Update app.py

app.py

@@ -28,14 +28,15 @@ pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
 )
 pipe.to("cuda")
 
-custom_model = "fffiloni/eugene_decors_jour"
-
-# This is where you load your trained weights
-pipe.load_lora_weights(custom_model, weight_name="pytorch_lora_weights.safetensors", use_auth_token=True)
 
 #pipe.enable_model_cpu_offload()
 
-def infer(image_in, prompt, controlnet_conditioning_scale, guidance_scale):
+def infer(model_name, image_in, prompt, controlnet_conditioning_scale, guidance_scale, seed):
+    custom_model = model_name
+
+    # This is where you load your trained weights
+    pipe.load_lora_weights(custom_model, weight_name="pytorch_lora_weights.safetensors", use_auth_token=True)
+    
     prompt = prompt
     negative_prompt = ""
 
@@ -58,6 +59,7 @@ def infer(image_in, prompt, controlnet_conditioning_scale, guidance_scale):
         controlnet_conditioning_scale=controlnet_conditioning_scale,
         guidance_scale = guidance_scale,
         num_inference_steps=50,
+        seed=seed,
         cross_attention_kwargs={"scale": lora_scale}
         ).images
 
@@ -67,16 +69,19 @@ def infer(image_in, prompt, controlnet_conditioning_scale, guidance_scale):
 
 with gr.Blocks() as demo:
     with gr.Column():
+        model_name = gr.Textbox(label="Model to use", placeholder="username/my_model"
         image_in = gr.Image(source="upload", type="filepath")
         prompt = gr.Textbox(label="Prompt")
         guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=7.5, type="float")
         controlnet_conditioning_scale = gr.Slider(label="Controlnet conditioning Scale", minimum=0.1, maximum=0.9, step=0.01, value=0.5, type="float")
+        seed = gr.Slider(label="seed", minimum=0, maximum=500000, step=1, value=42)
+
         submit_btn = gr.Button("Submit")
         result = gr.Image(label="Result")
 
     submit_btn.click(
         fn = infer,
-        inputs = [image_in, prompt, controlnet_conditioning_scale, guidance_scale ],
+        inputs = [model_name, image_in, prompt, controlnet_conditioning_scale, guidance_scale, seed],
         outputs = [result]
     )