mirror of
https://github.com/modelscope/DiffSynth-Studio.git
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Merge branch 'main' into ltx-2
This commit is contained in:
Zhongjie Duan
GitHub
@@ -85,6 +85,7 @@ graph LR;
|
||||
|[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)|
|
||||
|[lightx2v/Qwen-Image-Edit-2511-Lightning](https://modelscope.cn/models/lightx2v/Qwen-Image-Edit-2511-Lightning)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-Lightning.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511-Lightning.py)|-|-|-|-|
|
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|[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)|
|
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|[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)|
|
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|[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)|
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@@ -52,7 +52,12 @@ image.save("image.jpg")
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|模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
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|-|-|-|-|-|-|-|
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|[Tongyi-MAI/Z-Image](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image)|[code](/examples/z_image/model_inference/Z-Image.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image.py)|[code](/examples/z_image/model_training/full/Z-Image.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image.py)|[code](/examples/z_image/model_training/lora/Z-Image.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image.py)|
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|[DiffSynth-Studio/Z-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L)|[code](/examples/z_image/model_inference/Z-Image-i2L.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-i2L.py)|-|-|-|-|
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|[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)|
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|[PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|
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|[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](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|
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|[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](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|
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特殊训练脚本：
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@@ -75,6 +80,9 @@ image.save("image.jpg")
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* `seed`: 随机种子。默认为 `None`，即完全随机。
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* `rand_device`: 生成随机高斯噪声矩阵的计算设备，默认为 `"cpu"`。当设置为 `cuda` 时，在不同 GPU 上会导致不同的生成结果。
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* `num_inference_steps`: 推理次数，默认值为 8。
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* `controlnet_inputs`: ControlNet 模型的输入。
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* `edit_image`: 编辑模型的待编辑图像，支持多张图像。
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* `positive_only_lora`: 仅在正向提示词中使用的 LoRA 权重。
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如果显存不足，请开启[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文"模型总览"中的表格。
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@@ -77,7 +77,7 @@ graph LR;
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本节介绍如何利用 `DiffSynth-Studio` 训练新的模型，帮助科研工作者探索新的模型技术。
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* 从零开始训练模型【coming soon】
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* [从零开始训练模型](/docs/zh/Research_Tutorial/train_from_scratch.md)
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* 推理改进优化技术【coming soon】
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* 设计可控生成模型【coming soon】
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* 创建新的训练范式【coming soon】
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477
docs/zh/Research_Tutorial/train_from_scratch.md
Normal file
477
docs/zh/Research_Tutorial/train_from_scratch.md
Normal file
@@ -0,0 +1,477 @@
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# 从零开始训练模型
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DiffSynth-Studio 的训练引擎支持从零开始训练基础模型，本文介绍如何从零开始训练一个参数量仅为 0.1B 的小型文生图模型。
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## 1. 构建模型结构
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### 1.1 Diffusion 模型
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从 UNet [[1]](https://arxiv.org/abs/1505.04597) [[2]](https://arxiv.org/abs/2112.10752) 到 DiT [[3]](https://arxiv.org/abs/2212.09748) [[4]](https://arxiv.org/abs/2403.03206)，Diffusion 的主流模型结构经历了多次演变。通常，一个 Diffusion 模型的输入包括：
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* 图像张量（`latents`）：图像的编码，由 VAE 模型产生，含有部分噪声
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* 文本张量（`prompt_embeds`）：文本的编码，由文本编码器产生
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* 时间步（`timestep`）：标量，用于标记当前处于 Diffusion 过程的哪个阶段
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模型的输出是与图像张量形状相同的张量，表示模型预测的去噪方向，关于 Diffusion 模型理论的细节，请参考 [Diffusion 模型基本原理](/docs/zh/Training/Understanding_Diffusion_models.md)。在本文中，我们构建一个仅含 0.1B 参数的 DiT 模型：`AAADiT`。
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<details>
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<summary>模型结构代码</summary>
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```python
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import torch, accelerate
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from PIL import Image
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from typing import Union
