Instructions to use hf-internal-testing/tiny-ShapEPipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/tiny-ShapEPipeline with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-ShapEPipeline", 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
| { | |
| "_class_name": "PriorTransformer", | |
| "_diffusers_version": "0.39.0.dev0", | |
| "added_emb_type": null, | |
| "additional_embeddings": 0, | |
| "attention_head_dim": 16, | |
| "clip_embed_dim": 32, | |
| "dropout": 0.0, | |
| "embedding_dim": 16, | |
| "embedding_proj_dim": 16, | |
| "embedding_proj_norm_type": null, | |
| "encoder_hid_proj_type": null, | |
| "norm_in_type": "layer", | |
| "num_attention_heads": 2, | |
| "num_embeddings": 32, | |
| "num_layers": 1, | |
| "time_embed_act_fn": "gelu", | |
| "time_embed_dim": 64 | |
| } | |