Instructions to use OzzyGT/idol_diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use OzzyGT/idol_diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OzzyGT/idol_diffusers", 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
Download NOTICE from OzzyGT/idol_diffusers: direct link, hf CLI and curl.
- Browser
- Download file 920 Bytes
-
https://huggingface.co/OzzyGT/idol_diffusers/resolve/main/NOTICE
- Command line
-
hf download hf://OzzyGT/idol_diffusers/NOTICE
-
curl -L -o NOTICE https://huggingface.co/OzzyGT/idol_diffusers/resolve/main/NOTICE
920 Bytes
| The weights in transformer/, octree_decoder/ and gaussian_decoder/ are converted from: | |
| Idol, Sperid Labs, Apache-2.0 (see LICENSE). | |
| https://huggingface.co/speridlabs/idol | |
| Reference revision: 2731c22 | |
| The conversion renames modules and splits fused attention projections; the values are unchanged. | |
| image_encoder/ is DINOv3 ViT-H+/16 from: | |
| facebook/dinov3-vith16plus-pretrain-lvd1689m, Meta, DINOv3 License (see LICENSE_DINOv3). | |
| https://huggingface.co/facebook/dinov3-vith16plus-pretrain-lvd1689m | |
| Reference revision: c807c9e | |
| It is stored in bfloat16, cast from the original float32 weights; it is otherwise unchanged. | |
| vae/ is the FLUX.2 VAE from: | |
| FLUX.2 [klein] 4B, Black Forest Labs, Apache-2.0 (see LICENSE). | |
| https://huggingface.co/black-forest-labs/FLUX.2-klein-4B | |
| Reference revision: e7b7dc2 | |
| Its files are copied unchanged. The original Idol loads the same VAE weights, in float32, from | |
| black-forest-labs/FLUX.2-dev. | |