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from tqdm import tqdm
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from einops import rearrange, repeat
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from transformers import AutoProcessor, AutoTokenizer
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from diffsynth.core import ModelConfig, gradient_checkpoint_forward, attention_forward, UnifiedDataset, load_model
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from diffsynth.diffusion import FlowMatchScheduler, DiffusionTrainingModule, FlowMatchSFTLoss, ModelLogger, launch_training_task
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from diffsynth.diffusion.base_pipeline import BasePipeline, PipelineUnit
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from diffsynth.models.general_modules import TimestepEmbeddings
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from diffsynth.models.z_image_text_encoder import ZImageTextEncoder
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from diffsynth.models.flux2_vae import Flux2VAE
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class AAAPositionalEmbedding(torch.nn.Module):
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def __init__(self, height=16, width=16, dim=1024):
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super().__init__()
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self.image_emb = torch.nn.Parameter(torch.randn((1, dim, height, width)))
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self.text_emb = torch.nn.Parameter(torch.randn((dim,)))
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def forward(self, image, text):
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height, width = image.shape[-2:]
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image_emb = self.image_emb.to(device=image.device, dtype=image.dtype)
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image_emb = torch.nn.functional.interpolate(image_emb, size=(height, width), mode="bilinear")
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image_emb = rearrange(image_emb, "B C H W -> B (H W) C")
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text_emb = self.text_emb.to(device=text.device, dtype=text.dtype)
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text_emb = repeat(text_emb, "C -> B L C", B=text.shape[0], L=text.shape[1])
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emb = torch.concat([image_emb, text_emb], dim=1)
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return emb
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class AAABlock(torch.nn.Module):
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def __init__(self, dim=1024, num_heads=32):
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super().__init__()
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self.norm_attn = torch.nn.RMSNorm(dim, elementwise_affine=False)
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self.to_q = torch.nn.Linear(dim, dim)
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self.to_k = torch.nn.Linear(dim, dim)
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self.to_v = torch.nn.Linear(dim, dim)
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self.to_out = torch.nn.Linear(dim, dim)
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self.norm_mlp = torch.nn.RMSNorm(dim, elementwise_affine=False)
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self.ff = torch.nn.Sequential(
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torch.nn.Linear(dim, dim*3),
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torch.nn.SiLU(),
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torch.nn.Linear(dim*3, dim),
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)
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self.to_gate = torch.nn.Linear(dim, dim * 2)
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self.num_heads = num_heads
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def attention(self, emb, pos_emb):
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emb = self.norm_attn(emb + pos_emb)
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q, k, v = self.to_q(emb), self.to_k(emb), self.to_v(emb)
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emb = attention_forward(
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q, k, v,
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q_pattern="b s (n d)", k_pattern="b s (n d)", v_pattern="b s (n d)", out_pattern="b s (n d)",
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dims={"n": self.num_heads},
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)
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emb = self.to_out(emb)
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return emb
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def feed_forward(self, emb, pos_emb):
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emb = self.norm_mlp(emb + pos_emb)
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emb = self.ff(emb)
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return emb
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def forward(self, emb, pos_emb, t_emb):
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gate_attn, gate_mlp = self.to_gate(t_emb).chunk(2, dim=-1)
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emb = emb + self.attention(emb, pos_emb) * (1 + gate_attn)
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emb = emb + self.feed_forward(emb, pos_emb) * (1 + gate_mlp)
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return emb
