Instructions to use Anzhc/AAAAnima with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Anzhc/AAAAnima with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Anzhc/AAAAnima", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| license: other | |
| license_name: circlestone-labs-non-commercial-license | |
| license_link: https://huggingface.co/circlestone-labs/Anima/blob/main/LICENSE.md | |
| base_model: | |
| - circlestone-labs/Anima | |
| library_name: diffusers | |
| Nothing interesting going on here, move along. | |
| Just a small finetune of Anima, 4 epochs with 12k images. *Nothing* interesting was done. Is more 2.5d, will lead towards more abstract, that's about it. | |
| Not-so-early upd: another 5 epochs of 12k images with *nl*. Then 25 epochs of subset of 430 images with nl written by codex. So total of ~72k samples on top of 48k, for a total of 120k. | |
| ## Issues | |
| ~~Can have strong bokeh in some cases.~~ | |
| ~~Some wide shots are transforming into less common compositions for which there is barely any data and denoise fails hard on them.~~ | |
| Idk, seem fixed. | |
| ## Positives | |
| Tends to add more details. | |
| Elements in the scene usually interact more (i.e. abstract features affecting more concepts) | |
| Some complex compositions become better defined. | |
| Booba is bigger on average. (But i will not benchmark that(for now)) | |
| Less common concepts with abstract meaning may perform better (like horror) | |
| ## Other Features | |
| On average, less overwhelming contrast, dimmer. | |
| Can use higher cfg without blowing out colors (i use 4-6, vs 3.5 on base), but not required. | |
| ## Params i use | |
| dpm++ sde, 16-24 steps, cfg 3.5-7, shift 4 | |
| ## How was it trained | |
| Lr: variable LR per block group, from 3e-6 to 5e-6, from early to late. Additional modification for LR scaling based on bucket resolution | |
| Adapter: Trained for not-so-early, 5e-7. | |
| Loss: L2, with some modifications for rare aspect ratio buckets and depth. | |
| Schedule: Logit-Normal -0.2 1.5(or was it 1.5 -0.2...)(Bluvoll's schedule) with shift 4 and normal tail sampling modification. (Tldr, all that fixes lack of front in default logit normal and normalizes amount of sampling at the very late timesteps, without making them overbearing at high shift.) | |
| Captions: ~~tags only.~~ ~~(you won't make me suffer through captioning 12k images with gemma 4 31b or qwen 3.6 27b locally for NL counterpart)~~ | |
| Tags+NL, i suffered for 5 days. | |
| ## Examples | |
| Anima base 1.0 on left, this fintune on right(updated with not-so-early version). | |
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