<ul><li>Edit workflow：<a target="_blank" rel="ugc" href="http://i71i.com/mby8">http://i71i.com/mby8</a></li><li>Register for free and get 1000 points. Log in every day and get points</li></ul>Perfect Upgrade ，<span style="background-color:rgb(54, 56, 64);color:rgb(253, 126, 20);font-family:-apple-system, blinkmacsystemfont, &quot;Helvetica Neue&quot;, helvetica, &quot;segoe ui&quot;, arial, roboto, &quot;PingFang SC&quot;, miui, &quot;Hiragino Sans GB&quot;, &quot;Microsoft Yahei&quot;, sans-serif;font-size:14px">Seamlessly replace the fill model OneReward is a novel visual domain RLHF approach that significantly improves the generative capabilities of strategy models across multiple subtasks by using Qwen2.5-VL as a generative reward model to enhance multi-task reinforcement learning. Based on OneReward, FLUX.1-Fill-dev-OneReward - Based on FLUX Fill [dev], it surpasses the closed-source FLUX Fill [Pro] in image restoration and epitaxy tasks, providing a powerful new benchmark for future unified image editing research.------------------------------------------------Base model:black-forest-labs/ : Black Forest Labs.bytedance-research/OneReward: The OneReward model developed by the ByteDance research team for reinforcement learning optimization.yichengup/flux.1-fill-dev-OneReward: A model developed by yichengup that combines Flux.1-Fill-dev and OneReward, focusing on image filling and scaling tasks.Label:flux : Flux series models.flux-fill : The image fill feature in the Flux series.onereward: OneReward reinforcement learning methodology.------------------------------------------------FLUX.1-Fill-dev is an open-source image restoration and scaling model developed by Black Forest Labs, and the following is its detailed description:Basic informationModel Architecture: Adopts the Rectified Flow Transformer architecture, combined with the generation capabilities of diffusion models, to intelligently fill in missing areas of images based on text prompts.Parameter scale: 12 billion parameters.Training method: Guidance distillation is used to optimize inference speed.Licensing method: Model weights are publicly available and generated content can be used for personal, scientific, and commercial use, subject to the FLUX.1 [dev] Non-Commercial License.Core features:Image restoration: It can fill in missing or removed areas in the image based on text descriptions and binary masks, achieving high-precision image restoration.Image Expansion: Support for outpainting, which seamlessly expands the boundaries of existing images.Text Understanding and Generation: Understand complex text instructions and combine them with image context to generate natural, coherent restoration results.

flux.1-fill-dev-OneReward，Perfect Upgrade

<ul><li>Edit workflow：<a rel="ugc" href="http://i71i.com/mby8">http://i71i.com/mby8 </a> </li><li>Register for free and get 1000 points. Log in every day and get points </li></ul>OneReward is a novel visual domain RLHF approach that significantly improves the generative capabilities of strategy models across multiple subtasks by using Qwen2.5-VL as a generative reward model to enhance multi-task reinforcement learning. Based on OneReward, FLUX.1-Fill-dev-OneReward - Based on FLUX Fill [dev], it surpasses the closed-source FLUX Fill [Pro] in image restoration and epitaxy tasks, providing a powerful new benchmark for future unified image editing research.------------------------------------------------Base model:black-forest-labs/ : Black Forest Labs.bytedance-research/OneReward: The OneReward model developed by the ByteDance research team for reinforcement learning optimization.yichengup/flux.1-fill-dev-OneReward: A model developed by yichengup that combines Flux.1-Fill-dev and OneReward, focusing on image filling and scaling tasks.Label:flux : Flux series models.flux-fill : The image fill feature in the Flux series.onereward: OneReward reinforcement learning methodology.------------------------------------------------FLUX.1-Fill-dev is an open-source image restoration and scaling model developed by Black Forest Labs, and the following is its detailed description:Basic informationModel Architecture: Adopts the Rectified Flow Transformer architecture, combined with the generation capabilities of diffusion models, to intelligently fill in missing areas of images based on text prompts.Parameter scale: 12 billion parameters.Training method: Guidance distillation is used to optimize inference speed.Licensing method: Model weights are publicly available and generated content can be used for personal, scientific, and commercial use, subject to the FLUX.1 [dev] Non-Commercial License.Core features:Image restoration: It can fill in missing or removed areas in the image based on text descriptions and binary masks, achieving high-precision image restoration.Image Expansion: Support for outpainting, which seamlessly expands the boundaries of existing images.Text Understanding and Generation: Understand complex text instructions and combine them with image context to generate natural, coherent restoration results.

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