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class AAADiT(torch.nn.Module):
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def __init__(self, dim=1024):
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super().__init__()
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self.pos_embedder = AAAPositionalEmbedding(dim=dim)
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self.timestep_embedder = TimestepEmbeddings(256, dim)
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self.image_embedder = torch.nn.Sequential(torch.nn.Linear(128, dim), torch.nn.LayerNorm(dim))
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self.text_embedder = torch.nn.Sequential(torch.nn.Linear(1024, dim), torch.nn.LayerNorm(dim))
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self.blocks = torch.nn.ModuleList([AAABlock(dim) for _ in range(10)])
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self.proj_out = torch.nn.Linear(dim, 128)
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def forward(
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self,
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latents,
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prompt_embeds,
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timestep,
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use_gradient_checkpointing=False,
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use_gradient_checkpointing_offload=False,
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):
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pos_emb = self.pos_embedder(latents, prompt_embeds)
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t_emb = self.timestep_embedder(timestep, dtype=latents.dtype).view(1, 1, -1)
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image = self.image_embedder(rearrange(latents, "B C H W -> B (H W) C"))
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text = self.text_embedder(prompt_embeds)
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emb = torch.concat([image, text], dim=1)
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for block_id, block in enumerate(self.blocks):
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emb = gradient_checkpoint_forward(
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block,
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use_gradient_checkpointing=use_gradient_checkpointing,
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use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
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emb=emb,
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pos_emb=pos_emb,
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t_emb=t_emb,
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)
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emb = emb[:, :latents.shape[-1] * latents.shape[-2]]
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emb = self.proj_out(emb)
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emb = rearrange(emb, "B (H W) C -> B C H W", W=latents.shape[-1])
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return emb
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```
|
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</details>
|
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### 1.2 编解码器模型
|
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|
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除了用于去噪的 Diffusion 模型以外，我们还需要另外两个模型：
|
||||
|
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* 文本编码器：用于将文本编码为张量。我们采用 [Qwen/Qwen3-0.6B](https://modelscope.cn/models/Qwen/Qwen3-0.6B) 模型。
|
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* VAE 编解码器：编码器部分用于将图像编码为张量，解码器部分用于将图像张量解码为图像。我们采用 [black-forest-labs/FLUX.2-klein-4B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B) 中的 VAE 模型。
|
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|
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这两个模型的结构都已集成在 DiffSynth-Studio 中，分别位于 [/diffsynth/models/z_image_text_encoder.py](/diffsynth/models/z_image_text_encoder.py) 和 [/diffsynth/models/flux2_vae.py](/diffsynth/models/flux2_vae.py)，因此我们不需要修改任何代码。
|
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|
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## 2. 构建 Pipeline
|
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|
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我们在文档 [接入 Pipeline](/docs/zh/Developer_Guide/Building_a_Pipeline.md) 中介绍了如何构建一个模型 Pipeline，对于本文中的模型，我们也需要构建一个 Pipeline，连接文本编码器、Diffusion 模型、VAE 编解码器。
|
||||
|
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<details>
|
||||
<summary>Pipeline 代码</summary>
|
||||
|
||||
```python
|
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class AAAImagePipeline(BasePipeline):
|
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def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
|
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super().__init__(
|
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device=device, torch_dtype=torch_dtype,
|
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height_division_factor=16, width_division_factor=16,
|
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)
|
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self.scheduler = FlowMatchScheduler("FLUX.2")
|
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self.text_encoder: ZImageTextEncoder = None
|
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self.dit: AAADiT = None
|
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self.vae: Flux2VAE = None
|
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self.tokenizer: AutoProcessor = None
