Image-to-Image
Diffusers
StableDiffusionInstructPix2PixPipeline
stable-diffusion
stable-diffusion-diffusers
art
Instructions to use instruction-tuning-sd/scratch-cartoonizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use instruction-tuning-sd/scratch-cartoonizer 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("instruction-tuning-sd/scratch-cartoonizer", 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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## Training procedure and results
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Training was conducted on [instruction-tuning-sd/cartoonization](https://huggingface.co/datasets/instruction-tuning-sd/cartoonization) dataset. Refer to
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[this repository](https://github.com/
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Here are some results dervied from the pipeline:
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## Training procedure and results
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Training was conducted on [instruction-tuning-sd/cartoonization](https://huggingface.co/datasets/instruction-tuning-sd/cartoonization) dataset. Refer to
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[this repository](https://github.com/huggingface/instruction-tuned-sd) to know more.
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Here are some results dervied from the pipeline:
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