Instructions to use optimum-intel-internal-testing/tiny-stable-diffusion-torch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use optimum-intel-internal-testing/tiny-stable-diffusion-torch with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("optimum-intel-internal-testing/tiny-stable-diffusion-torch", 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
- Local Apps
- Draw Things
- DiffusionBee
File size: 432 Bytes
f935f13 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"_class_name": "AutoencoderKL",
"_diffusers_version": "0.7.0.dev0",
"act_fn": "silu",
"block_out_channels": [
32,
64
],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D"
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 32,
"out_channels": 3,
"sample_size": 128,
"up_block_types": [
"UpDecoderBlock2D",
"UpDecoderBlock2D"
]
}
|