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from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL |
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from diffusers.utils import load_image |
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from PIL import Image |
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import torch |
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import numpy as np |
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import cv2 |
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import gradio as gr |
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controlnet_conditioning_scale = 0.5 |
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controlnet = ControlNetModel.from_pretrained( |
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"diffusers/controlnet-canny-sdxl-1.0", |
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torch_dtype=torch.float16 |
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) |
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) |
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pipe = StableDiffusionXLControlNetPipeline.from_pretrained( |
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"stabilityai/stable-diffusion-xl-base-1.0", |
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controlnet=controlnet, |
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vae=vae, |
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torch_dtype=torch.float16, |
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) |
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pipe.enable_model_cpu_offload() |
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low_threshold = 100 |
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high_threshold = 200 |
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def get_canny_filter(image): |
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if not isinstance(image, np.ndarray): |
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image = np.array(image) |
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image = cv2.Canny(image, low_threshold, high_threshold) |
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image = image[:, :, None] |
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image = np.concatenate([image, image, image], axis=2) |
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canny_image = Image.fromarray(image) |
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return canny_image |
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def process(input_image, prompt): |
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canny_image = get_canny_filter(input_image) |
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images = pipe( |
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prompt,image=canny_image, controlnet_conditioning_scale=controlnet_conditioning_scale, |
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).images |
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return [canny_image,images[0]] |
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block = gr.Blocks().queue() |
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with block: |
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gr.Markdown("## ControlNet SDXL Canny") |
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gr.HTML(''' |
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<p style="margin-bottom: 10px; font-size: 94%"> |
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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. |
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</p> |
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''') |
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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>") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(source='upload', type="numpy") |
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prompt = gr.Textbox(label="Prompt") |
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run_button = gr.Button(label="Run") |
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with gr.Column(): |
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result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid_cols=2, height='auto') |
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ips = [input_image, prompt] |
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run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) |
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gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=RamAnanth1.ControlNet)") |
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block.launch(debug = True) |