Instructions to use lmquan/hummingbird with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lmquan/hummingbird 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("lmquan/hummingbird", 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
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# Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment
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This repository contains the LoRA weights for the Hummingbird model, presented in [Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment](https://huggingface.co/papers/2502.05153).
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The Hummingbird model generates high-quality, diverse images from a multimodal context, preserving scene attributes and object interactions from both a reference image and text guidance.
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# Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment [ICLR 2025]
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This repository contains the LoRA weights for the Hummingbird model, presented in [Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment](https://huggingface.co/papers/2502.05153).
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The Hummingbird model generates high-quality, diverse images from a multimodal context, preserving scene attributes and object interactions from both a reference image and text guidance.
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