Lora testing

app.py

@@ -13,7 +13,8 @@ from lycoris import create_lycoris_from_weights
 # Define model options
 MODEL_OPTIONS = {
     "Wan2.1-T2V-1.3B": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-    "Wan2.1-T2V-14B": "Wan-AI/Wan2.1-T2V-14B-Diffusers"
+    "Wan2.1-T2V-14B": "Wan-AI/Wan2.1-T2V-14B-Diffusers",
+    "Wan2.1-Fun-Reward-1.3B": "alibaba-pai/Wan2.1-Fun-Reward-LoRAs"
 }
 
 # Define scheduler options
@@ -23,10 +24,6 @@ SCHEDULER_OPTIONS = {
 }
 
 def download_adapter(repo_id, weight_name=None):
-    """
-    Download the adapter file from the Hugging Face Hub.
-    If weight_name is not provided, attempts to use pytorch_lora_weights.safetensors
-    """
     adapter_filename = weight_name if weight_name else "pytorch_lora_weights.safetensors"
     cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
     cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
@@ -41,7 +38,6 @@ def download_adapter(repo_id, weight_name=None):
         )
         return path_to_adapter_file
     except Exception as e:
-        # If specific file not found, try to get a list of available safetensors files
         if weight_name is None:
             raise ValueError(f"Could not download default adapter file: {str(e)}\nPlease specify the exact weight file name.")
         else:
@@ -65,55 +61,41 @@ def generate_video(
     output_fps,
     seed
 ):
-    # Get model ID from selection
     model_id = MODEL_OPTIONS[model_choice]
     
-    # Set seed for reproducibility
     if seed == -1 or seed is None or seed == "":
         seed = random.randint(0, 2147483647)
     else:
         seed = int(seed)
     
-    # Set the seed
     torch.manual_seed(seed)
     
-    # Load model
     vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
     pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.float16)
     
-    # Set scheduler
     if scheduler_type == "UniPCMultistepScheduler":
-        pipe.scheduler = UniPCMultistepScheduler.from_config(
-            pipe.scheduler.config,
-            flow_shift=flow_shift
-        )
+        pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
     else:
         pipe.scheduler = FlowMatchEulerDiscreteScheduler(shift=flow_shift)
     
-    # Move to GPU
     pipe.to("cuda")
     
-    # Load LyCORIS weights if provided
     if lycoris_id and lycoris_id.strip():
         try:
-            # Download the adapter file
-            adapter_file_path = download_adapter(repo_id=lycoris_id, weight_name=lycoris_weight_name if lycoris_weight_name and lycoris_weight_name.strip() else None)
-            
-            # Apply LyCORIS adapter
+            adapter_file_path = download_adapter(
+                repo_id=lycoris_id,
+                weight_name=lycoris_weight_name if lycoris_weight_name and lycoris_weight_name.strip() else None
+            )
             wrapper, *_ = create_lycoris_from_weights(lycoris_scale, adapter_file_path, pipe.transformer)
             wrapper.merge_to()
-            
         except ValueError as e:
-            # Return informative error if there are issues loading the adapter
             if "more than one weights file" in str(e) or "Could not download default adapter file" in str(e):
-                return f"Error: The repository '{lycoris_id}' may contain multiple weight files. Please specify a weight name using the 'LyCORIS Weight Name' field.", seed
+                return f"Error: The repository '{lycoris_id}' may contain multiple weight files. Please specify a weight name.", seed
             else:
                 return f"Error loading LyCORIS weights: {str(e)}", seed
-    
-    # Enable CPU offload for low VRAM
+
     pipe.enable_model_cpu_offload()
     
-    # Generate video
     output = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
@@ -125,7 +107,6 @@ def generate_video(
         generator=torch.Generator("cuda").manual_seed(seed)
     ).frames[0]
     
-    # Export to video
     temp_file = "output.mp4"
     export_to_video(output, temp_file, fps=output_fps)
     
@@ -134,116 +115,103 @@ def generate_video(
 # Create the Gradio interface
 with gr.Blocks() as demo:
     
-    gr.Markdown("# Wan 2.1 T2V")
-    
+    gr.Markdown("# Wan 2.1 T2V with Custom LoRA")
+
     with gr.Row():
         with gr.Column(scale=1):
             model_choice = gr.Dropdown(
                 choices=list(MODEL_OPTIONS.keys()),
-                value="Wan2.1-T2V-1.3B",
+                value="Wan2.1-Fun-Reward-1.3B",
                 label="Model"
             )
             
-            prompt = gr.Textbox(
-                label="Prompt",
-                value="",
-                lines=3
-            )
-            
+            prompt = gr.Textbox(label="Prompt", value="", lines=3)
             negative_prompt = gr.Textbox(
                 label="Negative Prompt",
                 value="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体��灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿",
                 lines=3
             )
             
