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videogen
videogen
Commit 5a37d9d2 authored by Stefy Lanza (nextime / spora )'s avatar Stefy Lanza (nextime / spora )
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Fix pipeline component mismatch fallback and indentation 

- Add fallback mechanism for models with incorrect model_index.json
- Detect pipeline class from model ID patterns when component mismatch occurs
- Fix indentation error in auto mode retry logic block
- Properly handle Wan2.2-I2V models with misconfigured pipeline class
parent 4668132a
Pipeline #230 canceled with stages
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      ......@@ -3518,86 +3518,137 @@ def main(args):
      print(f" [DEBUG] Response: {e.response}")
      print()
      # Check if we should retry with an alternative model (auto mode)
      # This applies to ALL error types - we try alternatives before giving up
      if getattr(args, '_auto_mode', False):
      retry_count = getattr(args, '_retry_count', 0)
      max_retries = getattr(args, '_max_retries', 3)
      alternative_models = getattr(args, '_auto_alternative_models', [])
      failed_base_models = getattr(args, '_failed_base_models', set())
      user_specified_model = getattr(args, '_user_specified_model', False)
      # Check if this is a pipeline component mismatch error
      # This happens when the model_index.json has the wrong _class_name
      is_component_mismatch = "expected" in error_str and "but only" in error_str and "were passed" in error_str
      if is_component_mismatch:
      # Try to re-detect the correct pipeline class from model ID pattern
      detected_class = None
      model_id_lower = model_id_to_load.lower()
      # If user explicitly specified the model, don't retry with alternatives
      # The user's model choice should be preserved
      if user_specified_model:
      print(f"\n⚠️ User-specified model failed: {model_id_to_load}")
      print(f" The model was explicitly provided with --model, not retrying with alternatives.")
      print(f" Please verify the model exists or try a different model.")
      else:
      # If this was a LoRA with a base model, track the failed base model
      if is_lora and base_model_id:
      failed_base_models.add(base_model_id)
      args._failed_base_models = failed_base_models
      print(f" ⚠️ Base model failed: {base_model_id}")
      print(f" Will skip other LoRAs depending on this base model")
      # Force detection based on model ID patterns (most reliable for misconfigured models)
      if "wan2.1" in model_id_lower or "wan2.2" in model_id_lower or "wan2" in model_id_lower:
      detected_class = "WanPipeline"
      elif "svd" in model_id_lower or "stable-video-diffusion" in model_id_lower:
      detected_class = "StableVideoDiffusionPipeline"
      elif "ltx" in model_id_lower:
      detected_class = "LTXVideoPipeline"
      elif "mochi" in model_id_lower:
      detected_class = "MochiPipeline"
      elif "cogvideo" in model_id_lower:
      detected_class = "CogVideoXPipeline"
      elif "flux" in model_id_lower:
      detected_class = "FluxPipeline"
      if detected_class and detected_class != m_info["class"]:
      print(f"\n⚠️ Pipeline component mismatch detected!")
      print(f" Configured class: {m_info['class']}")
      print(f" Detected class: {detected_class}")
      print(f" The model's model_index.json may have an incorrect _class_name.")
      print(f" Retrying with detected pipeline class: {detected_class}")
      # Find next valid alternative (skip LoRAs with failed base models)
      next_model = None
      skipped_loras = []
      # Get the correct pipeline class
      CorrectPipelineClass = get_pipeline_class(detected_class)
      if CorrectPipelineClass:
      try:
      pipe = CorrectPipelineClass.from_pretrained(model_id_to_load, **pipe_kwargs)
      # Success! Update the model info for future runs
      print(f" ✅ Successfully loaded with {detected_class}")
      # Continue with the loaded pipeline
      timing.end_step() # model_loading
      # Update PipelineClass for the rest of the code
      PipelineClass = CorrectPipelineClass
      # Skip to after the error handling
      goto_after_loading = True
      except Exception as retry_e:
      print(f" ❌ Retry with {detected_class} also failed: {retry_e}")
      # Continue with normal error handling
      is_component_mismatch = False # Don't retry again below
      error_str = str(retry_e)
      # If we successfully loaded with the corrected pipeline, skip error handling
      if not locals().get('goto_after_loading', False):
      # Check if we should retry with an alternative model (auto mode)
      # This applies to ALL error types - we try alternatives before giving up
      if getattr(args, '_auto_mode', False):
      retry_count = getattr(args, '_retry_count', 0)
      max_retries = getattr(args, '_max_retries', 3)
      alternative_models = getattr(args, '_auto_alternative_models', [])
      failed_base_models = getattr(args, '_failed_base_models', set())
      user_specified_model = getattr(args, '_user_specified_model', False)
      while alternative_models:
      candidate_name, candidate_info, candidate_reason = alternative_models.pop(0)
      # If user explicitly specified the model, don't retry with alternatives
      # The user's model choice should be preserved
      if user_specified_model:
      print(f"\n⚠️ User-specified model failed: {model_id_to_load}")
      print(f" The model was explicitly provided with --model, not retrying with alternatives.")
