Instructions to use timbrooks/instruct-pix2pix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use timbrooks/instruct-pix2pix 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("timbrooks/instruct-pix2pix", 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 History
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Fix deprecated float16/fp16 variant loading through new `version` API.
Hey timbrooks 👋, Your model repository seems to contain a [`fp16` branch](https://huggingface.co/timbrooks/instruct-pix2pix/tree/fp16) to load the model in float16 precision. Loading `fp16` versions from a branch instead of the main branch is deprecated and will eventually be forbidden. Instead, we strongly recommend to save `fp16` versions of the model under `.fp16.` version files directly on the 'main' branch as enabled through this PR.This PR makes sure that your model repository allows the user to correctly download float16 precision model weights by adding `fp16` model weights in both safetensors and PyTorch bin format: ```py pipe = DiffusionPipeline.from_pretrained(timbrooks/instruct-pix2pix, torch_dtype=torch.float16, variant='fp16') ``` For more information please have a look at: https://huggingface.co/docs/diffusers/using-diffusers/loading#checkpoint-variants. We made sure you that you can safely merge this pull request. Best, the 🧨 Diffusers team.365333d
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Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (ccf2f1ccf5a0af00fdf689c804470ac09043904f)c0d6477 -
add model
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