updates

training/mochi-1/dataset_mochi.py

@@ -5,7 +5,6 @@ import numpy as np
 import torch
 import torchvision.transforms as TT
 from accelerate.logging import get_logger
-from torchvision import transforms
 from torchvision.transforms import InterpolationMode
 from torchvision.transforms.functional import resize
 

training/mochi-1/text_to_video_lora.py

@@ -76,7 +76,7 @@ def save_model_card(
     fps=8,
 ):
     widget_dict = []
-    if videos is not None:
+    if videos is not None and len(videos) > 0:
         for i, video in enumerate(videos):
             export_to_video(video, os.path.join(repo_folder, f"final_video_{i}.mp4", fps=fps))
             widget_dict.append(
@@ -876,34 +876,34 @@ def main(args):
         torch.cuda.empty_cache()
         torch.cuda.synchronize(accelerator.device)
 
-        accelerator.print("===== Memory before testing =====")
-        print_memory(accelerator.device)
-        reset_memory(accelerator.device)
-
         # Final test inference
-        pipe = MochiPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            revision=args.revision,
-            variant=args.variant,
-            # torch_dtype=weight_dtype,
-        )
-        pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipe.scheduler.config)
-
-        if args.enable_slicing:
-            pipe.vae.enable_slicing()
-        if args.enable_tiling:
-            pipe.vae.enable_tiling()
-        if args.enable_model_cpu_offload:
-            pipe.enable_model_cpu_offload()
-
-        # Load LoRA weights
-        lora_scaling = args.lora_alpha / args.rank
-        pipe.load_lora_weights(args.output_dir, adapter_name="mochi-lora")
-        pipe.set_adapters(["mochi-lora"], [lora_scaling])
-
-        # Run inference
         validation_outputs = []
         if args.validation_prompt and args.num_validation_videos > 0:
+            accelerator.print("===== Memory before testing =====")
+            print_memory(accelerator.device)
+            reset_memory(accelerator.device)
+            
+            pipe = MochiPipeline.from_pretrained(
+                args.pretrained_model_name_or_path,
+                revision=args.revision,
+                variant=args.variant,
+                # torch_dtype=weight_dtype,
+            )
+            pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipe.scheduler.config)
+
+            if args.enable_slicing:
+                pipe.vae.enable_slicing()
+            if args.enable_tiling:
+                pipe.vae.enable_tiling()
+            if args.enable_model_cpu_offload:
+                pipe.enable_model_cpu_offload()
+
+            # Load LoRA weights
+            lora_scaling = args.lora_alpha / args.rank
+            pipe.load_lora_weights(args.output_dir, adapter_name="mochi-lora")
+            pipe.set_adapters(["mochi-lora"], [lora_scaling])
+
+            # Run inference
             validation_prompts = args.validation_prompt.split(args.validation_prompt_separator)
             for validation_prompt in validation_prompts:
                 pipeline_args = {
@@ -924,10 +924,10 @@ def main(args):
                 )
                 validation_outputs.extend(video)
 
-        accelerator.print("===== Memory after testing =====")
-        print_memory(accelerator.device)
-        reset_memory(accelerator.device)
-        torch.cuda.synchronize(accelerator.device)
+            accelerator.print("===== Memory after testing =====")
+            print_memory(accelerator.device)
+            reset_memory(accelerator.device)
+            torch.cuda.synchronize(accelerator.device)
 
         if args.push_to_hub:
             save_model_card(
@@ -942,7 +942,7 @@ def main(args):
                 repo_id=repo_id,
                 folder_path=args.output_dir,
                 commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*", "*.bin"],
+                ignore_patterns=["step_*", "epoch_*", "*.bin", "*.pt"],
             )
 
     accelerator.end_training()