customizable lora

app.py

@@ -21,7 +21,7 @@ hf_hub_download("XLabs-AI/flux-controlnet-canny", "controlnet.safetensors")
 print("downloaded!")
 
 @spaces.GPU(duration=240)
-def process_image(number_of_images, image, prompt, steps, use_lora, use_controlnet, use_depth, use_hed, use_ip, lora_weight, negative_image, neg_prompt, true_gs, guidance, cfg):
+def process_image(lora_path, lora_name, number_of_images, image, prompt, steps, use_lora, use_controlnet, use_depth, use_hed, use_ip, lora_weight, negative_image, neg_prompt, true_gs, guidance, cfg):
     from src.flux.xflux_pipeline import XFluxPipeline
     def run_xflux_pipeline(
         prompt, image, repo_id, name, device,
@@ -131,6 +131,8 @@ def process_image(number_of_images, image, prompt, steps, use_lora, use_controln
           num_steps=steps,
           num_images_per_prompt=number_of_images,
           use_lora=use_lora,
+          lora_repo_id=lora_path,
+          lora_name=lora_name,
           true_gs=true_gs,
           use_ip=use_ip,
           guidance=guidance
@@ -202,6 +204,8 @@ with gr.Blocks(css=custom_css) as demo:
                 use_depth = gr.Checkbox(label="Use depth")
                 use_hed = gr.Checkbox(label="Use hed")
                 use_lora = gr.Checkbox(label="Use LORA", value=True)
+                lora_path = gr.Checkbox(label="Lora Path", value="XLabs-AI/flux-lora-collection")
+                lora_name = gr.Checkbox(label="Lora Name", value="realism_lora.safetensors")
                 lora_weight = gr.Slider(step=0.1, minimum=0, maximum=1, value=0.7, label="Lora Weight")
                 
                 number_of_images = gr.Slider(step=1, minimum=0, maximum=4, value=2, label="Number of Images")
@@ -215,7 +219,7 @@ with gr.Blocks(css=custom_css) as demo:
         with gr.Column(scale=2, elem_classes="app"):
             output = gr.Gallery(label="Galery output", elem_classes="galery")
 
-    submit_btn.click(process_image, inputs=[number_of_images, input_image, prompt, steps, use_lora, controlnet, use_depth, use_hed, use_ip, lora_weight, negative_image, neg_prompt, true_gs, guidance, cfg], outputs=output)
+    submit_btn.click(process_image, inputs=[lora_path, lora_name, number_of_images, input_image, prompt, steps, use_lora, controlnet, use_depth, use_hed, use_ip, lora_weight, negative_image, neg_prompt, true_gs, guidance, cfg], outputs=output)
 
 if __name__ == '__main__':
     demo.launch(share=True, debug=True)