update

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)