Create app.py

app.py

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+from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
+from diffusers.utils import load_image
+from PIL import Image
+import torch
+import numpy as np
+import cv2
+
+controlnet_conditioning_scale = 0.5  # recommended for good generalization
+
+controlnet = ControlNetModel.from_pretrained(
+    "diffusers/controlnet-canny-sdxl-1.0",
+    torch_dtype=torch.float16
+)
+
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    controlnet=controlnet,
+    vae=vae,
+    torch_dtype=torch.float16,
+)
+pipe.enable_model_cpu_offload()
+
+low_threshold = 100
+high_threshold = 200
+
+def get_canny_filter(image):
+    
+    if not isinstance(image, np.ndarray):
+        image = np.array(image) 
+        
+    image = cv2.Canny(image, low_threshold, high_threshold)
+    image = image[:, :, None]
+    image = np.concatenate([image, image, image], axis=2)
+    canny_image = Image.fromarray(image)
+    return canny_image
+
+def process(input_image, prompt)
+    canny_image = get_canny_filter(input_image)
+    images = pipe(
+        prompt,image=image, controlnet_conditioning_scale=controlnet_conditioning_scale,
+        ).images
+
+    return [canny_image,images[0]]
+    
+block = gr.Blocks().queue()
+
+with block:
+    gr.Markdown("## ControlNet SDXL Canny")
+    gr.HTML('''
+     <p style="margin-bottom: 10px; font-size: 94%">
+                This is a demo for ControlNet SDXL, which is a neural network structure to control Stable Diffusion XL model by adding extra condition such as canny edge detection. 
+              </p>
+              ''')
+    gr.HTML("<p>You can duplicate this Space to run it privately without a queue and load additional checkpoints.  : <a style='display:inline-block' href='https://huggingface.co/spaces/RamAnanth1/controlnet-sdxl-canny?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14' alt='Duplicate Space'></a> </p>")
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(source='upload', type="numpy")
+            prompt = gr.Textbox(label="Prompt")
+            run_button = gr.Button(label="Run")
+            
+            
+        with gr.Column():
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
+    ips = [input_image, prompt]
+    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
+    # examples_list = [
+    #     #         [
+    #     #     "bird.png", 
+    #     #     "bird",
+    #     #     "Canny Edge Map"
+            
+    #     # ],
+        
+    #     #         [
+    #     #     "turtle.png", 
+    #     #     "turtle",
+    #     #     "Scribble",
+    #     #     "best quality, extremely detailed",
+    #     #     'longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality',
+    #     #      1, 
+    #     #     512,
+    #     #     20, 
+    #     #     9.0, 
+    #     #     123490213,
+    #     #     0.0,
+    #     #     100,
+    #     #     200
+            
+    #     # ],
+    #               [
+    #         "pose1.png", 
+    #        "Chef in the Kitchen",
+    #        "Pose",
+    #     #     "best quality, extremely detailed",
+    #     #     'longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality',
+    #     #      1, 
+    #     #     512,
+    #     #     20, 
+    #     #     9.0, 
+    #     #     123490213,
+    #     #     0.0,
+    #     #     100,
+    #     #     200
+            
+    #      ]
+    # ]
+    # examples = gr.Examples(examples=examples_list,inputs = [input_image, prompt], outputs = [result_gallery], cache_examples = True, fn = process)
+    gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=RamAnanth1.ControlNet)")  
+
+block.launch(debug = True)