Fix image generation to handle LoRA adapters - load base model first, then apply LoRA weights

videogen

@@ -4030,6 +4030,34 @@ def main(args):
             timing.begin_step("image_generation")
             
             img_info = MODELS[args.image_model]
+            
+            # Check if image model is a LoRA adapter
+            img_is_lora = img_info.get("is_lora", False)
+            img_lora_id = None
+            img_base_model_id = None
+            img_model_id_to_load = img_info["id"]
+            
+            if img_is_lora:
+                img_lora_id = img_info["id"]
+                img_base_model_id = img_info.get("base_model")
+                
+                # Try to infer base model from LoRA name if not specified
+                if not img_base_model_id:
+                    if "flux" in img_lora_id.lower():
+                        img_base_model_id = "black-forest-labs/FLUX.1-dev"
+                    elif "sdxl" in img_lora_id.lower():
+                        img_base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+                    elif "sd15" in img_lora_id.lower() or "sd1.5" in img_lora_id.lower():
+                        img_base_model_id = "runwayml/stable-diffusion-v1-5"
+                    else:
+                        # Default to Flux for unknown image LoRAs
+                        img_base_model_id = "black-forest-labs/FLUX.1-dev"
+                
+                print(f"  📦 Image model is a LoRA adapter")
+                print(f"     LoRA: {img_lora_id}")
+                print(f"     Base model: {img_base_model_id}")
+                img_model_id_to_load = img_base_model_id
+            
             ImgCls = get_pipeline_class(img_info["class"])
             if not ImgCls:
                 print(f"❌ Pipeline class '{img_info['class']}' not found for image model.")
@@ -4051,7 +4079,18 @@ def main(args):
                 img_kwargs["low_cpu_mem_usage"] = True
 
             try:
-                img_pipe = ImgCls.from_pretrained(img_info["id"], **img_kwargs)
+                img_pipe = ImgCls.from_pretrained(img_model_id_to_load, **img_kwargs)
+                
+                # Apply LoRA if image model is a LoRA adapter
+                if img_is_lora and img_lora_id:
+                    print(f"  Loading image LoRA adapter: {img_lora_id}")
+                    try:
+                        img_pipe.load_lora_weights(img_lora_id)
+                        print(f"  ✅ Image LoRA applied successfully")
+                    except Exception as lora_e:
+                        print(f"  ⚠️ Image LoRA loading failed: {lora_e}")
+                        print(f"     Continuing with base image model...")
+                
                 img_pipe.enable_model_cpu_offload()
 
                 img_prompt = ", ".join(args.prompt_image) if args.prompt_image else main_prompt