A merge of Flux.D with the 8-step HyperSD LoRA from ByteDance - turned into GGUF. As a result, you get an ultra-memory efficient and fast DEV (CFG sensitive) model that generates fully denoised images with just 8 steps while consuming ~6.2 GB VRAM (for the Q4_0 quant).
It can be used in ComfyUI with this custom node or with Forge UI. See https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/1050 to learn more about Forge UI GGUF support and also where to download the VAE, clip_l and t5xxl models.
Advantages Over FastFlux and Other Dev-Schnell Merges
Much better quality: you get much better quality and expressiveness at 8 steps compared to Schnell models like FastFlux
CFG/Guidance Sensitivity: Since this is a DEV model, unlike the Hybrid models, you get full (distilled) CFG sensitivity - i.e., you can control prompt sensitivity vs. creativity and softness vs. saturation.
Fully compatible with Dev LoRAs, better than the compatibility of Schnell models.
The only disadvantage: needs 8-step for best quality. But then, you'd probably try at least 8 steps for best results with Schnell anyway.
Which model should I download?
[Current situation: Using the updated Forge UI and Comfy UI (GGUF node) I can run Q8_0 on my 11GB 1080ti.]
Download the one that fits in your VRAM. The additional inference cost is quite small if the model fits in the GPU. Size order is Q4_0 < Q4_1 < Q5_0 < Q5_1 < Q8_0.
Q4_0 and Q4_1 should fit in 8 GB VRAM
Q5_0 and Q5_1 should fit in 11 GB VRAM
Q8_0 if you have more!
Note: With CPU offloading, you will be able to run a model even if doesn't fit in your VRAM.
All the license terms associated with Flux.1 Dev apply.
PS: Credit goes to ByteDance for the HyperSD Flux 8-steps LoRA which can be found at https://huggingface.co/ByteDance/Hyper-SD/tree/main