-            with gr.Row():
-                lycoris_id = gr.Textbox(
-                    label="Adapter Repo (e.g., markury/wan-st)",
-                    value="markury/wan-st"
-                )
+            lycoris_id = gr.Textbox(
+                label="Adapter Repo",
+                value="alibaba-pai/Wan2.1-Fun-Reward-LoRAs"
+            )
             
             with gr.Row():
                 lycoris_weight_name = gr.Textbox(
-                    label="Adapter Path in Repo",
-                    value="pytorch_lora_weights.safetensors",
-                    info="Specify for repos with multiple .safetensors files, e.g.: adapter_model.safetensors, pytorch_lora_weights.safetensors, etc."
+                    label="Adapter File Name",
+                    value="Wan2.1-Fun-1.3B-InP-HPS2.1.safetensors"
                 )
                 lycoris_scale = gr.Slider(
                     label="Adapter Scale",
                     minimum=0.0,
                     maximum=2.0,
-                    value=1.00,
+                    value=1.0,
                     step=0.05
                 )
             
-            with gr.Row():
-                scheduler_type = gr.Dropdown(
-                    choices=list(SCHEDULER_OPTIONS.keys()),
-                    value="UniPCMultistepScheduler",
-                    label="Scheduler"
-                )
-                flow_shift = gr.Slider(
-                    label="Flow Shift",
-                    minimum=1.0,
-                    maximum=12.0,
-                    value=3.0,
-                    step=0.5,
-                    info="2.0-5.0 for smaller videos, 7.0-12.0 for larger videos"
-                )
+            scheduler_type = gr.Dropdown(
+                choices=list(SCHEDULER_OPTIONS.keys()),
+                value="UniPCMultistepScheduler",
+                label="Scheduler"
+            )
+            flow_shift = gr.Slider(
+                label="Flow Shift",
+                minimum=1.0,
+                maximum=12.0,
+                value=3.0,
+                step=0.5
+            )
             
-            with gr.Row():
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=1024,
-                    value=832,
-                    step=32
-                )
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=1792,
-                    value=480,
-                    step=30
-                )
+            height = gr.Slider(
+                label="Height",
+                minimum=256,
+                maximum=1024,
+                value=832,
+                step=32
+            )
+            width = gr.Slider(
+                label="Width",
+                minimum=256,
+                maximum=1792,
+                value=480,
+                step=30
+            )
             
-            with gr.Row():
-                num_frames = gr.Slider(
-                    label="Number of Frames (4k+1 is recommended, e.g. 33)",
-                    minimum=17,
-                    maximum=129,
-                    value=33,
-                    step=4
-                )
-                output_fps = gr.Slider(
-                    label="Output FPS",
-                    minimum=8,
-                    maximum=30,
-                    value=16,
-                    step=1
-                )
+            num_frames = gr.Slider(
+                label="Number of Frames",
+                minimum=17,
+                maximum=129,
+                value=33,
+                step=4
+            )
+            output_fps = gr.Slider(
+                label="Output FPS",
+                minimum=8,
+                maximum=30,
+                value=16,
+                step=1
+            )
             
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance Scale (CFG)",
-                    minimum=1.0,
-                    maximum=15.0,
-                    value=4.0,
-                    step=0.5
-                )
-                num_inference_steps = gr.Slider(
-                    label="Inference Steps",
-                    minimum=10,
-                    maximum=100,
-                    value=20,
-                    step=1
-                )
+            guidance_scale = gr.Slider(
+                label="Guidance Scale (CFG)",
+                minimum=1.0,
+                maximum=15.0,
+                value=4.0,
+                step=0.5
+            )
+            num_inference_steps = gr.Slider(
+                label="Inference Steps",
+                minimum=10,
+                maximum=100,
+                value=20,
+                step=1
+            )
             
             seed = gr.Number(
                 label="Seed (-1 for random)",
                 value=-1,
-                precision=0,
-                info="Set a specific seed for deterministic results"
+                precision=0
             )
             
             generate_btn = gr.Button("Generate Video")
@@ -276,11 +244,10 @@ with gr.Blocks() as demo:
     
     gr.Markdown("""
     ## Tips for best results:
-    - For smaller resolution videos, try lower values of flow shift (2.0-5.0)
-    - For larger resolution videos, try higher values of flow shift (7.0-12.0)
-    - Number of frames should be of the form 4k+1 (e.g., 33, 81)
-    - Stick to lower frame counts. Even at 480p, an 81 frame sequence at 30 steps will nearly time out the request in this ZeroGPU space.
-    
+    - Smaller videos: Flow shift 2.0–5.0
+    - Larger videos: Flow shift 7.0–12.0
+    - Use frame count in 4k+1 form (e.g., 33, 65)
+    - Limit frame count and resolution to avoid timeout
     """)
 
-demo.launch()
+demo.launch()