[Note: Unzip the download to get the GGUF. Civit doesn't support it natively, hence this workaround]GGUF version of FluxUnchained by <a target="_blank" rel="ugc" href="https://civarchive.com/user/socalguitarist">socalguitarist</a> . Credit goes to him for tuning this model. I converted it to GGUF by a modified version of <a target="_blank" rel="ugc" href="https://github.com/city96/ComfyUI-GGUF/blob/main/tools/convert.py">this script</a>.It can be used in ComfyUI with this <a target="_blank" rel="ugc" href="https://github.com/city96/ComfyUI-GGUF">custom node</a> or with Forge UI.See <a target="_blank" rel="ugc" href="https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/1050">https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/1050</a> to learn more about Forge UI GGUF support and also where to download the VAE, clip_l and t5xxl models.<h3 id="which-model-should-i-download-qvho7d7ss">Which model should I download?</h3>[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 &lt; Q4_1 &lt; Q5_0 &lt; Q5_1 &lt; Q8_0.<ul><li>Q4_0 and Q4_1 should fit in 8 GB VRAM</li><li>Q5_0 and Q5_1 should fit in 11 GB VRAM</li><li>Q8_0 if you have more!</li></ul>Note: With CPU offloading, you will be able to run a model even if doesn't fit in your VRAM.<h2 id="updates-7gh1f8rcs">Updates</h2>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 checkpoint and then made the GGUF. This version can produce more details and much crisper results.All the license terms associated with Flux.1 Dev apply.

GGUF: Flux Unchained

Bigger than Q4_0, should be better

ggufFluxUnchained_q41V2.zip

Q4_1_v2

Files with SHA256: 7955f0ad3120cc1c5d84d56edaef8b878c6e38a526554d167eb6c401407b4017