Anima-Base

    The pretrained, unrefined base model. Maximum flexibility, diversity, and style adherence.

Anima-Turbo

    Coming soon.

anima-base-v1.0.safetensors goes in ComfyUI/models/diffusion_models

qwen_3_06b_base.safetensors goes in ComfyUI/models/text_encoders

qwen_image_vae.safetensors goes in ComfyUI/models/vae (this is the Qwen-Image VAE, you might already have it)

Works at resolutions between 512^2 and 1536^2 pixels.

30-50 steps, CFG 4-6.

A variety of samplers work. Some of my favorites:

    er_sde: neutral style, flat colors, sharp lines. I use this as a reasonable default.

    euler_a: Softer, thinner lines. Can sometimes tend towards a 2.5D look. CFG can be pushed a bit higher than other samplers without burning the image.

    dpmpp_2m_sde_gpu: similar in style to er_sde but can produce more variety and be more "creative". Depending on the prompt it can get too wild sometimes.

If going for a more realistic / painterly look, the beta57 scheduler (ComfyUI RES4LYF custom node pack) can help make better textures, since it puts more emphasis on low-noise timesteps.

Use lowercase for tags, and spaces instead of underscores. Score tags are the only tags that use underscores.

Recommended positive prefix: "masterpiece, best quality, score_7, safe, "

Recommended negative: "worst quality, low quality, score_1, score_2, score_3, artist name"

When using a tag that is different between Danbooru and Gelbooru, prefer the Gelbooru version.

Prompt weighting works, but needs a weight higher than typically used for SDXL. Example: "(chibi:2)"

Follow standard English capitalization rules for character and series names.

If using pure natural language, more descriptive is better. Aim for at least 2 sentences. Extremely short prompts can give unexpected results.

You can mix tags and natural language in arbitrary order.

You can put quality / artist tags at the beginning of a natural language prompt.

    "masterpiece, best quality, @big chungus. An anime girl with medium-length blonde hair is..."

Name a character, then describe their basic appearance.

    "Digital artwork of Fern from Sousou no Frieren, with long purple hair and purple eyes, wearing a black coat over a white dress with puffy sleeves..."

    This is extra important when prompting for multiple characters. If you just list off character names with no description of appearance, the model can get confused.

The model doesn't do realism well. This is intended. It is an anime / illustration / art focused model.

The model may generate undesired content, especially if the prompt is short or lacking details.

    Avoid this by using the appropriate safety tags in the positive and negative prompts, and by writing sufficiently detailed prompts.

The model isn't great at text rendering. It can generally do single words and sometimes short phrases, but lengthy text rendering won't work well.

The base version is a true base model. It hasn't been aesthetic tuned on a curated dataset. The default style is very plain and neutral, which is especially apparent if you don't use artist or quality tags.

Don't train the LLM adapter. My own training script, diffusion-pipe, lets you set llm_adapter_lr=0 to completely disable training it, and the example config has this as a default.

    Other trainers like sd-scripts have similar options that should be used.

    The LLM adapter processes the text embeddings before they get to the diffusion model, and therefore has an outsized influence on the generated images. The adapter itself contains a surprising amount of knowledge and is easy to degrade by training it.

Use a low learning rate. For a rank 32 LoRA, start with 2e-5 and adjust up or down from there.

    As a base model, there is no aggressive aesthetic tuning or RLHF you need to overcome when finetuning.

    The model has an extremely large and diverse amount of visual concepts baked in already. A light touch is all you need.

Example of a style LoRA, with dataset and configs shared.