|
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self.in_iteration_models = ("dit",)
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self.units = [
|
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AAAUnit_PromptEmbedder(),
|
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AAAUnit_NoiseInitializer(),
|
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AAAUnit_InputImageEmbedder(),
|
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]
|
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self.model_fn = model_fn_aaa
|
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|
||||
@staticmethod
|
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def from_pretrained(
|
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torch_dtype: torch.dtype = torch.bfloat16,
|
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device: Union[str, torch.device] = "cuda",
|
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model_configs: list[ModelConfig] = [],
|
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tokenizer_config: ModelConfig = None,
|
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vram_limit: float = None,
|
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):
|
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# Initialize pipeline
|
||||
pipe = AAAImagePipeline(device=device, torch_dtype=torch_dtype)
|
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model_pool = pipe.download_and_load_models(model_configs, vram_limit)
|
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|
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# 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")
|
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if tokenizer_config is not None:
|
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tokenizer_config.download_if_necessary()
|
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pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
|
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|
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# 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. 准备数据集
|
||||
|
||||
为了快速验证训练效果，我们使用数据集 [宝可梦-第一世代](https://modelscope.cn/datasets/DiffSynth-Studio/pokemon-gen1)，这个数据集转载自开源项目 [pokemon-dataset-zh](https://github.com/42arch/pokemon-dataset-zh)，包含从妙蛙种子到梦幻的 151 个第一世代宝可梦。如果你想使用其他数据集，请参考文档 [准备数据集](/docs/zh/Pipeline_Usage/Model_Training.md#准备数据集) 和 [`diffsynth.core.data`](/docs/zh/API_Reference/core/data.md)。
|
||||
|
||||
```shell
|
||||
modelscope download --dataset DiffSynth-Studio/pokemon-gen1 --local_dir ./data
|
||||
```
|
||||
|
||||
### 4. 开始训练
|
||||
|
||||
训练过程可使用 Pipeline 快速实现，我们已将完整的代码放在 [/docs/zh/Research_Tutorial/train_from_scratch.py](/docs/zh/Research_Tutorial/train_from_scratch.py)，可直接通过 `python docs/zh/Research_Tutorial/train_from_scratch.py` 开始单 GPU 训练。
|
||||
|
||||
如需开启多 GPU 并行训练，请运行 `accelerate config` 设置相关参数，然后使用命令 `accelerate launch docs/zh/Research_Tutorial/train_from_scratch.py` 开始训练。
|
||||
|
||||
这个训练脚本没有设置停止条件，请在需要时手动关闭。模型在训练大约 6 万步后收敛，单 GPU 训练需要 10～20 小时。
|
||||
|
||||
|
||||
<details>
|
||||
<summary>训练代码</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. 验证训练效果
|
||||
|
||||
如果你不想等待模型训练完成，可以直接下载[我们预先训练好的模型](https://modelscope.cn/models/DiffSynth-Studio/AAAMyModel)。
|
||||
|
||||
```shell
|
||||
modelscope download --model DiffSynth-Studio/AAAMyModel step-600000.safetensors --local_dir models/DiffSynth-Studio/AAAMyModel
|
||||
```
|
||||
|
||||
加载模型
|
||||
|
||||
```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")
|
||||
```
|
||||
|
||||
模型推理，生成第一世代宝可梦“御三家”，此时模型生成的图像内容与训练数据基本一致。
|
||||
|
||||
```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",
|
||||
"蓝色，米色，棕色，乌龟，水系，龟壳，大眼睛，短四肢，卷曲尾巴",
|
||||
]):
|
||||
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")
|
||||
```
|
||||
|
||||
||||
|
||||
|-|-|-|
|
||||
|
||||
模型推理，生成具有“锐利爪子”的宝可梦，此时不同的随机种子能够产生不同的图像结果。
|
||||
|
||||
```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")
|
||||
```
|
||||
|
||||
||||
|
||||
|-|-|-|
|
||||
|
||||
现在，我们获得了一个 0.1B 的小型文生图模型，这个模型已经能够生成 151 个宝可梦，但无法生成其他图像内容。如果在此基础上增加数据量、模型参数量、GPU 数量，你就可以训练出一个更强大的文生图模型！
|
||||
341
docs/zh/Research_Tutorial/train_from_scratch.py
Normal file
341
docs/zh/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,
|
||||
)
|
||||
@@ -6,7 +6,7 @@
|
||||
|
||||
Diffusion 模型通过多步迭代式地去噪（denoise）生成清晰的图像或视频内容，我们从一个数据样本 $x_0$ 的生成过程开始讲起。直观地，在完整的一轮 denoise 过程中，我们从随机高斯噪声 $x_T$ 开始，通过迭代依次得到 $x_{T-1}$、$x_{T-2}$、$x_{T-3}$、$\cdots$，在每一步中逐渐减少噪声含量，最终得到不含噪声的数据样本 $x_0$。
|
||||
|
||||
（图）
|
||||
|
||||
|
||||
这个过程是很直观的，但如果要理解其中的细节，我们就需要回答这几个问题：
|
||||
|
||||
@@ -28,7 +28,7 @@ Diffusion 模型通过多步迭代式地去噪（denoise）生成清晰的图像
|
||||
|
||||
那么在中间的某一步，我们可以直接合成含噪声的数据样本 $x_t=(1-\sigma_t)x_0+\sigma_t x_T$。
|
||||
|
||||
（图）
|
||||
|
||||
|
||||
## 迭代去噪的计算是如何进行的？
|
||||
|
||||
@@ -40,8 +40,6 @@ Diffusion 模型通过多步迭代式地去噪（denoise）生成清晰的图像
|
||||
|
||||
其中，引导条件 $c$ 是新引入的参数，它是由用户输入的，可以是用于描述图像内容的文本，也可以是用于勾勒图像结构的线稿图。
|
||||
|
||||
（图）
|
||||
|
||||
而模型的输出 $\hat \epsilon(x_t,c,t)$，则近似地等于 $x_T-x_0$，也就是整个扩散过程（去噪过程的反向过程）的方向。
|
||||
|
||||
接下来我们分析一步迭代中发生的计算，在时间步 $t$，模型通过计算得到近似的 $x_T-x_0$ 后，我们计算下一步的 $x_{t-1}$：
|
||||
@@ -89,8 +87,6 @@ $$
|
||||
|
||||
训练过程不同于生成过程，如果我们在训练过程中保留多步迭代，那么梯度需经过多步回传，带来的时间和空间复杂度是灾难性的。为了提高计算效率，我们在训练中随机选择某一时间步 $t$ 进行训练。
|
||||
|
||||
（图）
|
||||
|
||||
以下是训练过程的伪代码
|
||||
|
||||
> 从数据集获取数据样本 $x_0$ 和引导条件 $c$
|
||||
@@ -111,7 +107,7 @@ $$
|
||||
|
||||
从理论到实践，还需要填充更多细节。现代 Diffusion 模型架构已经发展成熟，主流的架构沿用了 Latent Diffusion 所提出的“三段式”架构，包括数据编解码器、引导条件编码器、去噪模型三部分。
|
||||
|
||||
（图）
|
||||
|
||||
|
||||
### 数据编解码器
|
||||
|
||||
|
||||
Reference in New Issue
Block a user