Instructions to use microsoft/vq-diffusion-ithq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use microsoft/vq-diffusion-ithq with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| # VQ Diffusion | |
| * [Paper](https://arxiv.org/abs/2205.16007.pdf) | |
| * [Original Repo](https://github.com/microsoft/VQ-Diffusion) | |
| * **Authors**: Shuyang Gu, Dong Chen, et al. | |
| ```python | |
| #!pip install diffusers[torch] transformers | |
| import torch | |
| from diffusers import VQDiffusionPipeline | |
| pipeline = VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq", torch_dtype=torch.float16) | |
| pipeline = pipeline.to("cuda") | |
| output = pipeline("teddy bear playing in the pool", truncation_rate=1.0) | |
| image = output.images[0] | |
| image.save("./teddy_bear.png") | |
| ``` | |
|  | |
| **Contribution**: This model was contribution by [williamberman](https://huggingface.co/williamberman) in [VQ-diffusion](https://github.com/huggingface/diffusers/pull/658). | |