rouwei_v080Epsilon.safetensors

6.46 GB · SafeTensor · fp16 · pruned

rouwei_v080Epsilon_trainingData.zip

1 MB · Other

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<rouwei080-xl>

In depth retraining of Illustrious to achieve best prompt adherence, knowledge and state of the art performance. Big dreams come true The version number is just an index of current final release, not a fraction of the planned training. Large scale finetune using gpu cluster with a dataset of ~13M pictures (~4M with natural text captions) - Fresh and wast knowledge about characters, concepts, styles, cultural and related things - The best prompt adherence among SDXL anime models at the moment of release - Solved main problems with tags bleeding and biases, common for Illustrious, NoobAi and other checkpoints - Excellent aesthetics and knowledge across a wide range of styles ( (), including hundreds of unique cherry-picked datasets from private galleries, including those received from the artists themselves) - High flexibility and variety without stability tradeoff - No more annoying watermarks for popular styles thanks to clean dataset - Vibrant colors and smooth gradients without trace of burning, full range even with epsilon - Pure training from Illustrious v0.1 without involving third-party checkpoints, Loras, tweakers, etc. There are also some issues and changes compared to the previous version, please RTFM. Dataset cut-off - end of April 2025. Features and prompting: Important change: When you are prompting artist styles, especially mixing several, their tags MUST BE in a separate CLIP chunk. Just add BREAK after it (for A1111 and derivatives), use conditioning concat node (for Comfy) or at least put them in the very end. Otherwise, significant degradation of results is likely. Basic: The checkpoint works both with short-simple and long-complex prompts. However, if there are contradictory or weird things - unlike with others they won't be ignored affecting the output. No guide-rails, no safeguards, no lobotomy. Just prompt what you want to see and don't prompt what shouldn't be on the picture. If you want to have a view from above - don't put ceiling into positive, if you want to have crop view with head out of frame - don't make detailed description of character facial features, and so on. Pretty simple but sometimes people are missing it. Version 0.8 comes with advanced understanding of natural text prompts. It doesn't mean that you are obligated to use it, tags only - completely fine, especially because understanding of tags combinations is also improved. Do not expect it to perform like Flux or other models based on T5 or LLM text encoders. The whole size ot SDXL checkpoint is less then only that text encoder, in addition illustrious-v0.1 which is used as the base completely forgot a lot of general things from vanilla sdxl-base. However, even in current state it works much better, allows to do new things usually impossible without external guidance, as well making manual editing, inpainting, etc more convenient. To achieve best performance you should keep track of CLIP chunks. In SDXL the prompt is separated into a chunks of 75 (77 including BOS and EOS) tokens, that are processing by CLIP separately, and only then are concatinating and comes as conditions to unet. If you want to specify some features for character/object and separate them from other prompt parts - make sure they are in the same chunk and optionally separate it with BREAK. It will not solve problem of traits mixing completely, but can reduce it improving overall understanding, since text encoders on RouWei are able to process the whole sequence, not individual concepts better then others. Dataset contains only booru-style tags and natural text expressions. Despite having a share of furries, real life photos, western media, etc. all captions have been converted to classic booru style to avoid a number of problems from mixing of different systems. So e621 tags won't be understanded properly. Sampling parameters: - ~1 megapixel for txt2img, any AR with resolution multiple of 32 (1024x1024, 1056x, 1152x, 1216x832,...). Euler_a, 20..28steps. - CFG: for epsilon version 4..9 (7 is best), for vpred version, 3..5 - Sigmas multiply may improve results a bit, CFG++ samplers work fine. LCM/PCM/DMD/... and exotic samplers untested. - Some schedulers doesn't work well. - Highresfix - x1.5 latent + denoise 0.6 or any gan + denoise 0.3..0.55. - For vpred version lower CFG 3..5 is needed! For vpred version lower CFG 3..5 is needed! Quality classification: Only 4 quality tags: masterpiece, best quality for positive and low quality, worst quality for negative. Nothing else. Actually you can even omit positive and reduce negative to low quality only, since they can affect basic style and composition. Meta tags like lowres have been removed and don't work, better not to use them. Low resolution images have been either removed or upscaled and cleaned with DAT depending on their importance. Negative prompt: worst quality, low quality, watermark That's all, no need of "rusty trombone", "farting on prey" and others. Do not put tags like greyscale, monochrome in negative unless you understand what are you doing. Extra tags for brightness/colors/contrast section below can be used Artist styles: , (also can be found in "training data"). Used with "by " it's mandatory. It will not work properly without it. "by " is a meta-token for styles to avoid mixing/misinterpret with tags/characters of similar or close name. This allows to have a better results for styles and at the same time avoid random style fluctuation that you may observe in other checkpoints. Multiple give very interesting results, can be controlled with prompt weights and spells. YOU MUST ADD BREAK after artists/style tags (for A1111) or concat conditioning (for Comfy) or put them in the very end of your prompt. For example: by kantoku, by wlop, best quality, masterpiece BREAK 1girl, ... General styles: 2.5d, anime screencap, bold line, sketch, cgi, digital painting, flat colors, smooth shading, minimalistic, ink style, oil st

SHA256

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ModelTrends.ai Model ID

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