🎈 What is Anima_LiquidMix?

Anima_LiquidMix is an experimental merge model designed to modify rendering tendencies while preserving Anima’s original prompt understanding as much as possible.

It was created by integrating custom-made LoRAs and experimental merge components into multiple Anima-based checkpoints in order to test how much texture, rendering characteristics, character knowledge, and visual behavior can be controlled through model merging.

Because Anima’s basic internal structure is preserved as much as possible after merging, the model can also be used as a base checkpoint for LoRA training.

This makes it useful for purposes such as:

• Testing LoRA behavior

• Training experiments

• Comparing rendering tendencies between different merged checkpoints

• Testing expanded Anima architectures

• Comparing prompt adherence across different model generations

🧬 Anima 2.9B / 40-Layer Version

In this version, the existing Anima_LiquidMix weights were expanded into the 40-layer structure while attempting to preserve the original LiquidMix rendering characteristics as much as possible.

The 12 additional DiT blocks introduced in Anima 2.9B are placed at the following positions:

2, 5, 8, 11, 14, 17, 21, 24, 27, 30, 33, 36

This expands the capacity of the DiT while retaining the experimental rendering characteristics developed in earlier LiquidMix versions.

This 2.9B checkpoint can also be used as a base model for future LoRA training.

🫗 Anima 3.8B / 52-Layer Version

In this version, the 40-block DiT of Anima 2.9B is expanded to 52 blocks.

The 12 additional DiT blocks are distributed throughout the existing 40-block architecture.

The insertion positions are:

3, 7, 11, 15, 19, 23, 27, 31, 35, 39, 43, 47

Like the 2.9B version, the 3.8B checkpoint can also be used experimentally as a base checkpoint for native Anima 3.8B / 52-layer LoRA training.

🧠 Qwen3.5 4B / Semantic Connector v2 Support

One of the major additions in the Anima 3.8B generation is support for Qwen3.5 4B through Semantic Connector v2.

This allows richer semantic information from natural-language prompts to influence image generation, in addition to conventional tag-based prompting.

It is particularly useful for more complex instructions involving:

• Multiple characters with different attributes

• Character-specific hair colors, clothing, and objects

• Left/right and other spatial relationships

• Interactions between multiple characters

• Longer natural-language composition instructions

For example, a prompt describing “a red-haired character on the left and a blue-haired character on the right, each holding a different object” is the type of structured instruction that the newer architecture is intended to handle more effectively.

Traditional Danbooru-style tags remain useful, so the 3.8B version can also be used with hybrid prompts combining tags and natural-language descriptions.

The 3.8B architecture also introduces Semantic Connector v2 weights, which connect this additional semantic information to the image-generation model.

🐲 Changes from Preview Base

• Modified contrast behavior

• Added more variation in color and lighting

• Enhanced texture rendering

• Modified glow and effect rendering

• Changed rendering tendencies for fantasy / furry / creature-themed characters

• Improved support for newer character knowledge derived from newer Anima checkpoints

• Improved potential for natural-language prompt understanding, multi-character attribute binding, and spatial instructions

🧪 Purpose

This model was not created to reproduce any specific art style.

Instead, it is an experimental model intended to observe and test how model merging, LoRA integration, architecture expansion, and semantic conditioning affect rendering behavior.

The Anima 2.9B version explores how rendering changes when a checkpoint originally developed around the 28-layer Anima architecture is expanded into a 40-layer DiT structure.

The Anima 3.8B version extends this experiment further by expanding the DiT from 40 to 52 blocks, while also exploring the newer semantic-conditioning capabilities provided by Qwen3.5 4B and Semantic Connector v2.

🍩 Recommended Use

• Comparing multiple Anima-based checkpoints

• Comparing 28-layer / 40-layer / 52-layer Anima models

• LoRA training experiments

• Testing attribute binding across multiple characters

• Testing prompts involving spatial relationships and character interactions

• Generating fantasy / furry / insect / monster characters

• Testing glow, effects, and high-contrast rendering

• Testing newer character knowledge derived from newer Anima checkpoints

🦠 Recommended Settings

General

• Sampler: ER SDE / Euler a / DPM++

• Steps: 20–30

• CFG: 4–6

• Resolution: 1024–1536

The 2.9B and 3.8B versions may behave somewhat differently from the original 28-layer models.

🐽 Requirements

For standard Anima text encoding, qwen_3_06b_base is recommended.

For the VAE, QwenimageVAE_liquid1127 is recommended for more stable rendering, color balance, and highlight reproduction.

Anima 2.9B

When using the 40-layer Anima 2.9B version, your ComfyUI environment must be capable of correctly detecting and loading the expanded 40-block DiT architecture.

LoRAs created for the original 28-layer Anima architecture may not be fully compatible with the 40-layer model without modification.

For legacy 28-layer LoRAs, a compatibility LoRA loader can be used to remap the original block indices to their corresponding positions in the 40-layer architecture.

Anima 3.8B

When using the 3.8B version, your environment must support the 52-block Anima 3.8B architecture.

Native 3.8B LoRAs use the expanded 52-layer structure, so 28L / 40L / 52L LoRAs should not be assumed to be directly interchangeable.

Configurations using the newer semantic-conditioning path also require support for Qwen3.5 4B and Semantic Connector v2.

Because Anima 3.8B is larger than Anima 2.9B, it also requires more VRAM and computational resources, particularly for LoRA training.

📛 Notes

• This is not an official model

• This is an experimental merge created for testing purposes

• Both the Anima 2.9B and Anima 3.8B versions are experimental

• The 40-layer and 52-layer versions have different internal structures from the original 28-layer Anima models

• Legacy LoRAs may behave differently on expanded architectures

• Native 52L LoRAs are recommended when training specifically for Anima 3.8B

• Qwen3.5 4B support depends on the generation environment and workflow

• Unexpected outputs or unstable behavior may occur

• Compatibility may vary depending on the ComfyUI version, custom nodes, and model loader implementation

🥚 Feedback

Feedback and example generations are welcome.

Comparison results involving the following would be especially helpful:

• Original Anima_LiquidMix / 28-layer architecture

• Anima 2.9B / 40-layer version

• Anima 3.8B / 52-layer version

• Tag-only prompts vs. natural-language / hybrid prompts on Anima 3.8B