--- title: Anima_Tile_and_Repair_ControlNet-LLLite canonical_url: "https://www.modelscope.cn/models/LaxharLAB/Anima_Tile_and_Repair_ControlNet-LLLite" md_url: "https://www.modelscope.cn/models/LaxharLAB/Anima_Tile_and_Repair_ControlNet-LLLite.md" repository: LaxharLAB/Anima_Tile_and_Repair_ControlNet-LLLite chinese_name: "Anima Tile & Repair ControlNet-LLLite" last_updated: 2026-06-17 pipeline_tag: image-deblurring tasks: - image-deblurring parameters: 23.5M tensor_type: - F32 library_name: - safetensors - pytorch frameworks: - Pytorch downloads: 6127 stars: 25 --- # Anima_Tile_and_Repair_ControlNet-LLLite > Anima_Tile_and_Repair_ControlNet-LLLite - LaxharLAB 在 ModelScope 开源的模型。This model is a Tile & Repair ControlNet-LLLite model for Anima, trained with part of the edited image-pair data from the Noob v2 project, and designed for anime image restoration, tiled… - **Repository**: LaxharLAB/Anima_Tile_and_Repair_ControlNet-LLLite - **Tasks**: image-deblurring - **Parameters**: 23.5M - **Downloads**: 6127 - **Stars**: 25 - **Last updated**: 2026-06-17 Source: https://www.modelscope.cn/models/LaxharLAB/Anima_Tile_and_Repair_ControlNet-LLLite --- # Anima Tile & Repair ControlNet-LLLite A lightweight **ControlNet-LLLite** model for the **Anima** model family, designed for anime image repair, tile-based enhancement, and restoration-style image-to-image workflows. The training data includes part of the edited image-pair data from the Noob v2 project. This model helps Anima restore blurry, low-quality, or degraded anime images while preserving the original composition, character structure, pose, and scene layout as much as possible. > This is not a standalone image generation model. It should be used together with an Anima-compatible checkpoint and inference workflow. --- ## Model Information | Item | Description | |---|---| | Model Name | Anima Tile & Repair ControlNet-LLLite | | Model Type | ControlNet / ControlNet-LLLite | | Base Model | Anima | | Version | v1.0 | | Format | SafeTensor | | Primary Use Case | Anime image repair, tiled detail enhancement, and restoration | | Recommended Checkpoint | `anima-base-v1.0.safetensors` | ---
## What This Model Does This model is intended to guide Anima during image-to-image repair and restoration tasks. Typical use cases include: - Repairing blurry anime images - Restoring low-quality or degraded anime images - Reducing compression artifacts and local visual noise - Enhancing tiled image details - Improving anime line art and local texture consistency - Preserving the original composition, pose, and scene layout - Producing cleaner and sharper anime-style outputs without heavily changing the source image The v1 release is mainly optimized for blur-damaged images and general low-quality anime restoration tasks. --- ## Gallery / Visual Examples The following images are sample results and visual examples included in this repository.

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SAMPLE3.png ## Recommended Checkpoint For regular inference, the recommended Anima checkpoint is: ```text anima-base-v1.0.safetensors ``` Earlier checkpoints may be used for comparison, but the final v1 Anima base checkpoint is recommended for normal use. --- ## Usage There are two main ways to use this model: 1. Python inference with the Anima ControlNet-LLLite script 2. ComfyUI workflow with `ControlNet-LLLite_node` --- ## Python Inference Example Example command: ```bash python anima_minimal_inference_control_net_lllite.py \ --dit /path/to/anima_dit_or_model \ --vae /path/to/qwen_image_vae \ --text_encoder /path/to/qwen3_text_encoder \ --lllite_weights /path/to/anima_tiled_lllite_v1.safetensors \ --control_image /path/to/input_image.png \ --prompt "restore this anime image with clean details, sharp line art, and natural texture" \ --image_size 1024 1024 \ --infer_steps 50 \ --guidance_scale 3.5 \ --lllite_multiplier 1.0 \ --save_path ./outputs/ ``` --- ## Batch Inference Example You can also use a prompt file for batch inference: ```bash python anima_minimal_inference_control_net_lllite.py \ --dit /path/to/anima_dit_or_model \ --vae /path/to/qwen_image_vae \ --text_encoder /path/to/qwen3_text_encoder \ --lllite_weights /path/to/anima_tiled_lllite_v1.safetensors \ --control_image /path/to/default_control_image.png \ --from_file prompts.txt \ --save_path ./outputs/ ``` Example `prompts.txt` line: ```text restore this blurry anime image with clean line art and improved details --w 1024 --h 1024 --d 42 --cn images/input_001.png --am 0.8 ``` --- ## Important Parameters | Parameter | Description | |---|---| | `--lllite_weights` | Path to the ControlNet-LLLite `.safetensors` file | | `--control_image` | Path to the control / reference image | | `--lllite_multiplier` | ControlNet-LLLite guidance strength | | `--cn` | Per-prompt control image override in batch mode | | `--am` | Per-prompt multiplier override in batch mode | Recommended starting range: ```text --lllite_multiplier 0.8 ~ 1.0 ``` If the repair effect is too weak, increase the multiplier slightly. If the result becomes too sharp, too constrained, or changes fine details too much, lower the multiplier. --- ## ComfyUI Usage This model can be used in ComfyUI with `ControlNet-LLLite_node`. Basic workflow concept: ```text Anima base model + ControlNet-LLLite_node + anima_tiled_lllite_v1.safetensors + input / control image = repaired Anima output ``` Some testers reported slight color shift when using the ComfyUI node. This issue was not observed in the Python inference path, so it may be related to the node implementation or specific workflow settings. If you encounter color shift, compare the result with the Python inference path and report the issue together with your workflow settings. --- ## Suggested Prompts You can start with prompts like: ```text restore this anime image with clean details, sharp line art, and natural texture ``` ```text repair the low-quality anime image, reduce blur and compression artifacts, preserve the original composition ``` ```text enhance the image details, clean up artifacts, keep the character structure and scene layout unchanged ``` ```text restore fine anime line art and local details while keeping the original pose, composition, and colors stable ``` --- Communication QQ Groups: 1080876483 531021130 635772191 956810411 519382846 Discord: Laxhar Dream Lab SDXL NOOB https://discord.com/invite/DKnFjKEEvH --- ## Training Information The v1 model was trained with A100 GPU resources. The model focuses on anime-style restoration and tile / repair guidance, and is optimized for Anima workflows rather than general photographic restoration. --- ## Limitations This is a v1 release, so there are still some limitations: - It is mainly designed for anime images. - It may not work well on realistic photos. - Very strong guidance may over-sharpen details. - Very strong guidance may make the output too constrained. - Some heavily degraded images may still require stronger restoration or future model versions. - ComfyUI node output may show slight color shift in some workflows. --- ## Roadmap A v2 version is planned / in training with a larger and more diverse dataset. Future versions are expected to improve robustness on: - Blurry images - Low-resolution images - Low-quality degraded images - More diverse tile and repair tasks - Stronger detail recovery while preserving the original layout --- ## Credits Special thanks to **Comfy.org** for providing GPU sponsorship. Thanks also to the volunteers who contributed testing and feedback: - Yidhar - GHOSTLXH - 年糕特工队 - 轻松 - Free Will Their feedback helped improve the training and release process. --- ## License / Usage Please follow the license and usage terms of Anima and the related ecosystem components. This model is released as an auxiliary ControlNet-LLLite guidance model for Anima-compatible workflows. --- ## Source Original model page: ```text https://civitai.red/models/2708551/anima-tile-and-repair-controlnet-lllite ```