Instructions to use NatureUniverse/playground-v2-training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NatureUniverse/playground-v2-training with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, CLIPTextModelWithProjection tokenizer = AutoTokenizer.from_pretrained("NatureUniverse/playground-v2-training") model = CLIPTextModelWithProjection.from_pretrained("NatureUniverse/playground-v2-training", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_index.json from NatureUniverse/playground-v2-training: direct link, hf CLI and curl.
- Browser
- Download file 680 Bytes
-
https://huggingface.co/NatureUniverse/playground-v2-training/resolve/refs%2Fpr%2F1/model_index.json
- Command line
-
hf download hf://NatureUniverse/playground-v2-training@refs/pr/1/model_index.json
-
curl -L -o model_index.json https://huggingface.co/NatureUniverse/playground-v2-training/resolve/refs%2Fpr%2F1/model_index.json
680 Bytes
| { | |
| "_class_name": "StableDiffusionXLPipeline", | |
| "_diffusers_version": "0.24.0", | |
| "feature_extractor": [ | |
| null, | |
| null | |
| ], | |
| "force_zeros_for_empty_prompt": true, | |
| "image_encoder": [ | |
| null, | |
| null | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "EulerAncestralDiscreteScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "text_encoder_2": [ | |
| "transformers", | |
| "CLIPTextModelWithProjection" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "tokenizer_2": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |