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videogen
videogen
Commit 4668132a authored by Stefy Lanza (nextime / spora )'s avatar Stefy Lanza (nextime / spora )
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Preserve user-specified model in auto mode retry logic 

- Track if user explicitly specified --model before auto mode runs
- Skip retry with alternative models when user's model fails
- Show clear error message explaining user's choice is preserved
- Only auto-selected models can be retried with alternatives
parent be1e5b9d
Pipeline #229 canceled with stages
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      Showing with 70 additions and 57 deletions
      videogen
      View file @ 4668132a
      ...@@ -3318,6 +3318,11 @@ def main(args): ...@@ -3318,6 +3318,11 @@ def main(args):
      if getattr(args, 'auto', False) and not hasattr(args, '_auto_mode'): if getattr(args, 'auto', False) and not hasattr(args, '_auto_mode'):
      if not args.prompt: if not args.prompt:
      parser.error("--auto requires --prompt to analyze") parser.error("--auto requires --prompt to analyze")
      # Track if user explicitly specified the model (before auto mode modifies it)
      # This is used to preserve user's model choice during retry
      args._user_specified_model = args.model is not None and args.model != (list(MODELS.keys())[0] if MODELS else None)
      args = run_auto_mode(args, MODELS) args = run_auto_mode(args, MODELS)
      if args is None: if args is None:
      sys.exit(1) sys.exit(1)
      ...@@ -3520,69 +3525,77 @@ def main(args): ...@@ -3520,69 +3525,77 @@ def main(args):
      max_retries = getattr(args, '_max_retries', 3) max_retries = getattr(args, '_max_retries', 3)
      alternative_models = getattr(args, '_auto_alternative_models', []) alternative_models = getattr(args, '_auto_alternative_models', [])
      failed_base_models = getattr(args, '_failed_base_models', set()) failed_base_models = getattr(args, '_failed_base_models', set())
      user_specified_model = getattr(args, '_user_specified_model', False)
      # If this was a LoRA with a base model, track the failed base model # If user explicitly specified the model, don't retry with alternatives
      if is_lora and base_model_id: # The user's model choice should be preserved
      failed_base_models.add(base_model_id) if user_specified_model:
      args._failed_base_models = failed_base_models print(f"\n⚠️ User-specified model failed: {model_id_to_load}")
      print(f" ⚠️ Base model failed: {base_model_id}") print(f" The model was explicitly provided with --model, not retrying with alternatives.")
      print(f" Will skip other LoRAs depending on this base model") print(f" Please verify the model exists or try a different model.")
      else:
      # Find next valid alternative (skip LoRAs with failed base models) # If this was a LoRA with a base model, track the failed base model
      next_model = None if is_lora and base_model_id:
      skipped_loras = [] failed_base_models.add(base_model_id)
      args._failed_base_models = failed_base_models
      while alternative_models: print(f" ⚠️ Base model failed: {base_model_id}")
      candidate_name, candidate_info, candidate_reason = alternative_models.pop(0) print(f" Will skip other LoRAs depending on this base model")
      # Check if this is a LoRA with a failed base model # Find next valid alternative (skip LoRAs with failed base models)
      if candidate_info.get("is_lora", False): next_model = None
      candidate_base = candidate_info.get("base_model") or candidate_info.get("_inferred_base_model") skipped_loras = []
      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 while alternative_models:
      next_model = (candidate_name, candidate_info, candidate_reason) candidate_name, candidate_info, candidate_reason = alternative_models.pop(0)
      break
      # Check if this is a LoRA with a failed base model
      # Update the alternatives list if candidate_info.get("is_lora", False):
      args._auto_alternative_models = alternative_models candidate_base = candidate_info.get("base_model") or candidate_info.get("_inferred_base_model")
      if candidate_base and candidate_base in failed_base_models:
      if skipped_loras: skipped_loras.append((candidate_name, candidate_base))
      print(f" ⏭️ Skipped {len(skipped_loras)} LoRA(s) with failed base models") continue # Skip this LoRA
      if retry_count < max_retries and next_model: # Found a valid candidate
      # We have a valid alternative - retry with it next_model = (candidate_name, candidate_info, candidate_reason)
      args._retry_count = retry_count + 1 break
      next_model_name, next_model_info, next_reason = next_model
      # Print appropriate error message based on error type # Update the alternatives list
      if "404" in error_str or "Entry Not Found" in error_str or "Repository Not Found" in error_str: args._auto_alternative_models = alternative_models
      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})...") if skipped_loras:
      print(f" New model: {next_model_name}") print(f" ⏭️ Skipped {len(skipped_loras)} LoRA(s) with failed base models")
      print(f" {next_reason}")
      # Update args with new model and recurse if retry_count < max_retries and next_model:
      args.model = next_model_name # We have a valid alternative - retry with it
      # Clean up any partial model loading args._retry_count = retry_count + 1
      if torch.cuda.is_available(): next_model_name, next_model_info, next_reason = next_model
      torch.cuda.empty_cache()
      # Retry main() with the new model # Print appropriate error message based on error type
      return main(args) 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}")
      # No more valid alternatives or retries exhausted elif "401" in error_str or "Unauthorized" in error_str:
      print(f"\n❌ All model retries exhausted ({retry_count}/{max_retries} attempts)") 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 # Print detailed error message for the user
      if "404" in error_str or "Entry Not Found" in error_str: if "404" in error_str or "Entry Not Found" in error_str:
      ......
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