Instructions to use g8nz/stable-diffusion-x4-upscaler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use g8nz/stable-diffusion-x4-upscaler with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("g8nz/stable-diffusion-x4-upscaler", 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 vae/diffusion_pytorch_model.fp16.bin from g8nz/stable-diffusion-x4-upscaler: direct link, hf CLI and curl.
- Browser
- Download file 111 MB
-
https://huggingface.co/g8nz/stable-diffusion-x4-upscaler/resolve/main/vae/diffusion_pytorch_model.fp16.bin
- Command line
-
hf download hf://g8nz/stable-diffusion-x4-upscaler/vae/diffusion_pytorch_model.fp16.bin
-
curl -L -o diffusion_pytorch_model.fp16.bin https://huggingface.co/g8nz/stable-diffusion-x4-upscaler/resolve/main/vae/diffusion_pytorch_model.fp16.bin
111 MB
- Xet hash:
- 129e795064bdfc6e4cb3e9be7a54922c401f6163b8f42b82a185ab7aef523cc8
- Size of remote file:
- 111 MB
- SHA256:
- fac10c7b4287c0bffe5707905b28641f2093ce07f4a7a2cf7c037b485bf46ea3
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