address some reviews; change hunyuan checkpoint repo-id

README.md

@@ -273,8 +273,7 @@ OUTPUT_DIR="/path/to/models/hunyuan-video/hunyuan-video-loras/hunyuan-video_caki
 
 # Model arguments
 model_cmd="--model_name hunyuan_video \
-  --pretrained_model_name_or_path tencent/HunyuanVideo
-  --revision refs/pr/18"
+  --pretrained_model_name_or_path hunyuanvideo-community/HunyuanVideo"
 
 # Dataset arguments
 dataset_cmd="--data_root $DATA_ROOT \
@@ -356,7 +355,7 @@ import torch
 from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel
 from diffusers.utils import export_to_video
 
-model_id = "tencent/HunyuanVideo"
+model_id = "hunyuanvideo-community/HunyuanVideo"
 transformer = HunyuanVideoTransformer3DModel.from_pretrained(
     model_id, subfolder="transformer", torch_dtype=torch.bfloat16
 )

finetrainers/hunyuan_video/hunyuan_video_lora.py

@@ -17,7 +17,7 @@ logger = get_logger("finetrainers")  # pylint: disable=invalid-name
 
 
 def load_condition_models(
-    model_id: str = "tencent/HunyuanVideo",
+    model_id: str = "hunyuanvideo-community/HunyuanVideo",
     text_encoder_dtype: torch.dtype = torch.float16,
     text_encoder_2_dtype: torch.dtype = torch.float16,
     revision: Optional[str] = None,
@@ -43,7 +43,7 @@ def load_condition_models(
 
 
 def load_latent_models(
-    model_id: str = "tencent/HunyuanVideo",
+    model_id: str = "hunyuanvideo-community/HunyuanVideo",
     vae_dtype: torch.dtype = torch.float16,
     revision: Optional[str] = None,
     cache_dir: Optional[str] = None,
@@ -56,12 +56,12 @@ def load_latent_models(
 
 
 def load_diffusion_models(
-    model_id: str = "tencent/HunyuanVideo",
+    model_id: str = "hunyuanvideo-community/HunyuanVideo",
     transformer_dtype: torch.dtype = torch.bfloat16,
     revision: Optional[str] = None,
     cache_dir: Optional[str] = None,
     **kwargs,
-) -> Dict[str, nn.Module]:
+) -> Dict[str, Union[nn.Module, FlowMatchEulerDiscreteScheduler]]:
     transformer = HunyuanVideoTransformer3DModel.from_pretrained(
         model_id, subfolder="transformer", torch_dtype=transformer_dtype, revision=revision, cache_dir=cache_dir
     )
@@ -70,7 +70,7 @@ def load_diffusion_models(
 
 
 def initialize_pipeline(
-    model_id: str = "tencent/HunyuanVideo",
+    model_id: str = "hunyuanvideo-community/HunyuanVideo",
     text_encoder_dtype: torch.dtype = torch.float16,
     text_encoder_2_dtype: torch.dtype = torch.float16,
     transformer_dtype: torch.dtype = torch.bfloat16,

finetrainers/trainer.py

@@ -138,7 +138,7 @@ class Trainer:
         self.vae = components.get("vae", self.vae)
         self.scheduler = components.get("scheduler", self.scheduler)
 
-    def _nuke_components(self) -> None:
+    def _delete_components(self) -> None:
         self.tokenizer = None
         self.tokenizer_2 = None
         self.tokenizer_3 = None
@@ -274,7 +274,7 @@ class Trainer:
             torch.save(other_conditions, filename.as_posix())
             index += 1
             progress_bar.update(1)
-        self._nuke_components()
+        self._delete_components()
 
         memory_statistics = get_memory_statistics()
         logger.info(f"Memory after precomputing conditions: {json.dumps(memory_statistics, indent=4)}")
@@ -323,7 +323,7 @@ class Trainer:
             torch.save(latent_conditions, filename.as_posix())
             index += 1
             progress_bar.update(1)
-        self._nuke_components()
+        self._delete_components()
 
         self.state.accelerator.wait_for_everyone()
         logger.info("Precomputation complete")
@@ -669,7 +669,8 @@ class Trainer:
                     accelerator.backward(loss)
 
                     if accelerator.sync_gradients and accelerator.distributed_type != DistributedType.DEEPSPEED:
-                        accelerator.clip_grad_norm_(self.transformer.parameters(), self.args.max_grad_norm)
+                        grad_norm = accelerator.clip_grad_norm_(self.transformer.parameters(), self.args.max_grad_norm)
+                        logs["grad_norm"] = grad_norm
 
                     self.optimizer.step()
                     self.lr_scheduler.step()