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n-Arno/Anima-P3-Turbo-AIO-Q4_K
Anima-P3-Turbo-AIO-Q4_K is a machine learning model from n-Arno. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.
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.gguf8.8 GB · 100%
From the Hugging Face model README
Update2
Added UNNAMED-ANIMA-V1-Turbo-AIO-SdCpp-Q4_K.gguf, quantization of the AIO version of UnNamedA V1.
Update
Two new model has been uploaded:
The Anima V1 Turbo is an AIO model based on Anima Base V1 with three LoRA merged:
Here is an example result (with the same command as below):

nArnima is the quantization of this model: https://civitai.red/models/2626223/narnima-turbo-all-in-one-anima-v1-checkpoint
Both have been quantized using this command:
sd-cli -M convert -m model-BF16.safetensors -o model-Q4_K.gguf --tensor-type-rules "^model\.diffusion_model\.blocks\.[0-9]*\.(cross_attn|self_attn|mlp).*=q4_K,^model\.diffusion_model\.blocks\.[0-9]*\.adaln_modulation_.*=q6_K,^text_encoders\.llm.*=q8_0"
The choice of tensor data type is experimental: https://civitai.red/articles/30215/quantizing-anima-some-tests
Previously
This is a "All In One" version of Anima-Preview3 for the GPU Poor:
sd-cli -M convert -m Anima-P3-Turbo-AIO.safetensors --type q4_K -o ANIMA/Anima-P3-Turbo-AIO-Q4_K.ggufParameters:
Example usage:
sd-cli -m Anima-P3-Turbo-AIO-Q4_K.gguf --vae-tiling --fa --offload-to-cpu --steps 16 --cfg-scale 1 -W 832 -H 1216 --sampling-method er_sde --scheduler smoothstep --cache-mode spectrum --seed 666 -p "score_9, masterpiece, best quality, newest, 1girl, solo, cowboy shot, facing viewer, holding a board with 'AIO ANIMA TURBO' written on it"

And as a server (with a LoRA folder provided):
sd-server -m Anima-P3-Turbo-AIO-Q4_K.gguf --vae-tiling --offload-to-cpu --fa -l 0.0.0.0 --lora-model-dir ./lora --steps 16 --cfg-scale 1 -W 832 -H 1216 --sampling-method er_sde --scheduler smoothstep --cache-mode spectrum --seed 666