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
File size: 478 Bytes
18c86ae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"_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
}
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