Merge pull request #107 from modelscope/Artiprocher-dev

reduce VRAM requirements in Kolors LoRA

Author: Artiprocher

diffsynth/models/__init__.py

@@ -174,6 +174,9 @@
         ("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"),
         ("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"),
     ],
+    "SDXL-vae-fp16-fix": [
+        ("AI-ModelScope/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix")
+    ],
 }
 Preset_model_id: TypeAlias = Literal[
     "HunyuanDiT",
@@ -201,6 +204,7 @@
     "StableDiffusion3",
     "StableDiffusion3_without_T5",
     "Kolors",
+    "SDXL-vae-fp16-fix",
 ]
 Preset_model_website: TypeAlias = Literal[
     "HuggingFace",

examples/train/kolors/README.md

@@ -4,23 +4,27 @@ Kolors is a Chinese diffusion model, which is based on ChatGLM and Stable Diffus
 
 ## Download models
 
-The following files will be used for constructing Kolors. You can download them from [huggingface](https://huggingface.co/Kwai-Kolors/Kolors) or [modelscope](https://modelscope.cn/models/Kwai-Kolors/Kolors).
+The following files will be used for constructing Kolors. You can download Kolors from [huggingface](https://huggingface.co/Kwai-Kolors/Kolors) or [modelscope](https://modelscope.cn/models/Kwai-Kolors/Kolors). Due to precision overflow issues, we need to download an additional VAE model (from [huggingface](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix) or [modelscope](https://modelscope.cn/models/AI-ModelScope/sdxl-vae-fp16-fix)).
 
 ```
-models/kolors/Kolors
-├── text_encoder
-│   ├── config.json
-│   ├── pytorch_model-00001-of-00007.bin
-│   ├── pytorch_model-00002-of-00007.bin
-│   ├── pytorch_model-00003-of-00007.bin
-│   ├── pytorch_model-00004-of-00007.bin
-│   ├── pytorch_model-00005-of-00007.bin
-│   ├── pytorch_model-00006-of-00007.bin
-│   ├── pytorch_model-00007-of-00007.bin
-│   └── pytorch_model.bin.index.json
-├── unet
-│   └── diffusion_pytorch_model.safetensors
-└── vae
+models
+├── kolors
+│   └── Kolors
+│       ├── text_encoder
+│       │   ├── config.json
+│       │   ├── pytorch_model-00001-of-00007.bin
+│       │   ├── pytorch_model-00002-of-00007.bin
+│       │   ├── pytorch_model-00003-of-00007.bin
+│       │   ├── pytorch_model-00004-of-00007.bin
+│       │   ├── pytorch_model-00005-of-00007.bin
+│       │   ├── pytorch_model-00006-of-00007.bin
+│       │   ├── pytorch_model-00007-of-00007.bin
+│       │   └── pytorch_model.bin.index.json
+│       ├── unet
+│       │   └── diffusion_pytorch_model.safetensors
+│       └── vae
+│           └── diffusion_pytorch_model.safetensors
+└── sdxl-vae-fp16-fix
     └── diffusion_pytorch_model.safetensors
 ```
 
@@ -29,7 +33,7 @@ You can use the following code to download these files:
 ```python
 from diffsynth import download_models
 
-download_models(["Kolors"])
+download_models(["Kolors", "SDXL-vae-fp16-fix"])
 ```
 
 ## Train
@@ -70,24 +74,30 @@ file_name,text
 
 We provide a training script `train_kolors_lora.py`. Before you run this training script, please copy it to the root directory of this project.
 
-The following settings are recommended. **We found the UNet model suffers from precision overflow issues, thus the training script doesn't support float16. 40GB VRAM is required. We are working on overcoming this pitfall.**
+The following settings are recommended. 22GB VRAM is required.
 
 ```
 CUDA_VISIBLE_DEVICES="0" python examples/train/kolors/train_kolors_lora.py \
-  --pretrained_path models/kolors/Kolors \
+  --pretrained_unet_path models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors \
+  --pretrained_text_encoder_path models/kolors/Kolors/text_encoder \
+  --pretrained_fp16_vae_path models/sdxl-vae-fp16-fix/diffusion_pytorch_model.safetensors \
   --dataset_path data/dog \
   --output_path ./models \
   --max_epochs 10 \
   --center_crop \
   --use_gradient_checkpointing \
-  --precision 32
+  --precision "16-mixed"
 ```
 
