( Not my Model )

https://civitai.com/models/2880480/anima-38b?modelVersionId=3284973

v1.1 / Semantic Connector v2

Anima visual style with Qwen3.5 4B language understanding.

52-block DiT · Qwen3.5 4B

What is this?

Anima 3.8B is an expansion of Anima 2.9B aimed at prompt adherence, multi-character binding, interactions, spatial instructions, and mixed natural-language/tag prompting. Anima 2.9B's layers were expanded to 52, making the diffusion model approximately 3.8B parameters, hence the name.

HF: https://huggingface.co/lylogummy/Anima-3.8B

Comfy Workflow: https://huggingface.co/lylogummy/Anima-3.8B/blob/main/workflows/workflow_1.1.json

Version 1.1 contains:

• the expanded 52-block diffusion transformer ;

• the new timestep-aware Semantic Connector v2 , bundled into the diffusion checkpoin;

• the separate Qwen3.5 4B and native Qwen3 0.6B text encoders.

“3.8B” is the release name for the expanded Pro52 diffusion model. Qwen3.5 4B remains a separate inference component.

What changed in v1.1

Trained for ~140h on 4xA40s.

Semantic Connector v2 replaces the older progressive-cross experiment. It is timestep-aware, so it remains part of the sampling model and is evaluated at each denoising step. The connector and DiT now ship as one inference checkpoint. There is no separate adapter file to select and no strength slider to tune: v2 always uses the trained strength of 1.0 .

The text encoders are still separate. They are only needed when a prompt is encoded and can be released or offloaded afterward. Changing the prompt loads them again; reusing cached conditioning does not. This keeps the steady sampling footprint much lower than holding the diffusion model and both text encoders in VRAM at the same time.

The original preview material is preserved below for reference. The old v1 adapter remains supported by the companion extensions as a fallback, but v1.1/v2 is the release path going forward.

Recommended starting point

• Resolution: 832x1216 px (or any other 1MP res)

• CFG: 4–7

• Steps: 28–50

• Sampler/scheduler: res_multistep + Beta (generally anything with Beta)

Prompting

Natural language, tags, and hybrids all work. For complex scenes, assign each subject its own sentence:

Miku on top right.
Teto on lower left.
Apple on top left.
Nothing on lower right.
etc.

Try to avoid pronouns when working with multiple characters, e.g: she/he, use explicit names instead; e.g: Instead of

2girls, Miku is sitting near Teto, she is eating an ice-cream.

Try:

There are two girls in this illustration.
Miku from Vocaloid and Teto from Vocaloid.
Miku is sitting near Teto, Miku is eating an ice-cream.

Other than this, prompt format should follow Anima/Anima 2.9 guidance.