GGUF: HyperFlux Unchained 8-Steps (Flux Unchained + ByteDance HyperSD LoRA)

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

A merge of FluxUnchained and HyperSD LoRA from ByteDance - converted to GGUF. As a result, it can now generate artistic NSFW images in 8 steps while consuming very low VRAM. The Q_4_0 model consumes around 6.5 GB VRAM and takes around 1.5 min to generate a 1024x1024 image with 8 steps on my 1080ti. [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.]

You can also combine it with other LoRAs to get the effect you want.

Advantages Over FastFlux Unchained and HyFU

• Much better quality: you get much better quality and expressiveness at 8 steps compared to Schnell models like HyFU

• 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