Brief introduction:
洗去蒸馏油腻，回归模型本真。
Wash away the distillation and return to the original basic.
可能是目前基于 Flux.1 Schnell 调制的各种模型中，快速出图(4-8步)，遵循原版 Flux Schnell 构图风格，提示词还原能力强，且在出图质量、出图细节、回归真实和风格多样化方面取得最佳平衡的开源可商用 Schnell 基础模型。
Only 4 step, The Model may achieve to the best balance in terms of image quality, details, reality, and style diversity compare with other tuned of Flux.1 Schnell. and have a good ability of prompt following, good of the original Flux model style following.
Based on
FLUX.1-schnell, Merge of
LibreFLUX, finetuned by
ComfyUI,
Block_Patcher_ComfyUI,
ComfyUI_essentials and other tools. Recommended 4-8 steps, usually step 4 is OK. Greatly improved quality and reality compare to other Flux.1 Schnell model.
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可能是目前快速出图(10步以内)的 Flux 微调模型中，遵循原版 Flux.1 Dev 风格，提示词还原能力强、出图质量最好、出图细节超越 Flux.1 Dev 模型，最接近 Flux.1 Pro 的基础模型。
May be the Best Quality Step 6-10 Model, In some details, it surpasses the Flux.1 Dev model and approaches the Flux.1 Pro model. and have good ability of prompt following, good of the original Flux.1 Dev style following.
Based on
Flux-Fusion-V2, Merge of
flux-dev-de-distill, finetuned by
ComfyUI,
Block_Patcher_ComfyUI,
ComfyUI_essentials and other tools.
Recommended 6-10 steps. Greatly improved quality compared to other Flux.1 model.
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GGUF Q8_0 / Q5_1 /Q4_1 量化版本模型文件，经过测试，已同步提供，将不会再提供别的量化版本，如有需要，朋友们可根据下面提示信息，自己下载 fp8 后进行量化。
GGUF Q8_0 / Q5_1 /Q4_1 quantized model file, had tested, and uploaded the same time, over-quantization will lose the advantages of this high-speed and high-precision model, so no other quantization will be provided, you can download the FP8 model file and quantizate it according to the following tips.
Recommend:
UNET versions (Model only) need Text Encoders and VAE, I recommend use below CLIP and Text Encoder model, will get better prompt guidance:
Sample workflow: a very simple workflow as below, needn't any other comfy custom nodes(For GGUF version, please use UNET Loader(GGUF) node of city96's):
Thanks for:
https://huggingface.co/black-forest-labs/FLUX.1-dev, A very good open source T2I model. under the FLUX.1 [dev] Non-Commercial License.
https://huggingface.co/black-forest-labs/FLUX.1-schnell, A very good open source T2I model, under the apache-2.0 licence.
https://huggingface.co/Anibaaal, Flux-Fusion is a very good mix and tuned model.
https://huggingface.co/nyanko7, Flux-dev-de-distill is a great experimental project! thanks for the
inference.py scripts.
https://huggingface.co/jimmycarter/LibreFLUX, A free, de-distilled FLUX model, is an Apache 2.0 version of FLUX.1-schnell.
https://huggingface.co/MonsterMMORPG, Furkan share a lot of Flux.1 model testing and tuning courses, some special test for the de-distill model.
https://github.com/cubiq/Block_Patcher_ComfyUI, cubiq's Flux blocks patcher sampler let me do a lot of test to know how the Flux.1 block parameter value change the image gerentrating. His
ComfyUI_essentials have a FluxBlocksBuster node, let me can adjust the blocks value easy. that is a great work!
https://huggingface.co/twodgirl, Share the model quantization script and the test dataset.
https://huggingface.co/John6666, Share the model convert script and the model collections.
https://github.com/city96/ComfyUI-GGUF, Native support GGUF Quantization Model.
https://github.com/leejet/stable-diffusion.cpp, Provider pure C/C++ GGUF model convert scripts.
Attn: For easy convert to GGUF Q5/Q4, you can use
https://github.com/ruSauron/to-gguf-bat script, download it and put to the same directory with sd.exe file, then just pull my fp8.safetensors model file to bat file in exploer, will pop a CMD windows, and follow the menu to conver the one you want.
LICENSE
The weights fall under the
FLUX.1 [dev] Non-Commercial License.
