qwen-image splited training

Author: Artiprocher

diffsynth/pipelines/qwen_image.py

@@ -174,9 +174,12 @@ def _enable_fp8_lora_training(self, dtype):
             computation_dtype=self.torch_dtype,
             computation_device="cuda",
         )
-        enable_vram_management(self.text_encoder, module_map=module_map, module_config=model_config)
-        enable_vram_management(self.dit, module_map=module_map, module_config=model_config)
-        enable_vram_management(self.vae, module_map=module_map, module_config=model_config)
+        if self.text_encoder is not None:
+            enable_vram_management(self.text_encoder, module_map=module_map, module_config=model_config)
+        if self.dit is not None:
+            enable_vram_management(self.dit, module_map=module_map, module_config=model_config)
+        if self.vae is not None:
+            enable_vram_management(self.vae, module_map=module_map, module_config=model_config)
 
 
     def enable_vram_management(self, num_persistent_param_in_dit=None, vram_limit=None, vram_buffer=0.5, enable_dit_fp8_computation=False):

diffsynth/trainers/unified_dataset.py

@@ -214,7 +214,7 @@ def __init__(self, map_location="cpu"):
         self.map_location = map_location
 
     def __call__(self, data):
-        return torch.load(data, map_location=self.map_location)
+        return torch.load(data, map_location=self.map_location, weights_only=False)
 
 
 
@@ -306,7 +306,7 @@ def load_metadata(self, metadata_path):
 
     def __getitem__(self, data_id):
         if self.load_from_cache:
-            data = self.cached_data[data_id % len(self.data)].copy()
+            data = self.cached_data[data_id % len(self.cached_data)]
             data = self.cached_data_operator(data)
         else:
             data = self.data[data_id % len(self.data)].copy()

diffsynth/trainers/utils.py

@@ -417,6 +417,13 @@ def export_trainable_state_dict(self, state_dict, remove_prefix=None):
                 state_dict_[name] = param
             state_dict = state_dict_
         return state_dict
+    
+    
+    def transfer_data_to_device(self, data, device):
+        for key in data:
+            if isinstance(data[key], torch.Tensor):
+                data[key] = data[key].to(device)
+        return data
 
 
 
@@ -484,7 +491,10 @@ def launch_training_task(
         for data in tqdm(dataloader):
             with accelerator.accumulate(model):
                 optimizer.zero_grad()
-                loss = model(data)
+                if dataset.load_from_cache:
+                    loss = model({}, inputs=data)
+                else:
+                    loss = model(data)
                 accelerator.backward(loss)
                 optimizer.step()
                 model_logger.on_step_end(accelerator, model, save_steps)
@@ -494,16 +504,24 @@ def launch_training_task(
     model_logger.on_training_end(accelerator, model, save_steps)
 
 
-def launch_data_process_task(model: DiffusionTrainingModule, dataset, output_path="./models"):
-    dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0])
+def launch_data_process_task(
+    dataset: torch.utils.data.Dataset,
+    model: DiffusionTrainingModule,
+    model_logger: ModelLogger,
+    num_workers: int = 8,
+):
+    dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], num_workers=num_workers)
     accelerator = Accelerator()
     model, dataloader = accelerator.prepare(model, dataloader)
-    os.makedirs(os.path.join(output_path, "data_cache"), exist_ok=True)
-    for data_id, data in enumerate(tqdm(dataloader)):
-        with torch.no_grad():
-            inputs = model.forward_preprocess(data)
-            inputs = {key: inputs[key] for key in model.model_input_keys if key in inputs}
-            torch.save(inputs, os.path.join(output_path, "data_cache", f"{data_id}.pth"))
+    
+    for data_id, data in tqdm(enumerate(dataloader)):
+        with accelerator.accumulate(model):
+            with torch.no_grad():
+                folder = os.path.join(model_logger.output_path, str(accelerator.process_index))
+                os.makedirs(folder, exist_ok=True)
+                save_path = os.path.join(model_logger.output_path, str(accelerator.process_index), f"{data_id}.pth")
+                data = model(data)
+                torch.save(data, save_path)
 
