revert

app.py

@@ -18,7 +18,7 @@ MAX_IMAGE_SIZE = 2048
 pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
 
 @spaces.GPU(duration=300)
-def infer(prompt, lora_model, lora_weight=1.0, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+def infer(prompt, lora_model, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
     global pipe
     
     # Load LoRA if specified
@@ -39,8 +39,7 @@ def infer(prompt, lora_model, lora_weight=1.0, seed=42, randomize_seed=False, wi
             height=height,
             num_inference_steps=num_inference_steps, 
             generator=generator,
-            guidance_scale=guidance_scale,
-            cross_attention_kwargs={"scale": lora_weight}
+            guidance_scale=guidance_scale
         ).images[0]
         
         # Unload LoRA weights after generation
@@ -128,13 +127,6 @@ with gr.Blocks(css=css) as demo:
                     step=1,
                     value=28,
                 )
-            lora_weight = gr.Slider(
-                label="LoRA Weight",
-                minimum=0,
-                maximum=2,
-                step=0.01,
-                value=1.0,
-            )
         
         gr.Examples(
             examples=examples,
@@ -147,8 +139,8 @@ with gr.Blocks(css=css) as demo:
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
-        inputs=[prompt, lora_model, lora_weight, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        inputs=[prompt, lora_model, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
         outputs=[result, seed, output_message]
     )
 
-demo.launch()
+demo.launch()