FluxSR is a LoRA adapter for one-step real-world image super-resolution, based on FLUX.1-dev. See the FluxSR project. The matching training, inference, and evaluation code is available in The source repository is a private preview and requires an authorized GitHub account. The adapter is provided as pytorchloraweights.safetensors. - pytorchloraweights.safetensors: transformer LoRA adapter. - trainingconfig.json: training and inference settings. - t5embedding.pth, clipembedding.pth: the fixed text conditions. - prompts10k.json: the 10,000 prompts used to generate training pairs. - FLUX.1-dev-LICENSE.md: model license supplied with the base model. - NOTICE: BFL attribution and FluxSR…
Independent publisher
Tianshuo Li
mon3tr
Models in Library2
Datasets in Library0
Models on Hugging Face2
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Published tasks: Image to image.
Reported licenses: apache-2.0, other. Open a record to review its specific license and conditions.
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Models
Jianze Li 1, Kai Liu 1, Ziqing Zhang 1, Weihang Zhang 2, Yong Guo 1, Linghe Kong 1, Yulun Zhang 1,† 1. Stage I — Spatial and composition training. Learn spatial localization, composition preferences, and complete reference-frame prediction. 2. Stage II — Quality-Guided Online Supervision (QOS). Generate greedy and sampled answers, evaluate complete-frame quality, and imitate the best answer. 3. Stage III — Greedy-Referenced Optimization (GRO). Refine fresh samples using positive and negative quality differences relative to the current greedy answer. At inference, FrameLite directly predicts one normalized crop through greedy decoding. No candidate search, external scorer, retrieval…