Instructions to use stabilityai/stable-diffusion-x4-upscaler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stabilityai/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("stabilityai/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 model_index.json from stabilityai/stable-diffusion-x4-upscaler: direct link, hf CLI and curl.
- Browser
- Download file 485 Bytes
-
https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/resolve/refs%2Fpr%2F21/model_index.json
- Command line
-
hf download hf://stabilityai/stable-diffusion-x4-upscaler@refs/pr/21/model_index.json
-
curl -L -o model_index.json https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/resolve/refs%2Fpr%2F21/model_index.json
485 Bytes
| { | |
| "_class_name": "StableDiffusionUpscalePipeline", | |
| "_diffusers_version": "0.9.0.dev0", | |
| "low_res_scheduler": [ | |
| "diffusers", | |
| "DDPMScheduler" | |
| ], | |
| "max_noise_level": 350, | |
| "scheduler": [ | |
| "diffusers", | |
| "DDIMScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |