management strategy that can be enabled during training. However, this framework currently does not support native FP8 precision training. For reasons, see [Q&A: Why doesn't the training framework support native FP8 precision training?](../QA.md#why-doesnt-the-training-framework-support-native-fp8-precision-training). It only supports storing models whose parameters are not updated by gradients (models that do not require gradient backpropagation, or whose gradients only update their LoRA) in FP8 precision.
|
||||
|
||||
## Enabling FP8
|
||||
|
||||
In our provided training scripts, you can quickly set models to be stored in FP8 precision through the `--fp8_models` parameter. Taking Qwen-Image LoRA training as an example, we provide a script for enabling FP8 training located at [`/examples/qwen_image/model_training/special/fp8_training/Qwen-Image-LoRA.sh`](/examples/qwen_image/model_training/special/fp8_training/Qwen-Image-LoRA.sh). After training is completed, you can verify the training results with the script [`/examples/qwen_image/model_training/special/fp8_training/validate.py`](/examples/qwen_image/model_training/special/fp8_training/validate.py).
|
||||
In our provided training scripts, you can quickly set models to be stored in FP8 precision through the `--fp8_models` parameter. Taking Qwen-Image LoRA training as an example, we provide a script for enabling FP8 training located at [`/examples/qwen_image/model_training/special/fp8_training/Qwen-Image-LoRA.sh`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/special/fp8_training/Qwen-Image-LoRA.sh). After training is completed, you can verify the training results with the script [`/examples/qwen_image/model_training/special/fp8_training/validate.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/special/fp8_training/validate.py).
|
||||
|
||||
Please note that this FP8 VRAM management strategy does not support gradient updates. When a model is set to be trainable, FP8 precision cannot be enabled for that model. Models that support FP8 include two types:
|
||||
|
||||
|
||||
@@ -8,7 +8,7 @@ This document introduces split training, which can automatically divide the trai
|
||||
|
||||
In the training process of most models, a large amount of computation occurs in "preprocessing," i.e., "computations unrelated to the denoising model," including VAE encoding, text encoding, etc. When the corresponding model parameters are fixed, the results of these computations are repetitive. For each data sample, the computational results are identical across multiple epochs. Therefore, we provide a "split training" feature that can automatically analyze and split the training process.
|
||||
|
||||
For standard supervised training of ordinary text-to-image models, the splitting process is straightforward. It only requires splitting the computation of all [`Pipeline Units`](/docs/en/Developer_Guide/Building_a_Pipeline.md#units) into the first stage, storing the computational results to disk, and then reading these results from disk in the second stage for subsequent computations. However, if gradient backpropagation is required during preprocessing, the situation becomes extremely complex. To address this, we introduced a computational graph splitting algorithm to analyze how to split the computation.
|
||||
For standard supervised training of ordinary text-to-image models, the splitting process is straightforward. It only requires splitting the computation of all [`Pipeline Units`](../Developer_Guide/Building_a_Pipeline.md#units) into the first stage, storing the computational results to disk, and then reading these results from disk in the second stage for subsequent computations. However, if gradient backpropagation is required during preprocessing, the situation becomes extremely complex. To address this, we introduced a computational graph splitting algorithm to analyze how to split the computation.
|
||||
|
||||
## Computational Graph Splitting Algorithm
|
||||
|
||||
@@ -16,7 +16,7 @@ For standard supervised training of ordinary text-to-image models, the splitting
|
||||
|
||||
## Using Split Training
|
||||
|
||||
Split training already supports [Standard Supervised Training](/docs/en/Training/Supervised_Fine_Tuning.md) and [Direct Distillation Training](/docs/en/Training/Direct_Distill.md). The `--task` parameter in the training command controls this. Taking LoRA training of the Qwen-Image model as an example, the pre-split training command is:
|
||||
Split training already supports [Standard Supervised Training](../Training/Supervised_Fine_Tuning.md) and [Direct Distillation Training](../Training/Direct_Distill.md). The `--task` parameter in the training command controls this. Taking LoRA training of the Qwen-Image model as an example, the pre-split training command is:
|
||||
|
||||
```shell
|
||||
accelerate launch examples/qwen_image/model_training/train.py \
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
# Standard Supervised Training
|
||||
|
||||
After understanding the [Basic Principles of Diffusion Models](/docs/en/Training/Understanding_Diffusion_models.md), this document introduces how the framework implements Diffusion model training. This document explains the framework's principles to help developers write new training code. If you want to use our provided default training functions, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md).
|
||||
After understanding the [Basic Principles of Diffusion Models](../Training/Understanding_Diffusion_models.md), this document introduces how the framework implements Diffusion model training. This document explains the framework's principles to help developers write new training code. If you want to use our provided default training functions, please refer to [Model Training](../Pipeline_Usage/Model_Training.md).
