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import os |
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import torch |
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import gradio as gr |
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import numpy as np |
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from PIL import Image |
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import matplotlib.pyplot as plt |
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from diffusers import DiffusionPipeline |
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from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation |
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from share_btn import community_icon_html, loading_icon_html, share_js |
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processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") |
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model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined") |
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pipe = DiffusionPipeline.from_pretrained( |
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"Fantasy-Studio/Paint-by-Example", |
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torch_dtype=torch.float16, |
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) |
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pipe = pipe.to("cuda") |
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def process_image(image, prompt): |
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inputs = processor(text=prompt, images=image, padding="max_length", return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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preds = outputs.logits |
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filename = "mask.png" |
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preds = torch.sigmoid(preds) |
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preds[preds >= 0.5] = 1 |
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preds[preds < 0.5] = 0 |
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plt.imsave(filename, preds) |
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return Image.open("mask.png").convert("RGB") |
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def read_content(file_path): |
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with open(file_path, "r", encoding="utf-8") as f: |
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content = f.read() |
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return content |
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def predict(input_image, text_query, reference, scale, seed, step): |
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width, height = input_image.size |
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if width < height: |
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factor = width / 512.0 |
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width = 512 |
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height = int((height / factor) / 8.0) * 8 |
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else: |
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factor = height / 512.0 |
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height = 512 |
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width = int((width / factor) / 8.0) * 8 |
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init_image = input_image.convert("RGB").resize((width, height)) |
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mask = process_image(input_image, text_query).resize((width, height)) |
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generator = torch.Generator("cuda").manual_seed(seed) if seed != 0 else None |
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output = pipe( |
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image=init_image, |
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mask_image=mask, |
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example_image=reference, |
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generator=generator, |
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guidance_scale=scale, |
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num_inference_steps=step, |
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).images[0] |
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return output, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) |
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css = ''' |
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.container {max-width: 1150px;margin: auto;padding-top: 1.5rem} |
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#image_upload{min-height:400px} |
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#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px} |
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#mask_radio .gr-form{background:transparent; border: none} |
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#word_mask{margin-top: .75em !important} |
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#word_mask textarea:disabled{opacity: 0.3} |
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.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5} |
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.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white} |
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.dark .footer {border-color: #303030} |
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.dark .footer>p {background: #0b0f19} |
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.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%} |
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#image_upload .touch-none{display: flex} |
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@keyframes spin { |
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from { |
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transform: rotate(0deg); |
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} |
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to { |
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transform: rotate(360deg); |
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} |
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} |
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#share-btn-container { |
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display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; |
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} |
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#share-btn { |
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all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important; |
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} |
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#share-btn * { |
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all: unset; |
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} |
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#share-btn-container div:nth-child(-n+2){ |
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width: auto !important; |
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min-height: 0px !important; |
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} |
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#share-btn-container .wrap { |
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display: none !important; |
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} |
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''' |
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example = {} |
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ref_dir = 'examples/reference' |
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image_dir = 'examples/image' |
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ref_list = [os.path.join(ref_dir, file) for file in os.listdir(ref_dir)] |
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ref_list.sort() |
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image_list = [os.path.join(image_dir, file) for file in os.listdir(image_dir)] |
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image_list.sort() |
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image_blocks = gr.Blocks(css=css) |
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with image_blocks as demo: |
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gr.HTML(read_content("header.html")) |
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with gr.Group(): |
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with gr.Box(): |
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with gr.Row(): |
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with gr.Column(): |
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image = gr.Image(source="upload", elem_id="image_upload", type="pil", label="Source Image") |
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text = gr.Textbox(lines=1, placeholder="Clothing item you want to replace...") |
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reference = gr.Image(source="upload", elem_id="image_upload", type="pil", label="Reference Image") |
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with gr.Column(): |
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image_out = gr.Image(label="Output", elem_id="output-img").style(height=400) |
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guidance = gr.Slider(label="Guidance scale", value=5, maximum=15,interactive=True) |
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steps = gr.Slider(label="Steps", value=50, minimum=2, maximum=75, step=1,interactive=True) |
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seed = gr.Slider(0, 10000, label='Seed (0 = random)', value=0, step=1) |
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with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True): |
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btn = gr.Button("Paint!").style( |
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margin=False, |
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rounded=(False, True, True, False), |
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full_width=True, |
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) |
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with gr.Group(elem_id="share-btn-container"): |
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community_icon = gr.HTML(community_icon_html, visible=True) |
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loading_icon = gr.HTML(loading_icon_html, visible=True) |
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share_button = gr.Button("Share to community", elem_id="share-btn", visible=True) |
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with gr.Row(): |
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with gr.Column(): |
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gr.Examples( |
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image_list, |
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inputs=[image], |
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label="Examples - Source Image", |
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examples_per_page=12 |
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) |
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with gr.Column(): |
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gr.Examples( |
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ref_list, |
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inputs=[reference], |
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label="Examples - Reference Image", |
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examples_per_page=12 |
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) |
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btn.click( |
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fn=predict, |
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inputs=[image, text, reference, guidance, seed, steps], |
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outputs=[image_out, community_icon, loading_icon, share_button] |
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) |
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share_button.click(None, [], [], _js=share_js) |
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gr.HTML( |
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""" |
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<div class="footer"> |
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<p>Gradio Demo by π€ Hugging Face |
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</p> |
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</div> |
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<div class="acknowledgments"> |
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<p><h4>LICENSE</h4> |
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The model is licensed with a <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" target="_blank" style="text-decoration: underline;" target="_blank">read the license</a></p> |
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""" |
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) |
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image_blocks.launch() |