 
 

examples/qwen_image/model_training/lora/Qwen-Image-Splited.sh

@@ -0,0 +1,25 @@
+accelerate launch examples/qwen_image/model_training/train_data_process.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \
+  --output_path "./models/train/Qwen-Image_lora_cache" \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8
+
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path models/train/Qwen-Image_lora_cache \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --find_unused_parameters \
+  --enable_fp8_training

examples/qwen_image/model_training/train.py

@@ -111,6 +111,7 @@ def forward_preprocess(self, data):
 
     def forward(self, data, inputs=None):
         if inputs is None: inputs = self.forward_preprocess(data)
+        else: inputs = self.transfer_data_to_device(inputs, self.pipe.device)
         models = {name: getattr(self.pipe, name) for name in self.pipe.in_iteration_models}
         loss = self.pipe.training_loss(**models, **inputs)
         return loss

examples/qwen_image/model_training/train_data_process.py

@@ -0,0 +1,154 @@
+import torch, os, json
+from diffsynth import load_state_dict
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from diffsynth.pipelines.flux_image_new import ControlNetInput
+from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, launch_data_process_task, qwen_image_parser
+from diffsynth.trainers.unified_dataset import UnifiedDataset
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+
+class QwenImageTrainingModule(DiffusionTrainingModule):
+    def __init__(
+        self,
+        model_paths=None, model_id_with_origin_paths=None,
+        tokenizer_path=None, processor_path=None,
+        trainable_models=None,
+        lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None,
+        use_gradient_checkpointing=True,
+        use_gradient_checkpointing_offload=False,
+        extra_inputs=None,
+        enable_fp8_training=False,
+    ):
+        super().__init__()
+        # Load models
+        offload_dtype = torch.float8_e4m3fn if enable_fp8_training else None
+        model_configs = []
+        if model_paths is not None:
+            model_paths = json.loads(model_paths)
+            model_configs += [ModelConfig(path=path, offload_dtype=offload_dtype) for path in model_paths]
+        if model_id_with_origin_paths is not None:
+            model_id_with_origin_paths = model_id_with_origin_paths.split(",")
+            model_configs += [ModelConfig(model_id=i.split(":")[0], origin_file_pattern=i.split(":")[1], offload_dtype=offload_dtype) for i in model_id_with_origin_paths]
+
+        tokenizer_config = ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/") if tokenizer_path is None else ModelConfig(tokenizer_path)
+        processor_config = ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/") if processor_path is None else ModelConfig(processor_path)
+        self.pipe = QwenImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs, tokenizer_config=tokenizer_config, processor_config=processor_config)
+        
+        # Enable FP8
+        if enable_fp8_training:
+            self.pipe._enable_fp8_lora_training(torch.float8_e4m3fn)
+        
+        # Reset training scheduler (do it in each training step)
+        self.pipe.scheduler.set_timesteps(1000, training=True)
+        
+        # Freeze untrainable models
+        self.pipe.freeze_except([] if trainable_models is None else trainable_models.split(","))
+        
+        # Add LoRA to the base models
+        if lora_base_model is not None:
+            model = self.add_lora_to_model(
+                getattr(self.pipe, lora_base_model),
+                target_modules=lora_target_modules.split(","),
+                lora_rank=lora_rank,
+                upcast_dtype=self.pipe.torch_dtype,
+            )
+            if lora_checkpoint is not None:
+                state_dict = load_state_dict(lora_checkpoint)
+                state_dict = self.mapping_lora_state_dict(state_dict)
+                load_result = model.load_state_dict(state_dict, strict=False)
+                print(f"LoRA checkpoint loaded: {lora_checkpoint}, total {len(state_dict)} keys")
+                if len(load_result[1]) > 0:
