Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import base64
+import io
+import cv2
+import numpy as np
+import torch
+from controlnet_aux import HEDdetector
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
+
+def predict(sketch, description):
+    # Convert sketch to PIL image
+    sketch_pil = Image.fromarray(sketch)
+
+    hed = HEDdetector.from_pretrained('lllyasviel/Annotators')
+
+    image = hed(sketch_pil, scribble=True)
+
+    model_id = "runwayml/stable-diffusion-v1-5"
+    controlnet_id = "lllyasviel/sd-controlnet-scribble"
+
+    # Load ControlNet model
+    controlnet = ControlNetModel.from_pretrained(controlnet_id, torch_dtype=torch.float16)
+
+    # Create pipeline with ControlNet model
+    pipe = StableDiffusionControlNetPipeline.from_pretrained(model_id, controlnet=controlnet, torch_dtype=torch.float16)
+
+    # Use improved scheduler
+    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+    # Enable smart CPU offloading and memory efficient attention
+    # pipe.enable_model_cpu_offload()
+    pipe.enable_xformers_memory_efficient_attention()
+
+
+    result = pipe(description, image, num_inference_steps=20).images[0]
+    
+    return result
+# Define sketchpad with custom size and stroke width
+sketchpad = gr.Sketchpad(shape=(1024, 1024), brush_radius=5)
+
+iface = gr.Interface(fn=predict, inputs=[sketchpad, "text"], outputs="image", live=False)
+iface.launch(share=True)