Update app.py

app.py

@@ -13,12 +13,10 @@ from diffusers import (
     EulerDiscreteScheduler,
 )
 
-# Initialize ControlNet model
 controlnet = ControlNetModel.from_pretrained(
     "DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16
 )
 
-# Initialize pipeline
 pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
     "XpucT/Deliberate",
     controlnet=controlnet,
@@ -27,13 +25,11 @@ pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
 ).to("cuda")
 pipe.enable_xformers_memory_efficient_attention()
 
-# Sampler configurations
 SAMPLER_MAP = {
     "DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
     "Euler": lambda config: EulerDiscreteScheduler.from_config(config),
 }
 
-# Inference function
 def inference(
     input_image: Image.Image,
     prompt: str,
@@ -47,6 +43,8 @@ def inference(
     if prompt is None or prompt == "":
         raise gr.Error("Prompt is required")
     
+    input_image = input_image.resize((512, 512))
+    
     pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config)
     generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
     
@@ -54,18 +52,15 @@ def inference(
         prompt=prompt,
         negative_prompt=negative_prompt,
         image=input_image,
-        control_image=input_image,  # type: ignore
-        width=512,  # type: ignore
-        height=512,  # type: ignore
+        control_image=input_image,
         guidance_scale=float(guidance_scale),
-        controlnet_conditioning_scale=float(controlnet_conditioning_scale),  # type: ignore
+        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
         generator=generator,
         strength=float(strength),
         num_inference_steps=40,
     )
-    return out.images[0]  # type: ignore
+    return out.images[0]
 
-# Gradio UI
 with gr.Blocks() as app:
     gr.Markdown(
         '''
@@ -78,7 +73,7 @@ with gr.Blocks() as app:
     with gr.Row():
         with gr.Column():
             input_image = gr.Image(label="Input Illusion", type="pil")
-            prompt = gr.Textbox(label="Prompt", info="Prompt that guides the generation towards")
+            prompt = gr.Textbox(label="Prompt")
             negative_prompt = gr.Textbox(label="Negative Prompt", value="ugly, disfigured, low quality, blurry, nsfw")
             with gr.Accordion(label="Advanced Options", open=False):
                 controlnet_conditioning_scale = gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=1.1, label="Controlnet Conditioning Scale")
@@ -99,4 +94,4 @@ with gr.Blocks() as app:
 app.queue(concurrency_count=4, max_size=20)
 
 if __name__ == "__main__":
-    app.launch(debug=True)
+    app.launch(debug=True)