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Parent(s):
4184646
init
Browse files- app.py +234 -0
- requirements.txt +14 -0
app.py
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import gradio as gr
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import torch
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from clip2latent import models
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from PIL import Image
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device = "cuda"
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model_choices = {
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"faces": {
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"checkpoint": "https://huggingface.co/lambdalabs/clip2latent/resolve/main/ffhq-sg2-510.ckpt",
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"config": "https://huggingface.co/lambdalabs/clip2latent/resolve/main/ffhq-sg2-510.yaml",
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},
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"landscape": {
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"checkpoint": "https://huggingface.co/lambdalabs/clip2latent/resolve/main/lhq-sg3-410.ckpt",
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"config": "https://huggingface.co/lambdalabs/clip2latent/resolve/main/lhq-sg3-410.yaml",
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}
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}
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model_cache = {}
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for k, v in model_choices.items():
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checkpoint = v["checkpoint"]
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cfg_file = v["config"]
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# Moving to the cpu seems to break the model, so just put all on the gpu
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model_cache[k] = models.Clip2StyleGAN(cfg_file, device, checkpoint)
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@torch.no_grad()
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def infer(prompt, model_select, n_samples, scale):
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model = model_cache[model_select]
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images, _ = model(prompt, n_samples_per_txt=n_samples, cond_scale=scale, skips=250, clip_sort=True)
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images = images.cpu()
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make_im = lambda x: (255*x.clamp(-1, 1)/2 + 127.5).to(torch.uint8).permute(1,2,0).numpy()
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images = [Image.fromarray(make_im(x)) for x in images]
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return images
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css = """
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a {
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color: inherit;
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text-decoration: underline;
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}
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.gradio-container {
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font-family: 'IBM Plex Sans', sans-serif;
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}
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.gr-button {
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color: white;
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border-color: #9d66e5;
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background: #9d66e5;
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}
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input[type='range'] {
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accent-color: #9d66e5;
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}
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.dark input[type='range'] {
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accent-color: #dfdfdf;
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}
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.container {
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max-width: 730px;
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margin: auto;
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padding-top: 1.5rem;
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}
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#gallery {
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min-height: 22rem;
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margin-bottom: 15px;
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margin-left: auto;
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margin-right: auto;
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border-bottom-right-radius: .5rem !important;
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border-bottom-left-radius: .5rem !important;
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}
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#gallery>div>.h-full {
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min-height: 20rem;
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}
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.details:hover {
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text-decoration: underline;
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}
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.gr-button {
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white-space: nowrap;
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}
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.gr-button:focus {
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border-color: rgb(147 197 253 / var(--tw-border-opacity));
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outline: none;
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box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
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--tw-border-opacity: 1;
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--tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
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--tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
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--tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
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--tw-ring-opacity: .5;
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}
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#advanced-options {
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margin-bottom: 20px;
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}
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.footer {
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margin-bottom: 45px;
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margin-top: 35px;
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text-align: center;
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border-bottom: 1px solid #e5e5e5;
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}
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.footer>p {
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font-size: .8rem;
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display: inline-block;
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padding: 0 10px;
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transform: translateY(10px);
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background: white;
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}
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.dark .logo{ filter: invert(1); }
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.dark .footer {
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border-color: #303030;
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}
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.dark .footer>p {
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background: #0b0f19;
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}
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.acknowledgments h4{
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margin: 1.25em 0 .25em 0;
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font-weight: bold;
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font-size: 115%;
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}
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"""
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examples = [
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[
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'a photograph of a happy person wearing sunglasses by the sea',
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'faces',
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2,
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2,
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],
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[
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'a photograph of Captain Jean Luc Picard',
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'faces',
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2,
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2,
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],
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[
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'a mountain in the middle of the sea',
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'landscape',
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2,
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2,
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],
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[
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'The sun setting over the sea',
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'landscape',
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2,
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2,
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],
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]
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def main():
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block = gr.Blocks(css=css)
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with block:
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gr.HTML(
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"""
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<div style="text-align: center; max-width: 650px; margin: 0 auto;">
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<div>
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<img class="logo" src="https://lambdalabs.com/static/images/lambda-logo.svg" alt="Lambda Logo"
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style="margin: auto; max-width: 7rem;">
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<h1 style="font-weight: 900; font-size: 3rem;">
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clip2latent
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</h1>
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</div>
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<p style="font-size: 94%">
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Official demo for <em>clip2latent: Text driven sampling of a pre-trained StyleGAN using denoising diffusion and CLIP</em>, accepted to BMVC 2022
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</p>
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<p style="margin-bottom: 10px; font-size: 94%">
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Get the <a href="https://github.com/justinpinkney/clip2latent">code on GitHub</a>, see the <a href="#">paper on Arxiv</a>.
