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
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thumbnail:
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datasets:
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- ChristophSchuhmann/improved_aesthetics_6plus
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license: creativeml-openrail-m
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tags:
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- stable-diffusion
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- stable-diffusion-diffusers
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- image-to-image
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# Stable Diffusion Image Variations Model Card
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thumbnail: >-
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https://repository-images.githubusercontent.com/523487884/fdb03a69-8353-4387-b5fc-0d85f888a63f
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datasets:
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- ChristophSchuhmann/improved_aesthetics_6plus
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- gsdf/EasyNegative
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license: creativeml-openrail-m
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tags:
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- stable-diffusion
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- stable-diffusion-diffusers
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- image-to-image
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language:
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- en
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metrics:
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- character
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library_name: open_clip
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pipeline_tag: text-to-image
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# Stable Diffusion Image Variations Model Card
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