+                    print(f"Warning, LoRA key mismatch! Unexpected keys in LoRA checkpoint: {load_result[1]}")
+            setattr(self.pipe, lora_base_model, model)
+            
+        # Store other configs
+        self.use_gradient_checkpointing = use_gradient_checkpointing
+        self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
+        self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
+
+    
+    def forward_preprocess(self, data):
+        # CFG-sensitive parameters
+        inputs_posi = {"prompt": data["prompt"]}
+        inputs_nega = {"negative_prompt": ""}
+        
+        # CFG-unsensitive parameters
+        inputs_shared = {
+            # Assume you are using this pipeline for inference,
+            # please fill in the input parameters.
+            "input_image": data["image"],
+            "height": data["image"].size[1],
+            "width": data["image"].size[0],
+            # Please do not modify the following parameters
+            # unless you clearly know what this will cause.
+            "cfg_scale": 1,
+            "rand_device": self.pipe.device,
+            "use_gradient_checkpointing": self.use_gradient_checkpointing,
+            "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload,
+            "edit_image_auto_resize": True,
+        }
+        
+        # Extra inputs
+        controlnet_input, blockwise_controlnet_input = {}, {}
+        for extra_input in self.extra_inputs:
+            if extra_input.startswith("blockwise_controlnet_"):
+                blockwise_controlnet_input[extra_input.replace("blockwise_controlnet_", "")] = data[extra_input]
+            elif extra_input.startswith("controlnet_"):
+                controlnet_input[extra_input.replace("controlnet_", "")] = data[extra_input]
+            else:
+                inputs_shared[extra_input] = data[extra_input]
+        if len(controlnet_input) > 0:
+            inputs_shared["controlnet_inputs"] = [ControlNetInput(**controlnet_input)]
+        if len(blockwise_controlnet_input) > 0:
+            inputs_shared["blockwise_controlnet_inputs"] = [ControlNetInput(**blockwise_controlnet_input)]
+        
+        # Pipeline units will automatically process the input parameters.
+        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)
+        return {**inputs_shared, **inputs_posi}
+    
+    
+    def forward(self, data, inputs=None):
+        if inputs is None: inputs = self.forward_preprocess(data)
+        return inputs
+
+
+
+if __name__ == "__main__":
+    parser = qwen_image_parser()
+    args = parser.parse_args()
+    dataset = UnifiedDataset(
+        base_path=args.dataset_base_path,
+        metadata_path=args.dataset_metadata_path,
+        repeat=1, # Set repeat = 1
+        data_file_keys=args.data_file_keys.split(","),
+        main_data_operator=UnifiedDataset.default_image_operator(
+            base_path=args.dataset_base_path,
+            max_pixels=args.max_pixels,
+            height=args.height,
+            width=args.width,
+            height_division_factor=16,
+            width_division_factor=16,
+        )
+    )
+    model = QwenImageTrainingModule(
+        model_paths=args.model_paths,
+        model_id_with_origin_paths=args.model_id_with_origin_paths,
+        tokenizer_path=args.tokenizer_path,
+        processor_path=args.processor_path,
+        trainable_models=args.trainable_models,
+        lora_base_model=args.lora_base_model,
+        lora_target_modules=args.lora_target_modules,
+        lora_rank=args.lora_rank,
+        lora_checkpoint=args.lora_checkpoint,
+        use_gradient_checkpointing=args.use_gradient_checkpointing,
+        use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
+        extra_inputs=args.extra_inputs,
+        enable_fp8_training=args.enable_fp8_training,
+    )
+    model_logger = ModelLogger(args.output_path, remove_prefix_in_ckpt=args.remove_prefix_in_ckpt)
+    launch_data_process_task(
+        dataset, model, model_logger,
+        num_workers=args.dataset_num_workers,
+    )

test.py

@@ -0,0 +1,6 @@
+import torch
+
+
+data = torch.load("models/train/Qwen-Image_lora_cache/0/0.pth", map_location="cpu", weights_only=False)
+for i in data:
+    print(i)