Alternative loading lora method

app.py

@@ -7,6 +7,7 @@ import gradio as gr
 import tempfile
 import re
 import os
+import traceback
 
 from huggingface_hub import hf_hub_download
 import numpy as np
@@ -18,8 +19,7 @@ MODEL_ID = "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers"
 
 # Merged FusionX enhancement LoRA
 LORA_REPO_ID = "vrgamedevgirl84/Wan14BT2VFusioniX"
-LORA_FILENAME = "Wan2.1_I2V_14B_FusionX_LoRA.safetensors"
-LORA_SUBFOLDER = "FusionX_LoRa"
+LORA_FILENAME = "FusionX_LoRa/Wan2.1_I2V_14B_FusionX_LoRA.safetensors"
 
 # Load enhanced model components
 print("🚀 Loading FusionX Enhanced Wan2.1 I2V Model...")
@@ -33,34 +33,36 @@ pipe = WanImageToVideoPipeline.from_pretrained(
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=8.0)
 pipe.to("cuda")
 
-# Load the single merged FusionX LoRA
+# Load and fuse the single merged FusionX LoRA
 try:
     lora_path = hf_hub_download(
         repo_id=LORA_REPO_ID, 
-        filename=LORA_FILENAME, 
-        subfolder=LORA_SUBFOLDER
+        filename=LORA_FILENAME
     )
-    # Load the LoRA weights into the transformer model component
-    pipe.load_lora_weights(lora_path, adapter_name="fusionx")
-    # Set the adapter to be active
-    pipe.set_adapters(["fusionx"])
-    print("✅ FusionX LoRA loaded and activated. Use the slider to control its strength.")
-except Exception as e:
-    print(f"⚠️ FusionX LoRA not loaded: {e}")
+    print("✅ LoRA downloaded to:", lora_path)
+    
+    # Load, set weight, and fuse the LoRA into the pipeline
+    pipe.load_lora_weights(lora_path, adapter_name="fusionx_lora")
+    pipe.set_adapters(["fusionx_lora"], adapter_weights=[0.75])
+    pipe.fuse_lora()
+    print("✅ FusionX LoRA loaded and fused with a weight of 0.75.")
 
+except Exception as e:
+    print("❌ Error during LoRA loading:")
+    traceback.print_exc()
 
 MOD_VALUE = 32
-DEFAULT_H_SLIDER_VALUE = 576  # FusionX optimized default
-DEFAULT_W_SLIDER_VALUE = 1024  # FusionX optimized default  
-NEW_FORMULA_MAX_AREA = 576.0 * 1024.0  # Updated for FusionX
+DEFAULT_H_SLIDER_VALUE = 640
+DEFAULT_W_SLIDER_VALUE = 1024
+NEW_FORMULA_MAX_AREA = 640.0 * 1024.0
 
-SLIDER_MIN_H, SLIDER_MAX_H = 128, 1080
-SLIDER_MIN_W, SLIDER_MAX_W = 128, 1920
+SLIDER_MIN_H, SLIDER_MAX_H = 128, 1024
+SLIDER_MIN_W, SLIDER_MAX_W = 128, 1024
 MAX_SEED = np.iinfo(np.int32).max
 
 FIXED_FPS = 24
 MIN_FRAMES_MODEL = 8
-MAX_FRAMES_MODEL = 121  # FusionX supports up to 121 frames
+MAX_FRAMES_MODEL = 81
 
 # Enhanced prompts for FusionX-style output
 default_prompt_i2v = "Cinematic motion, smooth animation, detailed textures, dynamic lighting, professional cinematography"
@@ -319,7 +321,7 @@ def handle_image_upload_for_dims_wan(uploaded_pil_image, current_h_val, current_
 
 def get_duration(input_image, prompt, height, width, 
                    negative_prompt, duration_seconds,
-                   guidance_scale, steps, lora_scale,
+                   guidance_scale, steps,
                    seed, randomize_seed, 
                    progress):
     # FusionX optimized duration calculation
@@ -333,7 +335,7 @@ def get_duration(input_image, prompt, height, width,
 @spaces.GPU(duration=get_duration)
 def generate_video(input_image, prompt, height, width, 
                    negative_prompt=default_negative_prompt, duration_seconds=3,
-                   guidance_scale=1, steps=8, lora_scale=1.0,
+                   guidance_scale=1, steps=8,
                    seed=42, randomize_seed=False, 
                    progress=gr.Progress(track_tqdm=True)):
     
@@ -362,8 +364,7 @@ def generate_video(input_image, prompt, height, width,
             num_frames=num_frames,
             guidance_scale=float(guidance_scale), 
             num_inference_steps=int(steps),
-            generator=torch.Generator(device="cuda").manual_seed(current_seed),
-            cross_attention_kwargs={"scale": float(lora_scale)}
+            generator=torch.Generator(device="cuda").manual_seed(current_seed)
         ).frames[0]
 
     # Create a unique filename for download
@@ -439,14 +440,6 @@ with gr.Blocks() as demo:
                             value=DEFAULT_W_SLIDER_VALUE, 
                             label=f"📐 Output Width (FusionX optimized: {MOD_VALUE} multiples)"
                         )
-                    lora_scale_slider = gr.Slider(
-                        minimum=0.0,
-                        maximum=2.5,
-                        step=0.05,
-                        value=1.0,
-                        label="💪 FusionX LoRA Strength",
-                        info="Control the intensity of the FusionX effect. >1.0 for stronger effect, <1.0 for less."
-                    )
                     steps_slider = gr.Slider(
                         minimum=1, 
                         maximum=20, 
@@ -493,7 +486,7 @@ with gr.Blocks() as demo:
         ui_inputs = [
             input_image_component, prompt_input, height_input, width_input,
             negative_prompt_input, duration_seconds_input,
-            guidance_scale_input, steps_slider, lora_scale_slider, seed_input, randomize_seed_checkbox
+            guidance_scale_input, steps_slider, seed_input, randomize_seed_checkbox
         ]
         generate_button.click(fn=generate_video, inputs=ui_inputs, outputs=[video_output, seed_input, download_output])