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--- |
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tags: |
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- text-to-image |
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- stable-diffusion |
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--- |
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Stable Diffusion model, fine-tuned for generating images of people with their thumbs up. |
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How to use it: |
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```py |
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from diffusers import StableDiffusionPipeline |
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import torch |
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from torchmetrics.functional.multimodal import clip_score |
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from functools import partial |
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model_ckpt = "raghav-gaggar/stable-diffusion-thumbs-up" |
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sd_pipeline = StableDiffusionPipeline.from_pretrained(model_ckpt, torch_dtype=torch.float16).to("cuda") |
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prompts = [ |
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"thumbs up", |
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"thumbs up", |
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"thumbs up", |
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"thumbs up", |
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"thumbs up", |
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"thumbs up", |
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"thumbs up", |
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"thumbs up", |
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"thumbs up", |
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"thumbs up", |
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] |
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images = sd_pipeline(prompts, num_images_per_prompt=1, output_type="numpy").images |
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print(images.shape) |
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clip_score_fn = partial(clip_score, model_name_or_path="openai/clip-vit-base-patch16") |
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def calculate_clip_score(images, prompts): |
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images_int = (images * 255).astype("uint8") |
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clip_score = clip_score_fn(torch.from_numpy(images_int).permute(0, 3, 1, 2), prompts).detach() |
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return round(float(clip_score), 4) |
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sd_clip_score = calculate_clip_score(images, prompts) |
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print(f"CLIP score: {sd_clip_score}") |
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``` |
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Sample pictures of this concept: |
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