Versions
int8: recommended. Fast, accurate, compatible with almost any GPU.
mxfp8: added for comparison. In theory (and according to nVidia PR) should be more accurate than int8, but in practice I was not able to spot any definitive advantages. A bit slower than int8, but still faster than original bf16. Compatible only with RTX 50xx series (Blackwell).
Performance on my setup
original bf16 (baseline): 2.20 it/s +0%
int8: 3.23 it/s +46%
int8 + torch compile (comfy core): 3.59 it/s +63%
int8 + turbo lora, cfg=1: 6.50 it/s +295%
int8 + turbo lora, cfg=1 + torch compile (comfy core): 7.55 it/s +343%
mxfp8: 2.58 it/s +17%
This is high quality int8 quantized version of base Anima v1.0 model. It retains ~90% of original model quality, but uses about 50% less VRAM and also runs faster on almost any nVidia GPU (AMD not tested). Nice trade-off, especially for low-end GPUs.
Can be used as a drop-in replacement for original Anima model in latest ComfyUI, no custom nodes required. If you have troubles running the model make sure that you updated both ComfyUI itself and its dependencies (e.g.pip install -U -r requirements.txt on manual linux install).
Converted to int8 / mxfp8 using convert_to_quant script.
Description
Anima-Aesthetic v1.0.
int8, ConvRot group size 256, rowwise, learned rounding SVD
FAQ
Comments (10)
Thank you for posting the comparison images. Will make int8 for Aesthetic 1b? It is better than Aesthetic 1 IMO.
@ikiru99percent Btw comparison images should have comfy workflow embedded (one custom node for the grid required), so you can test with different prompts / settings. As for the Aesthetic v1b I'll upload it later.
@somedoby Thank you! Great work.
Turbo V1 int8 is slower than Anima V1 with turbo lora in my PC with RTX 3070.
Turbo V1 int8: 1.04s/it.
Anima V1+turbo lora: 1.82it/s.
The quality drop feels a bit more like 20%, but the speed increase is insane, from 360 seconds to 160. You sir have done a wonderful job!
int4 just landed in ComfyUI 👀
Interesting!
But this bit "A proper quant would have a mix of: pure convrot int4, convrot int4 with int8 matrix mult, convrot int8 and 16 bit precision linears to get the best speed/size/quality." means we probably have wait a while before things are properly sorted out.



