Instructions to use hf-internal-testing/tiny-dit-pipe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/tiny-dit-pipe 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-dit-pipe", 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: 496 Bytes
9cb097e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"_class_name": "DiTTransformer2DModel",
"_diffusers_version": "0.29.0.dev0",
"activation_fn": "gelu-approximate",
"attention_bias": true,
"attention_head_dim": 8,
"dropout": 0.0,
"in_channels": 4,
"norm_elementwise_affine": false,
"norm_eps": 1e-05,
"norm_num_groups": 32,
"norm_type": "ada_norm_zero",
"num_attention_heads": 2,
"num_embeds_ada_norm": 1000,
"num_layers": 2,
"out_channels": 8,
"patch_size": 4,
"sample_size": 16,
"upcast_attention": false
}
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