|
||||
|
||||
Recalling the model training pseudocode from earlier, when we actually write code, the situation becomes extremely complex. Some models require additional guidance conditions and preprocessing, such as ControlNet; some models require cross-computation with the denoising model, such as VACE; some models require Gradient Checkpointing due to excessive VRAM demands, such as Qwen-Image's DiT.
|
||||
|
||||
To achieve strict consistency between inference and training, we abstractly encapsulate components like `Pipeline`, reusing inference code extensively during training. Please refer to [Integrating Pipeline](/docs/en/Developer_Guide/Building_a_Pipeline.md) to understand the design of `Pipeline` components. Next, we'll introduce how the training framework utilizes `Pipeline` components to build training algorithms.
|
||||
To achieve strict consistency between inference and training, we abstractly encapsulate components like `Pipeline`, reusing inference code extensively during training. Please refer to [Integrating Pipeline](../Developer_Guide/Building_a_Pipeline.md) to understand the design of `Pipeline` components. Next, we'll introduce how the training framework utilizes `Pipeline` components to build training algorithms.
|
||||
|
||||
## Framework Design Concept
|
||||
|
||||
@@ -48,13 +48,13 @@ In `__init__`, model initialization is required. First load the model, then swit
|
||||
)
|
||||
```
|
||||
|
||||
The logic for loading models is basically consistent with inference, supporting loading models from remote and local paths. See [Model Inference](/docs/en/Pipeline_Usage/Model_Inference.md) for details, but please note not to enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md).
|
||||
The logic for loading models is basically consistent with inference, supporting loading models from remote and local paths. See [Model Inference](../Pipeline_Usage/Model_Inference.md) for details, but please note not to enable [VRAM Management](../Pipeline_Usage/VRAM_management.md).
|
||||
|
||||
`switch_pipe_to_training_mode` can switch the model to training mode. See `switch_pipe_to_training_mode` for details.
|
||||
|
||||
### `forward`
|
||||
|
||||
In `forward`, the loss function value needs to be calculated. First perform preprocessing, then compute the loss function through the `Pipeline`'s [`model_fn`](/docs/en/Developer_Guide/Building_a_Pipeline.md#model_fn).
|
||||
In `forward`, the loss function value needs to be calculated. First perform preprocessing, then compute the loss function through the `Pipeline`'s [`model_fn`](../Developer_Guide/Building_a_Pipeline.md#model_fn).
|
||||
|
||||
```python
|
||||
def forward(self, data):
|
||||
@@ -90,7 +90,7 @@ The loss function calculation reuses `FlowMatchSFTLoss` from `diffsynth.diffusio
|
||||
The training framework requires other modules, including:
|
||||
|
||||
* accelerator: Training launcher provided by `accelerate`, see [`accelerate`](https://huggingface.co/docs/accelerate/index) for details
|
||||
* dataset: Generic dataset, see [`diffsynth.core.data`](/docs/en/API_Reference/core/data.md) for details
|
||||
* dataset: Generic dataset, see [`diffsynth.core.data`](../API_Reference/core/data.md) for details
|
||||
* model_logger: Model logger, see `diffsynth.diffusion.logger` for details
|
||||
|
||||
```python
|
||||
|
||||
@@ -6,7 +6,7 @@ This document introduces the basic principles of Diffusion models to help you un
|
||||
|
||||
Diffusion models generate clear images or video content through iterative denoising. We start by explaining the generation process of a data sample $x_0$. Intuitively, in a complete round of denoising, we start from random Gaussian noise $x_T$ and iteratively obtain $x_{T-1}$, $x_{T-2}$, $x_{T-3}$, $\cdots$, gradually reducing the noise content at each step until we finally obtain the noise-free data sample $x_0$.
|
||||
|
||||
(Figure)
|
||||
|
||||
|
||||
This process is intuitive, but to understand the details, we need to answer several questions:
|
||||
|
||||
@@ -28,7 +28,7 @@ As for the intermediate values $\sigma_{T-1}$, $\sigma_{T-2}$, $\cdots$, $\sigma
|
||||
|
||||
At an intermediate step, we can directly synthesize noisy data samples $x_t=(1-\sigma_t)x_0+\sigma_t x_T$.
|
||||
|
||||
(Figure)
|
||||
|
||||
|
||||
## How is the iterative denoising computation performed?
|
||||
|
||||
@@ -40,8 +40,6 @@ Before understanding the iterative denoising computation, we need to clarify wha
|
||||
|
||||
Among these, the guidance condition $c$ is a newly introduced parameter that is input by the user. It can be text describing the image content or a sketch outlining the image structure.
|
||||
|
||||
(Figure)
|
||||
|
||||
The model's output $\hat \epsilon(x_t,c,t)$ approximately equals $x_T-x_0$, which is the direction of the entire diffusion process (the reverse process of denoising).
|
||||
|
||||
Next, we analyze the computation occurring in one iteration. At time step $t$, after the model computes an approximation of $x_T-x_0$, we calculate the next $x_{t-1}$:
|
||||
@@ -91,8 +89,6 @@ After understanding the iterative denoising process, we next consider how to tra
|
||||
|
||||
The training process differs from the generation process. If we retain multi-step iterations during training, the gradient would need to backpropagate through multiple steps, bringing catastrophic time and space complexity. To improve computational efficiency, we randomly select a time step $t$ for training.
|
||||
|
||||
(Figure)
|
||||
|
||||
The following is pseudocode for the training process:
|
||||
|
||||
> Obtain data sample $x_0$ and guidance condition $c$ from the dataset
|
||||
@@ -113,7 +109,7 @@ The following is pseudocode for the training process:
|
||||
|
||||
From theory to practice, more details need to be filled in. Modern Diffusion model architectures have matured, with mainstream architectures following the "three-stage" architecture proposed by Latent Diffusion, including data encoder-decoder, guidance condition encoder, and denoising model.
|
||||
|
||||
(Figure)
|
||||
|
||||
|
||||
### Data Encoder-Decoder
|
||||
|
||||
@@ -142,4 +138,4 @@ The denoising model is the true essence of Diffusion models, with diverse model
|
||||
|
||||
## How does this project encapsulate and implement model training?
|
||||
|
||||
Please read the next document: [Standard Supervised Training](/docs/en/Training/Supervised_Fine_Tuning.md)
|
||||
Please read the next document: [Standard Supervised Training](../Training/Supervised_Fine_Tuning.md)
|
||||
124
docs/en/conf.py
Normal file
124
docs/en/conf.py
Normal file
@@ -0,0 +1,124 @@
|
||||
# Configuration file for the Sphinx documentation builder.
|
||||
#
|
||||
# This file only contains a selection of the most common options. For a full
|
||||
# list see the documentation:
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html
|
||||
|
||||
# -- Path setup --------------------------------------------------------------
|
||||
|
||||
# If extensions (or modules to document with autodoc) are in another directory,
|
||||
# add these directories to sys.path here. If the directory is relative to the
|
||||
# documentation root, use os.path.abspath to make it absolute, like shown here.
|
||||
#
|
||||
import os
|
||||
import sys
|
||||
|
||||
# import sphinx_book_theme
|
||||
|
||||
sys.path.insert(0, os.path.abspath('../../'))
|
||||
# -- Project information -----------------------------------------------------
|
||||
|
||||
project = 'diffsynth'
|
||||
copyright = '2022-2025, Alibaba ModelScope'
|
||||
author = 'ModelScope Authors'
|
||||
version_file = '../../diffsynth/version.py'
|
||||
html_theme = 'sphinx_rtd_theme'
|
||||
language = 'en'
|
||||
|
||||
|
||||
def get_version():
|
||||
with open(version_file, 'r', encoding='utf-8') as f:
|
||||
exec(compile(f.read(), version_file, 'exec'))
|
||||
return locals()['__version__']
|
||||
|
||||
|
||||
# The full version, including alpha/beta/rc tags
|
||||
version = get_version()
|
||||
release = version
|
||||
|
||||
# -- General configuration ---------------------------------------------------
|
||||
|
||||
# Add any Sphinx extension module names here, as strings. They can be
|
||||
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
|
||||
# ones.
|
||||
extensions = [
|
||||
'sphinx.ext.napoleon',
|
||||
'sphinx.ext.autosummary',
|
||||
'sphinx.ext.autodoc',
|
||||
'sphinx.ext.viewcode',
|
||||
'sphinx_markdown_tables',
|
||||
'sphinx_copybutton',
|
||||
"sphinx_rtd_theme",
|
||||
'sphinx.ext.mathjax',
|
||||
'myst_parser',
|
||||
]
|
||||
# build the templated autosummary files
|
||||
autosummary_generate = True
|
||||
numpydoc_show_class_members = False
|
||||
|
||||
# Enable overriding of function signatures in the first line of the docstring.
|
||||
autodoc_docstring_signature = True
|
||||
|
||||
# Disable docstring inheritance
|
||||
autodoc_inherit_docstrings = False
|
||||
|
||||
# Show type hints in the description
|
||||
autodoc_typehints = 'description'
|
||||
|
||||
# Add parameter types if the parameter is documented in the docstring
|
||||
autodoc_typehints_description_target = 'documented_params'
|
||||
|
||||
autodoc_default_options = {
|
||||
'member-order': 'bysource',
|
||||
}
|
||||
|
||||
# Add any paths that contain templates here, relative to this directory.
|
||||
templates_path = ['_templates']
|
||||
|
||||
# The suffix(es) of source filenames.
|
||||
# You can specify multiple suffix as a list of string:
|
||||
#
|
||||
source_suffix = ['.rst', '.md']
|
||||
|
||||
# The master toctree document.
|
||||
root_doc = 'index'
|
||||
|
||||
# List of patterns, relative to source directory, that match files and
|
||||
# directories to ignore when looking for source files.
|
||||
# This pattern also affects html_static_path and html_extra_path.
|
||||
exclude_patterns = ['build']
|
||||
# A list of glob-style patterns [1] that are used to find source files.
|
||||
# They are matched against the source file names relative to the source directory,
|
||||
# using slashes as directory separators on all platforms.
|
||||
# The default is **, meaning that all files are recursively included from the source directory.
|
||||
# -- Options for HTML output -------------------------------------------------
|
||||
|
||||
# The theme to use for HTML and HTML Help pages. See the documentation for
|
||||
# a list of builtin themes.
|
||||
#
|
||||
# html_theme = 'sphinx_book_theme'
|
||||
# html_theme_path = [sphinx_book_theme.get_html_theme_path()]
|
||||
# html_theme_options = {}
|
||||
|
||||
# Add any paths that contain custom static files (such as style sheets) here,
|
||||
# relative to this directory. They are copied after the builtin static files,
|
||||
# so a file named "default.css" will overwrite the builtin "default.css".
|
||||
html_static_path = ['_static']
|
||||
# html_css_files = ['css/readthedocs.css']
|
||||
|
||||
# -- Options for HTMLHelp output ---------------------------------------------
|
||||
# Output file base name for HTML help builder.
|
||||
|
||||
# -- Extension configuration -------------------------------------------------
|
||||
# Ignore >>> when copying code
|
||||
copybutton_prompt_text = r'>>> |\.\.\. '
|
||||
copybutton_prompt_is_regexp = True
|
||||
|
||||
# Example configuration for intersphinx: refer to the Python standard library.
|
||||
intersphinx_mapping = {'https://docs.python.org/': None}
|
||||
|
||||
myst_enable_extensions = [
|
||||
'amsmath',
|
||||
'dollarmath',
|
||||
'colon_fence',
|
||||
]
|
||||
77
docs/en/index.rst
Normal file
77
docs/en/index.rst
Normal file
@@ -0,0 +1,77 @@
|
||||
Welcome to DiffSynth-Studio's Documentation
|
||||
==========================================
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Documentation Introduction
|
||||
|
||||
README
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Getting Started
|
||||
|
||||
Pipeline_Usage/Setup
|
||||
Pipeline_Usage/Model_Inference
|
||||
Pipeline_Usage/VRAM_management
|
||||
Pipeline_Usage/Model_Training
|
||||
Pipeline_Usage/Environment_Variables
|
||||
Pipeline_Usage/GPU_support
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Model Details
|
||||
|
||||
Model_Details/FLUX
|
||||
Model_Details/Wan
|
||||
Model_Details/Qwen-Image
|
||||
Model_Details/FLUX2
|
||||
Model_Details/Z-Image
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Training Framework
|
||||
|
||||
Training/Understanding_Diffusion_models
|
||||
Training/Supervised_Fine_Tuning
|
||||
Training/FP8_Precision
|
||||
Training/Direct_Distill
|
||||
Training/Split_Training
|
||||
Training/Differential_LoRA
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Model Integration
|
||||
|
||||
Developer_Guide/Integrating_Your_Model
|
||||
Developer_Guide/Building_a_Pipeline
|
||||
Developer_Guide/Enabling_VRAM_management
|
||||
Developer_Guide/Training_Diffusion_Models
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: API Reference
|
||||
|
||||
API_Reference/core/attention
|
||||
API_Reference/core/data
|
||||
API_Reference/core/gradient
|
||||
API_Reference/core/loader
|
||||
API_Reference/core/vram
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Research Guide
|
||||
|
||||
Research_Tutorial/train_from_scratch
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: FAQ
|
||||
|
||||
QA
|
||||
|
||||
Indices and tables
|
||||
==================
|
||||
* :ref:`genindex`
|
||||
* :ref:`modindex`
|
||||
* :ref:`search`
|
||||
Reference in New Issue
Block a user