      print(f" Please verify the model exists or try a different model.")
      else:
      # If this was a LoRA with a base model, track the failed base model
      if is_lora and base_model_id:
      failed_base_models.add(base_model_id)
      args._failed_base_models = failed_base_models
      print(f" ⚠️ Base model failed: {base_model_id}")
      print(f" Will skip other LoRAs depending on this base model")
      # Check if this is a LoRA with a failed base model
      if candidate_info.get("is_lora", False):
      candidate_base = candidate_info.get("base_model") or candidate_info.get("_inferred_base_model")
      if candidate_base and candidate_base in failed_base_models:
      skipped_loras.append((candidate_name, candidate_base))
      continue # Skip this LoRA
      # Find next valid alternative (skip LoRAs with failed base models)
      next_model = None
      skipped_loras = []
      # Found a valid candidate
      next_model = (candidate_name, candidate_info, candidate_reason)
      break
      # Update the alternatives list
      args._auto_alternative_models = alternative_models
      if skipped_loras:
      print(f" ⏭️ Skipped {len(skipped_loras)} LoRA(s) with failed base models")
      if retry_count < max_retries and next_model:
      # We have a valid alternative - retry with it
      args._retry_count = retry_count + 1
      next_model_name, next_model_info, next_reason = next_model
      while alternative_models:
      candidate_name, candidate_info, candidate_reason = alternative_models.pop(0)
      # Check if this is a LoRA with a failed base model
      if candidate_info.get("is_lora", False):
      candidate_base = candidate_info.get("base_model") or candidate_info.get("_inferred_base_model")
      if candidate_base and candidate_base in failed_base_models:
      skipped_loras.append((candidate_name, candidate_base))
      continue # Skip this LoRA
      # Found a valid candidate
      next_model = (candidate_name, candidate_info, candidate_reason)
      break
      # Print appropriate error message based on error type
      if "404" in error_str or "Entry Not Found" in error_str or "Repository Not Found" in error_str:
      print(f"❌ Model not found on HuggingFace: {model_id_to_load}")
      elif "401" in error_str or "Unauthorized" in error_str:
      print(f"❌ Model requires authentication: {model_id_to_load}")
      elif "FrozenDict" in error_str or "scale_factor" in error_str or "has no attribute" in error_str:
      print(f"❌ Pipeline compatibility error: {model_id_to_load}")
      print(f" This model uses an incompatible pipeline architecture.")
      else:
      print(f"❌ Model loading failed: {model_id_to_load}")
      print(f" Error: {error_str[:100]}...")
      # Update the alternatives list
      args._auto_alternative_models = alternative_models
      print(f"\n🔄 Retrying with alternative model ({args._retry_count}/{max_retries})...")
      print(f" New model: {next_model_name}")
      print(f" {next_reason}")
      if skipped_loras:
      print(f" ⏭️ Skipped {len(skipped_loras)} LoRA(s) with failed base models")
      # Update args with new model and recurse
      args.model = next_model_name
      # Clean up any partial model loading
      if torch.cuda.is_available():
      torch.cuda.empty_cache()
      # Retry main() with the new model
      return main(args)
      # No more valid alternatives or retries exhausted
      print(f"\n❌ All model retries exhausted ({retry_count}/{max_retries} attempts)")
      # Print detailed error message for the user
      if retry_count < max_retries and next_model:
      # We have a valid alternative - retry with it
      args._retry_count = retry_count + 1
      next_model_name, next_model_info, next_reason = next_model
      # Print appropriate error message based on error type
      if "404" in error_str or "Entry Not Found" in error_str or "Repository Not Found" in error_str:
      print(f"❌ Model not found on HuggingFace: {model_id_to_load}")
      elif "401" in error_str or "Unauthorized" in error_str:
      print(f"❌ Model requires authentication: {model_id_to_load}")
      elif "FrozenDict" in error_str or "scale_factor" in error_str or "has no attribute" in error_str:
      print(f"❌ Pipeline compatibility error: {model_id_to_load}")
      print(f" This model uses an incompatible pipeline architecture.")
      else:
      print(f"❌ Model loading failed: {model_id_to_load}")
      print(f" Error: {error_str[:100]}...")
      print(f"\n🔄 Retrying with alternative model ({args._retry_count}/{max_retries})...")
      print(f" New model: {next_model_name}")
      print(f" {next_reason}")
      # Update args with new model and recurse
      args.model = next_model_name
      # Clean up any partial model loading
      if torch.cuda.is_available():
      torch.cuda.empty_cache()
      # Retry main() with the new model
      return main(args)
      # No more valid alternatives or retries exhausted
      print(f"\n❌ All model retries exhausted ({retry_count}/{max_retries} attempts)")
      # Print detailed error message for the user
      if "404" in error_str or "Entry Not Found" in error_str:
      print(f"❌ Model not found on HuggingFace: {model_id_to_load}")
      print(f" This model may have been removed or the ID is incorrect.")
      ......
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