Instructions to use atsyplikhin/rita_sd_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use atsyplikhin/rita_sd_model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("atsyplikhin/rita_sd_model", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Download model_index.json from atsyplikhin/rita_sd_model: direct link, hf CLI and curl.
- Browser
- Download file 460 Bytes
-
https://huggingface.co/atsyplikhin/rita_sd_model/resolve/refs%2Fpr%2F2/model_index.json
- Command line
-
hf download hf://atsyplikhin/rita_sd_model@refs/pr/2/model_index.json
-
curl -L -o model_index.json https://huggingface.co/atsyplikhin/rita_sd_model/resolve/refs%2Fpr%2F2/model_index.json
460 Bytes
| { | |
| "_class_name": "StableDiffusionPipeline", | |
| "_diffusers_version": "0.7.0.dev0", | |
| "feature_extractor": [ | |
| "transformers", | |
| "CLIPImageProcessor" | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "DDIMScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |