Image-to-Image
Diffusers
StableDiffusionImageVariationPipeline
stable-diffusion
stable-diffusion-diffusers
Instructions to use lambda/sd-image-variations-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lambda/sd-image-variations-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lambda/sd-image-variations-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Commit ·
541c94a
1
Parent(s): a2a1398
update readme
Browse files
README.md
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@@ -49,7 +49,7 @@ tform = transforms.Compose([
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[0.48145466, 0.4578275, 0.40821073],
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[0.26862954, 0.26130258, 0.27577711]),
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])
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-
inp = tform(im).to(device)
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out = sd_pipe(inp, guidance_scale=3)
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out["images"][0].save("result.jpg")
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[0.48145466, 0.4578275, 0.40821073],
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[0.26862954, 0.26130258, 0.27577711]),
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])
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inp = tform(im).to(device).unsqueeze(0)
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out = sd_pipe(inp, guidance_scale=3)
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out["images"][0].save("result.jpg")
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