This is a simplified guide to an AI model called kontext-fix-jpeg-compression maintained by fofr. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter. This is a simplified guide to an AI model called kontext-fix-jpeg-compression kontext-fix-jpeg-compression maintained by fofr fofr . If you like these kinds of analysis, join AIModels.fyi AIModels.fyi or follow us on Twitter Twitter . Model overview kontext-fix-jpeg-compression is a specialized fine-tune of FLUX Kontext designed to restore images degraded by JPEG compression artifacts. This model addresses a common problem where JPEG's lossy compression introduces visible blockiness, color banding, and quality loss. Unlike general image restoration approaches, this fine-tune focuses specifically on the compression artifacts that result from JPEG encoding. If you need to restore photos with broader damage like scratches or fading, restore-image offers a more general restoration approach, while kontext-old-and-damaged takes a different direction by intentionally aging photos. kontext-fix-jpeg-compression restore-image kontext-old-and-damaged Model inputs and outputs The model accepts a degraded image along with a text prompt describing the desired outcome. It outputs a cleaned image in your choice of format and quality. Key parameters let you control the generation process, from inference steps that affect detail to guidance scales that influence how closely the output follows your instructions. Inputs Input Image: A JPEG, PNG, GIF, or WebP image to processPrompt: Text description of what you want to generate or how to edit the imageAspect Ratio: Set to match your input image or choose from preset ratios (1:1, 16:9, 21:9, 3:2, 2:3, 4:5, 5:4, 3:4, 4:3, 9:16, 9:21)Megapixels: Output resolution, either 1 megapixel or 0.25 megapixelsNum Inference Steps: Number of generation steps (4-50, default 30)Guidance: Guidance scale for generation (0-10, default 2.5)Seed: Optional random seed for reproducible resultsOutput Format: Choose from WebP, JPG, or PNGOutput Quality: Quality level 0-100 for JPG and WebP outputsDisable Safety Checker: Option to bypass NSFW filteringLoRA Strength: Control the strength of the fine-tune (default 1)Replicate Weights: Path to custom LoRA weights if applicable Input Image: A JPEG, PNG, GIF, or WebP image to process Input Image Prompt: Text description of what you want to generate or how to edit the image Prompt Aspect Ratio: Set to match your input image or choose from preset ratios (1:1, 16:9, 21:9, 3:2, 2:3, 4:5, 5:4, 3:4, 4:3, 9:16, 9:21) Aspect Ratio Megapixels: Output resolution, either 1 megapixel or 0.25 megapixels Megapixels Num Inference Steps: Number of generation steps (4-50, default 30) Num Inference Steps Guidance: Guidance scale for generation (0-10, default 2.5) Guidance Seed: Optional random seed for reproducible results Seed Output Format: Choose from WebP, JPG, or PNG Output Format Output Quality: Quality level 0-100 for JPG and WebP outputs Output Quality Disable Safety Checker: Option to bypass NSFW filtering Disable Safety Checker LoRA Strength: Control the strength of the fine-tune (default 1) LoRA Strength Replicate Weights: Path to custom LoRA weights if applicable Replicate Weights Outputs Output: The restored image in your specified format and quality Output: The restored image in your specified format and quality Output Capabilities This model intelligently removes the characteristic blocky patterns and color shifts that JPEG compression introduces. It understands the structure of compression artifacts and can reconstruct missing detail while preserving the original image content. The model maintains content fidelity while smoothing away compression noise, making it suitable for images that have been over-compressed or saved multiple times with quality loss. What can I use it for? Professionals working with archived photographs or web-sourced images can use this to improve visual quality before further processing or publication. Digital marketers can restore product images that were originally saved in low-quality JPEG format. Publishers and archivists benefit from recovering detail in compressed historical photographs. Photographers can clean up smartphone images or compressed web downloads before editing. The model is useful in workflows where image quality matters but source files have compression limitations. You could integrate this into a service that helps users improve their photo libraries or build tools for photography professionals. Things to try Experiment with the prompt to guide how the model interprets and repairs the image—describing the subject matter helps the model make better restoration decisions. Adjust the guidance scale to balance between faithful artifact removal and creative enhancement; lower guidance stays closer to the original while higher guidance makes more dramatic improvements. Try different inference step counts to find the sweet spot between quality and processing speed for your use case. When working with heavily compressed images, you might describe the intended content in your prompt to help the model reconstruct lost detail more accurately.

Text-to-3D in a Hurry: Hunyuan 3D Rapid Turns Prompts Into Fully-Textured Assets

Fix JPEG Artifacts Fast With FLUX Kontext

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