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
vq-diffusion-ithq / learned_classifier_free_sampling_embeddings /diffusion_pytorch_model.fp16.safetensors
- Xet hash:
- fe5bbcb66738a0ff4bd2e612298bcf2665a9831aeb98ffd210051c9e113ff389
- Size of remote file:
- 79 kB
- SHA256:
- dc720c92892ffc66d8f4115028baa2269545b03982fc2a09d8a48f2af379b10b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.