 Optional arguments:
 ```
   -h, --help            show this help message and exit
-  --pretrained_path PRETRAINED_PATH
-                        Path to pretrained model. For example, `models/kolors/Kolors`.
+  --pretrained_unet_path PRETRAINED_UNET_PATH
+                        Path to pretrained model (UNet). For example, `models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors`.
+  --pretrained_text_encoder_path PRETRAINED_TEXT_ENCODER_PATH
+                        Path to pretrained model (Text Encoder). For example, `models/kolors/Kolors/text_encoder`.
+  --pretrained_fp16_vae_path PRETRAINED_FP16_VAE_PATH
+                        Path to pretrained model (VAE). For example, `models/kolors/Kolors/sdxl-vae-fp16-fix/diffusion_pytorch_model.safetensors`.
   --dataset_path DATASET_PATH
                         The path of the Dataset.
   --output_path OUTPUT_PATH

examples/train/kolors/train_kolors_lora.py

@@ -1,4 +1,4 @@
-from diffsynth import ModelManager, KolorsImagePipeline
+from diffsynth import KolorsImagePipeline, load_state_dict, ChatGLMModel, SDXLUNet, SDXLVAEEncoder
 from peft import LoraConfig, inject_adapter_in_model
 from torchvision import transforms
 from PIL import Image
@@ -40,23 +40,40 @@ def __len__(self):
 
 
 
+def load_model_from_diffsynth(ModelClass, model_kwargs, state_dict_path, torch_dtype, device):
+    model = ModelClass(**model_kwargs).to(dtype=torch_dtype, device=device)
+    state_dict = load_state_dict(state_dict_path, torch_dtype=torch_dtype)
+    model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict))
+    return model
+
+
+def load_model_from_transformers(ModelClass, model_kwargs, state_dict_path, torch_dtype, device):
+    model = ModelClass.from_pretrained(state_dict_path, torch_dtype=torch_dtype)
+    model = model.to(dtype=torch_dtype, device=device)
+    return model
+    
+
+
 class LightningModel(pl.LightningModule):
-    def __init__(self, torch_dtype=torch.float16, learning_rate=1e-4, pretrained_weights=[], lora_rank=4, lora_alpha=4, use_gradient_checkpointing=True):
+    def __init__(
+        self,
+        pretrained_unet_path, pretrained_text_encoder_path, pretrained_fp16_vae_path,
+        torch_dtype=torch.float16, learning_rate=1e-4, lora_rank=4, lora_alpha=4, use_gradient_checkpointing=True
+    ):
         super().__init__()
 
         # Load models
-        model_manager = ModelManager(torch_dtype=torch_dtype, device=self.device)
-        model_manager.load_models(pretrained_weights)
-        self.pipe = KolorsImagePipeline.from_model_manager(model_manager)
+        self.pipe = KolorsImagePipeline(device=self.device, torch_dtype=torch_dtype)
+        self.pipe.text_encoder = load_model_from_transformers(ChatGLMModel, {}, pretrained_text_encoder_path, torch_dtype, self.device)
+        self.pipe.unet = load_model_from_diffsynth(SDXLUNet, {"is_kolors": True}, pretrained_unet_path, torch_dtype, self.device)
+        self.pipe.vae_encoder = load_model_from_diffsynth(SDXLVAEEncoder, {}, pretrained_fp16_vae_path, torch_dtype, self.device)
 
         # Freeze parameters
         self.pipe.text_encoder.requires_grad_(False)
         self.pipe.unet.requires_grad_(False)
-        self.pipe.vae_decoder.requires_grad_(False)
         self.pipe.vae_encoder.requires_grad_(False)
         self.pipe.text_encoder.eval()
         self.pipe.unet.train()
-        self.pipe.vae_decoder.eval()
         self.pipe.vae_encoder.eval()
 
         # Add LoRA to UNet
@@ -88,7 +105,7 @@ def training_step(self, batch, batch_idx):
             self.pipe.text_encoder, text, clip_skip=2, device=self.device, positive=True,
         )
         height, width = image.shape[-2:]
-        latents = self.pipe.vae_encoder(image.to(dtype=torch.float32, device=self.device)).to(self.pipe.torch_dtype)
+        latents = self.pipe.vae_encoder(image.to(self.device))
         noise = torch.randn_like(latents)
         timestep = torch.randint(0, 1100, (1,), device=self.device)[0]
         add_time_id = torch.tensor([height, width, 0, 0, height, width], device=self.device)
@@ -126,11 +143,25 @@ def on_save_checkpoint(self, checkpoint):
 def parse_args():
     parser = argparse.ArgumentParser(description="Simple example of a training script.")
     parser.add_argument(
-        "--pretrained_path",
+        "--pretrained_unet_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained model (UNet). For example, `models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors`.",
+    )
+    parser.add_argument(
+        "--pretrained_text_encoder_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained model (Text Encoder). For example, `models/kolors/Kolors/text_encoder`.",
+    )
+    parser.add_argument(
+        "--pretrained_fp16_vae_path",
         type=str,
         default=None,
         required=True,
-        help="Path to pretrained model. For example, `models/kolors/Kolors`.",
+        help="Path to pretrained model (VAE). For example, `models/kolors/Kolors/sdxl-vae-fp16-fix/diffusion_pytorch_model.safetensors`.",
     )
     parser.add_argument(
         "--dataset_path",
@@ -267,11 +298,9 @@ def parse_args():
 
     # model
     model = LightningModel(
-        pretrained_weights=[
-            os.path.join(args.pretrained_path, "text_encoder"),
-            os.path.join(args.pretrained_path, "unet/diffusion_pytorch_model.safetensors"),
-            os.path.join(args.pretrained_path, "vae/diffusion_pytorch_model.safetensors"),
-        ],
+        args.pretrained_unet_path,
+        args.pretrained_text_encoder_path,
+        args.pretrained_fp16_vae_path,
         torch_dtype=torch.float32 if args.precision == "32" else torch.float16,
         learning_rate=args.learning_rate,
         lora_rank=args.lora_rank,