Instructions to use BxuanZ/FLUX-RS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BxuanZ/FLUX-RS with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BxuanZ/FLUX-RS", 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
- Local Apps
- Draw Things
- DiffusionBee
Add link to paper, code and citation
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by nielsr HF Staff - opened
README.md
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---
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license: other
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license_name: flux-1-dev-non-commercial-license
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license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
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library_name: diffusers
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pipeline_tag: text-to-image
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base_model: black-forest-labs/FLUX.1-dev
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tags:
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- diffusers
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- flux
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# FLUX-RS
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FLUX-RS is a remote sensing text-to-image checkpoint obtained by fine-tuning `black-forest-labs/FLUX.1-dev` on a curated remote sensing corpus. It is designed to improve semantic alignment and visual realism for aerial and satellite scene synthesis, especially in settings that require dense structural details such as buildings, roads, vehicles, farmland parcels, and waterfront layouts.
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This model serves as the domain-specialized generative prior used in **SHARP**: *Spectrum-aware Highly-dynamic Adaptation for Resolution Promotion in Remote Sensing Synthesis*.
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## Model Summary
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## Usage
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--height 1024
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```
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---
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base_model: black-forest-labs/FLUX.1-dev
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library_name: diffusers
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license: other
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license_name: flux-1-dev-non-commercial-license
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license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
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pipeline_tag: text-to-image
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tags:
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- diffusers
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- flux
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# FLUX-RS
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[**Paper**](https://huggingface.co/papers/2603.21783) | [**Code**](https://github.com/bxuanz/SHARP)
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FLUX-RS is a remote sensing text-to-image checkpoint obtained by fine-tuning `black-forest-labs/FLUX.1-dev` on a curated remote sensing corpus. It is designed to improve semantic alignment and visual realism for aerial and satellite scene synthesis, especially in settings that require dense structural details such as buildings, roads, vehicles, farmland parcels, and waterfront layouts.
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This model serves as the domain-specialized generative prior used in **SHARP**: *Spectrum-aware Highly-dynamic Adaptation for Resolution Promotion in Remote Sensing Synthesis*.
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## Model Summary
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- **Developed by:** Bingxuan Zhao, Qing Zhou, Chuang Yang, and Qi Wang
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- **Base model:** `black-forest-labs/FLUX.1-dev`
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- **Model type:** Diffusers `FluxPipeline`
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- **Domain:** Remote sensing text-to-image generation
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- **Intended use:** Research, evaluation, and remote sensing synthesis experiments
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## Usage
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--height 1024
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```
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## Citation
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```bibtex
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@misc{zhao2026sharpspectrumawarehighlydynamicadaptation,
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title={SHARP: Spectrum-aware Highly-dynamic Adaptation for Resolution Promotion in Remote Sensing Synthesis},
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author={Bingxuan Zhao and Qing Zhou and Chuang Yang and Qi Wang},
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year={2026},
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eprint={2603.21783},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2603.21783},
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}
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```
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## License
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This checkpoint is derived from `black-forest-labs/FLUX.1-dev`. Please follow the license and usage terms associated with the base model when using or redistributing FLUX-RS.
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