Instructions to use OzzyGT/idol_custom_blocks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use OzzyGT/idol_custom_blocks 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_custom_blocks", 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 components.py from OzzyGT/idol_custom_blocks: direct link, hf CLI and curl.
- Browser
- Download file 2.25 kB
-
https://huggingface.co/OzzyGT/idol_custom_blocks/resolve/main/components.py
- Command line
-
hf download hf://OzzyGT/idol_custom_blocks/components.py
-
curl -L -o components.py https://huggingface.co/OzzyGT/idol_custom_blocks/resolve/main/components.py
2.25 kB
| # Copyright 2026 Sperid Labs and The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from diffusers.configuration_utils import FrozenDict | |
| from diffusers.modular_pipelines.modular_pipeline_utils import ComponentSpec | |
| from diffusers.schedulers import FlowMatchEulerDiscreteScheduler | |
| from .gaussian_decoder_idol import IdolGaussianDecoderModel | |
| from .octree_decoder_idol import IdolOctreeDecoderModel | |
| from .transformer_idol import IdolTransformerModel | |
| # The converted weights. The model classes come from this repo, so the weights repo holds no code and loads without | |
| # `trust_remote_code`. Load from elsewhere with | |
| # `pipe.load_components(pretrained_model_name_or_path={"transformer": ..., "octree_decoder": ..., ...})`. | |
| IDOL_WEIGHTS_REPO = "OzzyGT/idol_diffusers" | |
| def transformer_spec() -> ComponentSpec: | |
| return ComponentSpec( | |
| "transformer", IdolTransformerModel, pretrained_model_name_or_path=IDOL_WEIGHTS_REPO, subfolder="transformer" | |
| ) | |
| def octree_decoder_spec() -> ComponentSpec: | |
| return ComponentSpec( | |
| "octree_decoder", | |
| IdolOctreeDecoderModel, | |
| pretrained_model_name_or_path=IDOL_WEIGHTS_REPO, | |
| subfolder="octree_decoder", | |
| ) | |
| def gaussian_decoder_spec() -> ComponentSpec: | |
| return ComponentSpec( | |
| "gaussian_decoder", | |
| IdolGaussianDecoderModel, | |
| pretrained_model_name_or_path=IDOL_WEIGHTS_REPO, | |
| subfolder="gaussian_decoder", | |
| ) | |
| def scheduler_spec() -> ComponentSpec: | |
| # shift 3.0 is the reference's time shift (`rescale_t`) | |
| return ComponentSpec( | |
| "scheduler", | |
| FlowMatchEulerDiscreteScheduler, | |
| config=FrozenDict({"shift": 3.0}), | |
| default_creation_method="from_config", | |
| ) | |