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</p>
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</div>
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"""
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)
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with gr.Group():
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with gr.Box():
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with gr.Row().style(mobile_collapse=False, equal_height=True):
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text = gr.Textbox(
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label="Enter your prompt",
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show_label=False,
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max_lines=1,
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placeholder="Enter your prompt",
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).style(
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border=(True, False, True, True),
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rounded=(True, False, False, True),
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container=False,
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)
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btn = gr.Button("Generate image").style(
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margin=False,
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rounded=(False, True, True, False),
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)
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gallery = gr.Gallery(
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label="Generated images", show_label=False, elem_id="gallery"
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).style(grid=[2], height="auto")
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with gr.Row(elem_id="advanced-options"):
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model_select = gr.Dropdown(label="Model", choices=["faces", "landscape"], value="faces",)
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samples = gr.Slider(label="Images", minimum=1, maximum=4, value=2, step=1)
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scale = gr.Slider(
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label="Guidance Scale", minimum=0, maximum=10, value=2, step=0.5
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)
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ex = gr.Examples(examples=examples, fn=infer, inputs=[text, model_select, samples, scale], outputs=gallery, cache_examples=False)
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ex.dataset.headers = [""]
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text.submit(infer, inputs=[text, model_select, samples, scale], outputs=gallery)
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btn.click(infer, inputs=[text, model_select, samples, scale], outputs=gallery)
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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 Lambda Labs
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</p>
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</div>
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<div class="acknowledgments">
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<img src="https://raw.githubusercontent.com/justinpinkney/clip2latent/main/images/headline-large.jpeg"></img>
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<br>
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<h2 style="font-size:1.5em">clip2latent: Text driven sampling of a pre-trained StyleGAN using denoising diffusion and CLIP</h2>
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<p>Justin N. M. Pinkney and Chuan Li @ <a href="https://lambdalabs.com/">Lambda Inc.</a>
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<br>
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<br>
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<em>Abstract:</em>
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We introduce a new method to efficiently create text-to-image models from a pre-trained CLIP and StyleGAN.
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It enables text driven sampling with an existing generative model without any external data or fine-tuning.
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This is achieved by training a diffusion model conditioned on CLIP embeddings to sample latent vectors of a pre-trained StyleGAN, which we call <em>clip2latent</em>.
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We leverage the alignment between CLIP’s image and text embeddings to avoid the need for any text labelled data for training the conditional diffusion model.
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We demonstrate that clip2latent allows us to generate high-resolution (1024x1024 pixels) images based on text prompts with fast sampling, high image quality, and low training compute and data requirements.
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We also show that the use of the well studied StyleGAN architecture, without further fine-tuning, allows us to directly apply existing methods to control and modify the generated images adding a further layer of control to our text-to-image pipeline.
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</p>
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<br>
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<p>Trained using <a href="https://lambdalabs.com/service/gpu-cloud">Lambda GPU Cloud</a></p>
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</div>
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"""
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)
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block.queue()
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block.launch()
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if __name__ == "__main__":
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main()
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requirements.txt
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--extra-index-url https://download.pytorch.org/whl/cu113
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torch
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torchvision
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wandb==0.12.16
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ninja==1.10.2.3
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dalle2-pytorch==0.2.38
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hydra-core==1.1.2
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typer==0.4.1
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joblib==1.1.0
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webdataset==0.2.5
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gradio==3.4
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protobuf==3.20.1
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scipy==1.9.1
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git+https://github.com/justinpinkney/clip2latent.git
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