GGUF: FastFlux (Flux.1-Schnell Merged with Flux.1-Dev)

[Note: Unzip the download to get the GGUF. Civit doesn't support it natively, hence this workaround]

Flux1.D merged in Flux1.S. It can generate good-quality images (better than Schnell) with just 4 steps, and the quality further improves with more steps, while consuming a very low amount of VRAM. Q_4_0 can produce 1024x1024 images in 45 seconds on my 11GB 1080ti, while using around 6.5 Gigs of VRAM.

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.

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.

LoRA usage tips

The model seems to work pretty well with LoRAs (tested in Comfy). But you might need to increase the number of steps a little (8-10).

Updates

V2: I created the original (v1) from an fp8 checkpoint. Due to double quantization, it accumulated more errors. So I found that v1 couldn't produce sharp images. For v2 I manually merged the bf16 Dev and Schnell checkpoints and then made the GGUF. This version can produce more details and much crisper results.

All the license terms associated with Flux.1 Dev and Flux.1 Schnell apply.

PS: Credit goes to jice and comfy.org for the merge recipe. I used a slightly modified version of https://github.com/city96/ComfyUI-GGUF/blob/main/tools/convert.py to create this.