Instructions to use CompVis/ldm-super-resolution-4x-openimages with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CompVis/ldm-super-resolution-4x-openimages with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CompVis/ldm-super-resolution-4x-openimages", 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
Download vqvae/diffusion_pytorch_model.fp16.bin from CompVis/ldm-super-resolution-4x-openimages: direct link, hf CLI and curl.
- Browser
- Download file 111 MB
-
https://huggingface.co/CompVis/ldm-super-resolution-4x-openimages/resolve/main/vqvae/diffusion_pytorch_model.fp16.bin
- Command line
-
hf download hf://CompVis/ldm-super-resolution-4x-openimages/vqvae/diffusion_pytorch_model.fp16.bin
-
curl -L -o diffusion_pytorch_model.fp16.bin https://huggingface.co/CompVis/ldm-super-resolution-4x-openimages/resolve/main/vqvae/diffusion_pytorch_model.fp16.bin
111 MB
- Xet hash:
- 97d8cdd1dfe8e667d099e814be71a277b74ad2d3b406cc55c77f071e98b2e934
- Size of remote file:
- 111 MB
- SHA256:
- 800b11b7c0a3f1f447bdf76dc787d9021ab4858325fadf574e0228b2c2736527
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