Instructions to use optimum-intel-internal-testing/tiny-random-flux with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use optimum-intel-internal-testing/tiny-random-flux 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-random-flux", 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: 586 Bytes
cb92278 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"_class_name": "AutoencoderKL",
"_diffusers_version": "0.32.1",
"act_fn": "silu",
"block_out_channels": [
4
],
"down_block_types": [
"DownEncoderBlock2D"
],
"force_upcast": true,
"in_channels": 3,
"latent_channels": 1,
"latents_mean": null,
"latents_std": null,
"layers_per_block": 1,
"mid_block_add_attention": true,
"norm_num_groups": 1,
"out_channels": 3,
"sample_size": 32,
"scaling_factor": 1.5035,
"shift_factor": 0.0609,
"up_block_types": [
"UpDecoderBlock2D"
],
"use_post_quant_conv": false,
"use_quant_conv": false
}
|