Instructions to use Lotior/mcld with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lotior/mcld 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("Lotior/mcld", 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
Add model card
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by nielsr HF Staff - opened
README.md
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license: mit
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license: mit
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pipeline_tag: image-to-image
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library_name: diffusers
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---
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# MCLD [](https://arxiv.org/abs/2503.15686)
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> **Multi-focal Conditioned Latent Diffusion for Person Image Synthesis** <br>
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> _Jiaqi Liu, Jichao Zhang, Paolo Rota, Nicu Sebe_<br>
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> _Computer Vision and Pattern Recognition Conference (**CVPR**), 2025, Nashville, USA_
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This repository contains the model described in [Multi-focal Conditioned Latent Diffusion for Person Image Synthesis](https://arxiv.org/abs/2503.15686).
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Code: https://github.com/jqliu09/mcld
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