Update app.py

app.py

@@ -5,11 +5,15 @@ import torch
 from diffusers import FluxPipeline
 
 pipeline = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.float16).to("cuda")
-pipeline.load_lora_weights("pepper13/fluxfw")
 #pipeline.enable_model_cpu_offload()
 
 @spaces.GPU(duration=70)
-def generate(prompt, negative_prompt, width, height, sample_steps):
+def generate(prompt, negative_prompt, width, height, sample_steps, lora_id):
+    try:
+        pipeline.load_lora_weights(lora_id)
+    except Exception as e:
+        return f"An error occured while loading the adapter: {e}."
+    
     return pipeline(prompt=f"{prompt}\nDO NOT INCLUDE {negative_prompt}", width=width, height=height, num_inference_steps=sample_steps, generator=torch.Generator("cpu").manual_seed(42), guidance_scale=7).images[0]
 
 with gr.Blocks() as interface:
@@ -29,8 +33,9 @@ with gr.Blocks() as interface:
                             height = gr.Slider(label="Height", info="The height in pixels of the generated image.", value=512, minimum=128, maximum=4096, step=64, interactive=True)
                         with gr.Column():
                             sampling_steps = gr.Slider(label="Sampling Steps", info="The number of denoising steps.", value=20, minimum=4, maximum=50, step=1, interactive=True)
+                            lora_id = gr.Textbox(label="Adapter Repository", info="ID of the FLUX LoRA", value="pepper13/fluxfw")
         
-        generate_button.click(fn=generate, inputs=[prompt, negative_prompt, width, height, sampling_steps], outputs=[output])
+        generate_button.click(fn=generate, inputs=[prompt, negative_prompt, width, height, sampling_steps, lora_id], outputs=[output])
 
 if __name__ == "__main__":
     interface.launch()