---
language:
- en
license: other
license_name: upstream-anima-and-source-data-terms
license_link: https://huggingface.co/circlestone-labs/Anima/blob/main/LICENSE.md
base_model:
- circlestone-labs/Anima
datasets:
- toyxyz/booruregion_10k
- toyxyz/booru3dpose_10k
- toyxyz/boorupose_outline_10k
pipeline_tag: image-to-image
tags:
- anima
- controlnet
- lllite
- comfyui
- region-control
- 3d-pose
- pose-conditioning
- outline-conditioning
- instance-segmentation
- safetensors
---
# Anima ControlNet-LLLite
Lightweight ControlNet adapters trained on **[Anima Base 1.0](https://huggingface.co/circlestone-labs/Anima/blob/main/split_files/diffusion_models/anima-base-v1.0.safetensors)** (`anima-base-v1.0.safetensors`). Use Anima Base 1.0 as the base model with these adapters. See the [official Anima repository](https://huggingface.co/circlestone-labs/Anima) for the base model, text encoder and VAE. Choose a model by its control type; each model has a separate document with its training and evaluation details.
## Models and control types
| Model | Control type | Detailed documentation |
| --- | --- | --- |
| [anima_regional_4335.safetensors](anima_regional_4335.safetensors) | **RGB region control** for subject layout, silhouettes, poses, and object placement | [Model details and training parameters](docs/anima_regional_4335.md) |
| [anima_3dpose_6200.safetensors](anima_3dpose_6200.safetensors) | **3D pose control** from shaded RGB body meshes, including fingers | [Model details and training parameters](docs/anima_3dpose_6200.md) |
| [anima_3dpose_outline_7000.safetensors](anima_3dpose_outline_7000.safetensors) | **3D pose with outline** from shaded RGB body meshes plus complementary-color silhouette and overlap boundaries | [Model details and training parameters](docs/anima_3dpose_outline_7000.md) |
`anima_regional_4335` uses a **3-channel RGB instance-color map**: distinct solid colors identify regions, and black marks unassigned pixels. Colors are not fixed semantic labels. This is a region-conditioning adapter, not a black/white-mask inpainting model.
`anima_3dpose_6200` uses **colored, shaded 3D body meshes on black** to guide body layout and pose. The final adapter completed **6,200 updates** (4,335 + 1,865 full-state resumed updates). It uses the same Anima LLLite v2 format; pose and hand accuracy can still vary.
`anima_3dpose_outline_7000` uses **the shaded RGB mesh plus complementary-color outlines and body-overlap boundaries**, on black. It completed **7,000 fresh updates**; it is not the 6,200-step model continued for 800 more steps. Start by comparing the training-like **nominal 4px width at a 768px short-side reference**. The body shading remains part of the input: bare lineart, stick-figure OpenPose, depth maps and binary masks are different conditioning types.
### Combining 3D pose and regional control
**For multi-person scenes, using the 3D pose and regional adapters together can help achieve more stable separation and placement of individual characters.** The pose adapter guides body posture, while the regional adapter defines distinct spatial regions for people. The two forms of guidance complement each other; the combined setup has not been quantitatively evaluated in this release.
## Regional control example
Image (left) and colored region overlay (right). Distinct colors show separate subject and object regions.
### Regional control example workflow and sample input
Sample RGB region map (768 × 1152):
Download the [example workflow JSON](example_workflow/regional_example.json) and [sample region map PNG](example_workflow/regional_example.png), or browse the [example_workflow folder and instructions](example_workflow/README.md). Import the JSON into ComfyUI, upload `regional_example.png` in **Load Image**, and select your installed model files. The example uses the 4,335-step adapter, 30 sampling steps, CFG 4 and strength 1.0.
## 3D pose control example
User-provided ComfyUI example: condition (left), generated image (right). Embedded workflow metadata is removed. See the [3D pose document](docs/anima_3dpose_6200.md) for training and evaluation limits.
### 3D pose example workflow and sample input
Sample pose condition (768 × 1152):
Download the [example workflow JSON](example_workflow/3dpose_example.json) and [sample pose PNG](example_workflow/3dpose_example.png), or browse the [example_workflow folder and instructions](example_workflow/README.md). Import the JSON into ComfyUI, upload `3dpose_example.png` in **Load Image**, and select your installed model files. The example uses the 6,200-step adapter, 30 sampling steps, CFG 4 and strength 1.0.
## 3D pose outline control and comparison
Columns: **pose6200 + no outline**, **outline7000 + no outline**, then outline7000 with **4 / 6 / 8 / 12px** nominal normalized strokes. A completed comparison used **50 poses and 300 images**, with the same seed, prompt, camera, framing and output size within each pose. It used the **miaomiaoHarem_anima16 derivative**, not Anima Base 1.0, at 30 sampling steps, CFG 4, er_sde/simple and strength 2.
**Thicker is not consistently better.** Some hand boundaries become clearer; other cases gain an unwanted extra person or lose a hand relationship. The original pose adapter can be better on individual cases. Start with 4px and compare 6px; assess 8/12px per scene. This is a qualitative one-seed experiment, not a quantitative model ranking.
