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Voir le guide →OmniGen est un projet de image open source, créé par VectorSpaceLab (VectorSpaceLab/OmniGen sur GitHub). Il compte 4 343 étoiles sur GitHub et 364 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 4 décembre 2025.
OmniGen: one unified model for text-to-image, editing, subject-driven and identity-preserving generation
Create the documented Conda environment (Python 3.10.x) — OmniGen inference expects a CUDA GPU.
Clone the repo and install it in editable mode after installing the pinned PyTorch build.
Load Shitao/OmniGen-v1 weights from Hugging Face through the OmniGen pipeline (or Diffusers).
Write a prompt, pass input images with guidance scales, and save your first edited or subject-driven image.
Text-to-image, editing, subject-driven, identity-preserving, and image-conditioned generation in one model.
Tasks that usually need ControlNet or IP-Adapter are handled natively by the framework.
Reference images inline with <|image_1|>-style placeholders and combine multiple inputs.
Separate guidance_scale and img_guidance_scale parameters balance text and image influence.
Use the model through Hugging Face Diffusers or run the included Gradio demo (app.py).
LoRA and full-parameter training scripts (train.py with accelerate) adapt the model to your data.
Clone the project and enter its directory.
git clone https://github.com/VectorSpaceLab/OmniGen.git
cd OmniGenCreate the Conda env, install the pinned CUDA PyTorch build from the README, then install the package.
conda create -n omnigen python=3.10.13
conda activate omnigen
pip install torch==2.3.1+cu118 torchvision --extra-index-url https://download.pytorch.org/whl/cu118
pip install -e .Load the pretrained model and generate with an image reference, following the README's examples.
from OmniGen import OmniGenPipeline
pipe = OmniGenPipeline.from_pretrained("Shitao/OmniGen-v1")
images = pipe(prompt="Edit <|image_1|>", input_images=["input.png"], guidance_scale=2.5, img_guidance_scale=1.6, seed=0)VectorSpaceLab, the research team behind the OmniGen paper (arXiv 2409.11340).
The code is MIT-licensed and the weights are downloadable from Hugging Face; check the model card for the weights' own terms.
No — it runs locally on your GPU with downloaded weights.
OmniGen folds editing, subject-driven, and conditioned generation into one model with in-prompt image references, instead of a base model plus separate plugin networks.
Create the Python 3.10 Conda env, install the pinned PyTorch build, `pip install -e .`, then run the OmniGenPipeline example from the README.
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