Instructions to use PANDATREE/BRIDGE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use PANDATREE/BRIDGE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("PANDATREE/BRIDGE", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Commit History
22 days ago
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Add pipeline tag, library name, and paper link
Verified signatureThis PR adds `pipeline_tag: image-to-image` and `library_name: diffusers` to the metadata for better discoverability and integration on the Hub. It also adds a link to the paper page in the README content.b078763
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Link released subject-condition data, training guide, and BBox token-control examples
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Add BRIDGE FLUX.2 Klein 9B sparse-mask and dense-bbox eval checkpoints
Verified signaturePreserve the original Qwen weight file. Add both BF16 step-1400 ScheduleFree eval transformers with SHA-256 manifests and upstream FLUX license notices.401ac6d
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Upload model.safetensors with huggingface_hub
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Upload README.md with huggingface_hub
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initial commit
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