update

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

@@ -0,0 +1,25 @@
+# dataset format: 
+# {
+#   "image": "path/to/win_image.png", # win image
+#   "lose_image": "path/to/lose_image.png", # lose image
+#   "prompt": "a photo of ...",
+# }
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/dpo.jsonl \
+  --data_file_keys "image,lose_image" \
+  --max_pixels 1048576 \
+  --dataset_repeat 400 \
+  --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" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image_DPO_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 \
+  --task dpo \
+  --beta_dpo 2500 \
+  --find_unused_parameters

examples/qwen_image/model_training/train.py

@@ -1,5 +1,4 @@
-import torch, os, json
-from diffsynth import load_state_dict
+import torch, os
 from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
 from diffsynth.pipelines.flux_image_new import ControlNetInput
 from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, qwen_image_parser, launch_training_task, launch_data_process_task
@@ -7,7 +6,6 @@ from diffsynth.trainers.unified_dataset import UnifiedDataset
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 
-
 class QwenImageTrainingModule(DiffusionTrainingModule):
     def __init__(
         self,
@@ -20,6 +18,7 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
         extra_inputs=None,
         enable_fp8_training=False,
         task="sft",
+        beta_dpo=1000.,
     ):
         super().__init__()
         # Load models
@@ -40,8 +39,9 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
         self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
         self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
         self.task = task
+        self.lora_base_model = lora_base_model
+        self.beta_dpo = beta_dpo
 
-    
     def forward_preprocess(self, data):
         # CFG-sensitive parameters
         inputs_posi = {"prompt": data["prompt"]}
@@ -81,9 +81,44 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
         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, return_inputs=False, **kwargs):
+
+    def forward_dpo(self, data, accelerator=None):
+        # Loss DPO: -logσ(−β(diff_policy − diff_ref))
+        # Prepare inputs
+        win_data = {key: data[key] for key in ["prompt", "image"]}
+        lose_data = {"prompt": None, "image": data["lose_image"]}
+        inputs_win = self.forward_preprocess(win_data)
+        inputs_lose = self.forward_preprocess(lose_data)
+        inputs_lose.update({key: inputs_win[key] for key in ["prompt", "prompt_emb", "prompt_emb_mask"]})
+        inputs_win.pop('noise')
+        inputs_lose.pop('noise')
+        models = {name: getattr(self.pipe, name) for name in self.pipe.in_iteration_models}
+        # sample timestep and noise
+        timestep = self.pipe.sample_timestep()
+        noise = torch.rand_like(inputs_win["latents"])
+        # compute diff_policy = loss_win - loss_lose
+        loss_win = self.pipe.training_loss_minimum(noise, timestep, **models, **inputs_win)
+        loss_lose = self.pipe.training_loss_minimum(noise, timestep, **models, **inputs_lose)
+        diff_policy = loss_win - loss_lose
+        # compute diff_ref
+        if self.lora_base_model is not None:
+            self.disable_all_lora_layers(accelerator.unwrap_model(self).pipe.dit)
+            # load the original model weights
+            with torch.no_grad():
+                loss_win_ref = self.pipe.training_loss_minimum(noise, timestep, **models, **inputs_win)
+                loss_lose_ref = self.pipe.training_loss_minimum(noise, timestep, **models, **inputs_lose)
+                diff_ref = loss_win_ref - loss_lose_ref
+            self.enable_all_lora_layers(accelerator.unwrap_model(self).pipe.dit)
+        else:
+            # TODO: may support full model training
+            raise NotImplementedError("DPO with full model training is not supported yet.")
+        # compute loss
+        loss = -1. * torch.nn.functional.logsigmoid(self.beta_dpo * (diff_ref - diff_policy)).mean()
+        return loss
+
+    def forward(self, data, inputs=None, return_inputs=False, accelerator=None, **kwargs):
+        if self.task == "dpo":
+            return self.forward_dpo(data, accelerator=accelerator)
         # Inputs
         if inputs is None:
             inputs = self.forward_preprocess(data)
@@ -137,11 +172,13 @@ if __name__ == "__main__":
         extra_inputs=args.extra_inputs,
         enable_fp8_training=args.enable_fp8_training,
         task=args.task,
+        beta_dpo=args.beta_dpo,
     )
     model_logger = ModelLogger(args.output_path, remove_prefix_in_ckpt=args.remove_prefix_in_ckpt)
     launcher_map = {
         "sft": launch_training_task,
         "data_process": launch_data_process_task,
         "direct_distill": launch_training_task,
+        "dpo": launch_training_task,
     }
     launcher_map[args.task](dataset, model, model_logger, args=args)

examples/qwen_image/model_training/train_dpo.py

@@ -1,184 +0,0 @@
-import torch, os
-from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
-from diffsynth.pipelines.flux_image_new import ControlNetInput
-from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, qwen_image_parser, launch_training_task, launch_data_process_task
-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,
-        task="sft",
-        beta_dpo=1000.,
-    ):
-        super().__init__()
-        # Load models
-        model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, enable_fp8_training=enable_fp8_training)
-        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)
-
-        # Training mode
-        self.switch_pipe_to_training_mode(
-            self.pipe, trainable_models,
-            lora_base_model, lora_target_modules, lora_rank, lora_checkpoint=lora_checkpoint,
-            enable_fp8_training=enable_fp8_training,
-        )
-        
-        # 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 []
-        self.task = task
-        self.lora_base_model = lora_base_model
-        self.beta_dpo = beta_dpo
-
-    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_dpo(self, data, accelerator=None):
-        # Loss DPO: -logσ(−β(diff_policy − diff_ref))
-        # Prepare inputs
-        win_data = {key: data[key] for key in ["prompt", "image"]}
-        lose_data = {"prompt": None, "image": data["lose_image"]}
-        inputs_win = self.forward_preprocess(win_data)
-        inputs_lose = self.forward_preprocess(lose_data)
-        inputs_lose.update({key: inputs_win[key] for key in ["prompt", "prompt_emb", "prompt_emb_mask"]})
-        inputs_win.pop('noise')
-        inputs_lose.pop('noise')
-        models = {name: getattr(self.pipe, name) for name in self.pipe.in_iteration_models}
-        # sample timestep and noise
-        timestep = self.pipe.sample_timestep()
-        noise = torch.rand_like(inputs_win["latents"])
-        # compute diff_policy = loss_win - loss_lose
-        loss_win = self.pipe.training_loss_minimum(noise, timestep, **models, **inputs_win)
-        loss_lose = self.pipe.training_loss_minimum(noise, timestep, **models, **inputs_lose)
-        diff_policy = loss_win - loss_lose
-        # compute diff_ref
-        if self.lora_base_model is not None:
-            self.disable_all_lora_layers(accelerator.unwrap_model(self).pipe.dit)
-            # load the original model weights
-            with torch.no_grad():
-                loss_win_ref = self.pipe.training_loss_minimum(noise, timestep, **models, **inputs_win)
-                loss_lose_ref = self.pipe.training_loss_minimum(noise, timestep, **models, **inputs_lose)
-                diff_ref = loss_win_ref - loss_lose_ref
-            self.enable_all_lora_layers(accelerator.unwrap_model(self).pipe.dit)
-        else:
-            # TODO: may support full model training
-            raise NotImplementedError("DPO with full model training is not supported yet.")
-        # compute loss
-        loss = -1. * torch.nn.functional.logsigmoid(self.beta_dpo * (diff_ref - diff_policy)).mean()
-        return loss
-
-    def forward(self, data, inputs=None, return_inputs=False, accelerator=None, **kwargs):
-        if self.task == "dpo":
-            return self.forward_dpo(data, accelerator=accelerator)
-        # Inputs
-        if inputs is None:
-            inputs = self.forward_preprocess(data)
-        else:
-            inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype)
-        if return_inputs: return inputs
-        
-        # Loss
-        if self.task == "sft":
-            models = {name: getattr(self.pipe, name) for name in self.pipe.in_iteration_models}
-            loss = self.pipe.training_loss(**models, **inputs)
-        elif self.task == "data_process":
-            loss = inputs
-        elif self.task == "direct_distill":
-            loss = self.pipe.direct_distill_loss(**inputs)
-        else:
-            raise NotImplementedError(f"Unsupported task: {self.task}.")
-        return loss
-
-
-
-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=args.dataset_repeat,
-        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,
-        task=args.task,
-        beta_dpo=args.beta_dpo,
-    )
-    model_logger = ModelLogger(args.output_path, remove_prefix_in_ckpt=args.remove_prefix_in_ckpt)
-    launcher_map = {
-        "sft": launch_training_task,
-        "data_process": launch_data_process_task,
-        "direct_distill": launch_training_task,
-        "dpo": launch_training_task,
-    }
-    launcher_map[args.task](dataset, model, model_logger, args=args)

examples/qwen_image/model_training/validate_lora/Qwen-Image-DPO.py

@@ -0,0 +1,19 @@
+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/"),
+)
+pipe.load_lora(pipe.dit, "models/train/Qwen-Image_DPO_lora/epoch-4.safetensors")
+prompt = "黑板上写着“群起效尤,心灵手巧”，字的颜色分别是 “群”: 橙色、“起”: 黑色、“效”: 蓝色、“尤”: 绿色、“心”: 紫色、“灵”: 粉色、“手”: 红色、“巧”: 白色"
+for seed in range(0, 5):
+    image = pipe(prompt, seed=seed)
+    image.save(f"image_dpo_{seed}.jpg")