Update app.py

app.py

@@ -34,7 +34,7 @@ pipe.to("cuda")
 
 #pipe.enable_model_cpu_offload()
 
-def infer(use_custom_model, model_name, custom_lora_weight, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, steps, seed, progress=gr.Progress(track_tqdm=True)):
+def infer(use_custom_model, model_name, custom_lora_weight, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, inf_steps, seed, progress=gr.Progress(track_tqdm=True)):
     prompt = prompt
     negative_prompt = negative_prompt
     generator = torch.Generator(device="cuda").manual_seed(seed)
@@ -61,9 +61,9 @@ def infer(use_custom_model, model_name, custom_lora_weight, image_in, prompt, ne
             prompt, 
             negative_prompt=negative_prompt, 
             image=image, 
-            controlnet_conditioning_scale=controlnet_conditioning_scale,
-            guidance_scale = guidance_scale,
-            num_inference_steps=steps,
+            controlnet_conditioning_scale=float(controlnet_conditioning_scale),
+            guidance_scale = float(guidance_scale),
+            num_inference_steps=inf_steps,
             generator=generator,
             cross_attention_kwargs={"scale": lora_scale}
         ).images
@@ -72,9 +72,9 @@ def infer(use_custom_model, model_name, custom_lora_weight, image_in, prompt, ne
             prompt, 
             negative_prompt=negative_prompt, 
             image=image, 
-            controlnet_conditioning_scale=controlnet_conditioning_scale,
-            guidance_scale = guidance_scale,
-            num_inference_steps=steps,
+            controlnet_conditioning_scale=float(controlnet_conditioning_scale),
+            guidance_scale = float(guidance_scale),
+            num_inference_steps=inf_steps,
             generator=generator,
         ).images
 
@@ -103,10 +103,10 @@ with gr.Blocks(css=css) as demo:
                 prompt = gr.Textbox(label="Prompt")
                 negative_prompt = gr.Textbox(label="Negative prompt", value="extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured")
                 guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=7.5)
-                steps = gr.Slider(label="Inference Steps", minimum="25", maximum="50", step=1, value=25)
+                inf_steps = gr.Slider(label="Inference Steps", minimum="25", maximum="50", step=1, value=25)
             with gr.Column():
                 preprocessor = gr.Dropdown(label="Preprocessor", choices=["canny"], value="canny", interactive=False, info="For the moment, only canny is available")  
-                controlnet_conditioning_scale = gr.Slider(label="Controlnet conditioning Scale", minimum=0.1, maximum=0.9, step=0.01, value=0.5, type="float")
+                controlnet_conditioning_scale = gr.Slider(label="Controlnet conditioning Scale", minimum=0.1, maximum=0.9, step=0.01, value=0.5)
                 seed = gr.Slider(label="seed", minimum=0, maximum=500000, step=1, value=42)
         use_custom_model = gr.Checkbox(label="Use a public custom model ?(optional)", value=False, info="To use a private model, you'll prefer to duplicate the space with your own access token.")
         with gr.Row():
@@ -117,7 +117,7 @@ with gr.Blocks(css=css) as demo:
 
     submit_btn.click(
         fn = infer,
-        inputs = [use_custom_model, model_name, custom_lora_weight, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, steps, seed],
+        inputs = [use_custom_model, model_name, custom_lora_weight, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, inf_steps, seed],
         outputs = [result]
     )