Update app.py

app.py

@@ -14,7 +14,7 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 model_id = "Wan-AI/Wan2.2-T2V-A14B-Diffusers"
 vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
 pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
-flow_shift = 1.0
+flow_shift = 2.0
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
 pipe.to(device)
 
@@ -108,7 +108,7 @@ else:
 print("Initialization complete. Gradio is starting...")
 
 @spaces.GPU()
-def generate(prompt, negative_prompt, width, height, num_inference_steps, optional_lora_id, progress=gr.Progress(track_tqdm=True)):
+def generate(prompt, negative_prompt, width, height, num_inference_steps, optional_lora_id, guidance_scale= 3.5, guidance_scale_2= 4,progress=gr.Progress(track_tqdm=True)):
     
     # --- Step 1: ALWAYS build the full weight vector from scratch for THIS run ---
     
@@ -116,7 +116,7 @@ def generate(prompt, negative_prompt, width, height, num_inference_steps, option
     adapter_weights = [0.0] * len(ALL_ADAPTER_NAMES)
     try:
         base_lora_index = ALL_ADAPTER_NAMES.index(CAUSVID_NAME)
-        adapter_weights[base_lora_index] = 1.0
+        adapter_weights[base_lora_index] = 0.0
     except ValueError:
         pass # Base lora was not loaded, so its weight remains 0.
 
@@ -145,7 +145,8 @@ def generate(prompt, negative_prompt, width, height, num_inference_steps, option
         width=width,
         num_frames=1,
         num_inference_steps=num_inference_steps,
-        guidance_scale=1.0, 
+        guidance_scale=float(guidance_scale),
+        guidance_scale_2=float(guidance_scale_2),
     )
     image = output.frames[0][0]
     image = (image * 255).astype(np.uint8)
@@ -165,7 +166,9 @@ iface = gr.Interface(
         gr.Slider(minimum=1, maximum=80, step=1, label="Inference Steps", value=10),
         gr.Textbox(
             label="Optional LoRA",
-        )
+        ),
+        gr.Slider(minimum=1, maximum=20, step=0.5, label="Guidance Scale", value=3.5),
+        gr.Slider(minimum=1, maximum=20, step=0.5, label="Guidance Scale_2", value=4),
     ],
     outputs=gr.Image(label="output"),
 )