[Full outline model document](docs/anima_3dpose_outline_7000.md) · [Representative successes and failures, inputs and outputs](examples/outline7000/README.md) · [Single 4px workflow](example_workflow/outline7000_single_4px.json) · [Six-way workflow](example_workflow/outline7000_six_way_comparison.json)
## Advanced workflow: 3D pose + regional control
Download the [advanced workflow JSON](example_workflow/advanced_region_3dpose_example.json). This example uses **[ComfyUI_toyxyz_test_nodes](https://github.com/toyxyz/ComfyUI_toyxyz_test_nodes)** for **3D Pose Editor**, **ComfyCouple Region painter**, and **ComfyCouple Mask**, together with [ComfyUI-Anima-LLLite](https://github.com/kohya-ss/ComfyUI-Anima-LLLite). It combines `anima_3dpose_6200` and `anima_regional_4335` on **Anima Base 1.0**, with per-person prompts and editable region masks.
**Keep the regional ControlNet's `end_percent` low** (the supplied example uses **0.3**) to end its influence earlier in sampling. Alternatively, **paint additional mask areas in ComfyCouple Region painter** to expand or refine each person's region. Adjust these areas to cover the intended character, including clothing beyond the body silhouette. The pose adapter uses `end_percent = 1.0`, and both adapters have `preserve_wrapper = true` so their controls can be combined. These are example settings; adjust them to the scene.
See the [advanced workflow instructions](example_workflow/README.md#advanced-workflow-3d-pose--regional-control) for setup and saved settings.
## How to use
1. Use an Anima-compatible ComfyUI setup with [ComfyUI-Anima-LLLite](https://github.com/kohya-ss/ComfyUI-Anima-LLLite).
2. Download the adapter and place it in the `controlnet` model search path (`ComfyUI/models/controlnet/`, or an external directory configured for `controlnet`).
3. Load Anima Base 1.0, its compatible text encoder and VAE. In **Apply Anima ControlNet-LLLite (sd-scripts)** (`AnimaLLLiteApply_sdscripts`), select the adapter and connect the model and RGB control image.
4. Connect the patched model to the sampler and use your usual positive/negative prompts. Keep the control image's aspect ratio aligned with the output canvas.
For `anima_regional_4335`, start with **strength 1.0**, **start/end 0.0/1.0**, **30 sampling steps**, and **CFG 4**. Use the same seed when comparing results.
For `anima_3dpose_6200`, use a **shaded RGB 3D-mesh condition**. Native evaluation used strength/multiplier **1.0**, **30 sampling steps**, **CFG 4** and **flow shift 3**; see [model-specific usage](docs/anima_3dpose_6200.md#use-in-comfyui).
## Dataset
`anima_regional_4335` was trained on [toyxyz/booruregion_10k](https://huggingface.co/datasets/toyxyz/booruregion_10k): paired target images, RGB region maps, and captions. The training split contains **10,000 pairs** (3,834 Danbooru originals and 6,166 Anima-generated images), with **120 validation** and **120 test** pairs.
Full dataset provenance, measured training time, steps, batch size, architecture, parameters, file hashes, evaluation limits, and source terms are recorded in the [model-specific document](docs/anima_regional_4335.md) and its [training JSON](docs/anima_regional_4335.training.json).
`anima_3dpose_6200` was trained on [toyxyz/booru3dpose_10k](https://huggingface.co/datasets/toyxyz/booru3dpose_10k): **10,080 training pairs** (6,220 Anima-generated and 3,860 Danbooru originals), with **10 validation** and **10 test** pairs, both generated-only. Details, measured timings, full-state continuation, file SHA256 and limitations are in its [document](docs/anima_3dpose_6200.md) and [training JSON](docs/anima_3dpose_6200.training.json).
`anima_3dpose_outline_7000` was trained on [toyxyz/boorupose_outline_10k](https://huggingface.co/datasets/toyxyz/boorupose_outline_10k): **10,080 train / 10 validation / 10 test** pairs. The training targets, captions, splits and dimensions match the 3D pose dataset, while all conditioning images differ. Training used fresh 7,000 updates and matched target-camera meshes with nominal 4px complementary-color strokes. [Settings, pinned dataset revision, hashes and evaluation](docs/anima_3dpose_outline_7000.md).
## Acknowledgements
We sincerely thank [Kohya S.](https://github.com/kohya-ss) for the ControlNet-LLLite training and inference implementations, publicly released weights, and detailed technical documentation. These adapters were trained with [sd-scripts](https://github.com/kohya-ss/sd-scripts) and use [ComfyUI-Anima-LLLite](https://github.com/kohya-ss/ComfyUI-Anima-LLLite) for the shared ComfyUI examples.
We also thank [CircleStone Labs](https://huggingface.co/circlestone-labs) and [Comfy Org](https://github.com/Comfy-Org) for releasing the [Anima](https://huggingface.co/circlestone-labs/Anima) base model.
## License
These weights are derivatives of Anima and are subject to the [CircleStone Labs Non-Commercial License](https://huggingface.co/circlestone-labs/Anima/blob/main/LICENSE.md). Please refer to the linked upstream license for the full terms.