Instructions to use sijunhe/tiny-random-stable-diffusion-pipe-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- paddlenlp
How to use sijunhe/tiny-random-stable-diffusion-pipe-1 with paddlenlp:
# ⚠️ Type of model unknown from paddlenlp.transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sijunhe/tiny-random-stable-diffusion-pipe-1", from_hf_hub=True) model = AutoModel.from_pretrained("sijunhe/tiny-random-stable-diffusion-pipe-1", from_hf_hub=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "_class_name": "StableDiffusionPipeline", | |
| "_ppdiffusers_version": "0.0.0", | |
| "feature_extractor": [ | |
| "paddlenlp.transformers", | |
| "CLIPFeatureExtractor" | |
| ], | |
| "requires_safety_checker": true, | |
| "safety_checker": [ | |
| "stable_diffusion", | |
| "StableDiffusionSafetyChecker" | |
| ], | |
| "scheduler": [ | |
| "ppdiffusers", | |
| "DDIMScheduler" | |
| ], | |
| "text_encoder": [ | |
| "paddlenlp.transformers", | |
| "CLIPTextModel" | |
| ], | |
| "tokenizer": [ | |
| "paddlenlp.transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "unet": [ | |
| "ppdiffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "vae": [ | |
| "ppdiffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |