mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-19 06:48:12 +00:00
86 lines
3.3 KiB
Markdown
86 lines
3.3 KiB
Markdown
# Quick Start
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In this document, we introduce how to quickly get started with DiffSynth-Studio for creation through a piece of code.
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## Installation
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Use the following command to clone and install DiffSynth-Studio from GitHub. For more information, please refer to [Installation](./Installation.md).
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```shell
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git clone https://github.com/modelscope/DiffSynth-Studio.git
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cd DiffSynth-Studio
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pip install -e .
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```
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## One-click Run!
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By running the following code, we will download the model, load the model, and generate an image.
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```python
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import torch
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from diffsynth import ModelManager, FluxImagePipeline
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model_manager = ModelManager(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_id_list=["FLUX.1-dev"]
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)
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pipe = FluxImagePipeline.from_model_manager(model_manager)
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torch.manual_seed(0)
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image = pipe(
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prompt="In a forest, a wooden plank sign reading DiffSynth",
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height=576, width=1024,
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)
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image.save("image.jpg")
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```
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From this example, we can see that there are two key modules in DiffSynth: `ModelManager` and `Pipeline`. We will introduce them in detail next.
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## Downloading and Loading Models
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`ModelManager` is responsible for downloading and loading models, which can be done in one step with the following code.
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```python
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import torch
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from diffsynth import ModelManager
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model_manager = ModelManager(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_id_list=["FLUX.1-dev"]
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)
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```
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Of course, we also support completing this step by step, and the following code is equivalent to the above.
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```python
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import torch
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from diffsynth import download_models, ModelManager
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download_models(["FLUX.1-dev"])
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model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda")
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model_manager.load_models([
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"models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
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"models/FLUX/FLUX.1-dev/text_encoder_2",
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"models/FLUX/FLUX.1-dev/ae.safetensors",
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"models/FLUX/FLUX.1-dev/flux1-dev.safetensors"
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])
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```
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When downloading models, we support downloading from [ModelScope](https://www.modelscope.cn/) and [HuggingFace](https://huggingface.co/), and we also support downloading non-preset models. For more information about model downloading, please refer to [Model Download](./DownloadModels.md).
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When loading models, you can put all the model paths you want to load into it. For model weight files in formats such as `.safetensors`, `ModelManager` will automatically determine the model type after loading; for folder format models, `ModelManager` will try to parse the `config.json` file within and try to call the corresponding module in third-party libraries such as `transformers`. For models supported by DiffSynth-Studio, please refer to [Supported Models](./Models.md).
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## Building Pipeline
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DiffSynth-Studio provides multiple inference `Pipeline`s, which can be directly obtained through `ModelManager` to get the required models and initialize. For example, the text-to-image `Pipeline` for the FLUX.1-dev model can be constructed as follows:
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```python
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pipe = FluxImagePipeline.from_model_manager(model_manager)
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```
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For more `Pipeline`s used for image generation and video generation, see [Inference Pipelines](./Pipelines.md).
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