LAXMAYDAY/NOOB2-Project-Character-Reference-Bypass-Injector-Research

NOOB2 Project — Anima Native Reference V4 Scaled Mixture (49K)

Anima Native Reference V4 Scaled Mixture 49K is the latest integrated research checkpoint in this repository. It extends the earlier two-reference V2 E180 release with a text-slot competitive router and a mixed curriculum for:

• one-reference general image editing;
• one-reference anime-character generation;
• two-reference prompt-directed generation and editing.

Generation starts from pure noise. Reference images are encoded as native Anima VAE latent sequences; they are not used as an img2img initialization canvas. There is therefore no denoise-strength control. The reference route and router weights are integrated into the diffusion checkpoint: no external LoRA, IP-Adapter, or reference-adapter weight is required.

Research preview: the dual-reference and anime single-reference paths are the strongest parts of this checkpoint. General editing is materially better than a wiring-only model, but exact object counts and fine local structure can still fail. Treat this as an experimental release rather than a production image editor.

Latest V4 release

Item	Value
Release name	Native Reference V4 Scaled Mixture 49K
Integrated checkpoint	anima-v4-scaled-mix-50-30-20-e2-step49000-256area.safetensors
Checkpoint path	checkpoints/v4-scaled-49k/anima-v4-scaled-mix-50-30-20-e2-step49000-256area.safetensors
Checkpoint SHA-256	4500a4aad657e0d8e821607afe050b09931f84bf601ea1447ce2a52cca782e2f
Checkpoint bytes	4302295014
Optimizer updates	49,000
Epochs	2
Training geometry	256-pixel nominal area with aspect-ratio buckets
Reference inputs	One or two ordered images
Generation path	Pure-noise image-conditioned generation
External adapter/LoRA	None
Primary interface	Updated ComfyUI custom nodes and UI/API workflows
Status	Experimental research preview

The older V2 E180 checkpoint and its original workflows remain available under checkpoints/v2-e180/ and the historical V2 paths. They are not overwritten by this release.

What changed from V2 E180

V4 retains the integrated V2 native-reference latent/attention route, then adds an integrated competitive text-slot router. For active references, the router uses canonical prompt clauses aligned to the positive prompt's raw Qwen token sequence. It can route one physical reference to logical slot 0 or slot 1 and can route two ordered references independently.

For classifier-free guidance, the positive and negative denoising branches use their own standard Anima text conditions, while both branches share the positive prompt's router-only Qwen context, Qwen attention mask, and reference clause masks. This split is required for the trained V4 checkpoint and is implemented by the V4 runtime; a V2-only runtime is not compatible.

The two slots still have no permanently assigned scene, identity, pose, style, foreground, or background roles. The prompt determines what should be taken from each present image.

Supported V4 reference layouts

Present inputs	Logical slot IDs	Prompt requirement
Reference Image 1 only	[0]	One canonical clause bound to Image 1
Reference Image 2 only	[1]	One canonical clause bound to Image 2
Both images	[0, 1]	Distinct canonical clauses for Image 1 and Image 2

The runtime must preserve the physical image-to-slot mapping exactly. A single image connected to slot 1 must remain logical slot 1; it must not be silently renumbered to slot 0.

Single-reference prompt example

Use Image 1 as the character identity reference. Generate the same character
in a new standing pose, wearing a dark school uniform, in a clean anime
illustration with detailed eyes.

Two-reference prompt example

Use Image 1 as the composition and pose reference. Use Image 2 as the character
identity and clothing reference. Generate a coherent new anime illustration
that follows the pose from Image 1 and the character from Image 2.

V4 competitive routing fails closed when references are present but the prompt binding is missing, ambiguous, non-canonical, or truncated. The updated ComfyUI nodes should surface that validation error rather than silently run an unbound reference route.

Training mixture

One 10-update task pattern was repeated with an exact 5:3:2 ratio:

Task	Optimizer updates	Share	Training manifest
General one-reference edit	24,500	50%	97,982 rows
Anime one-reference character generation	14,700	30%	58,700 rows
Ordered two-reference generation/edit	9,800	20%	10,127 transport rows / 5,408 logical groups

The general-edit portion uses the selected GPT-Image-Edit data. The single-reference character portion is derived from the recovered anime image collection with exact-character grouping and within-label filtering. The two-reference portion uses the GPT-generated LAXMAY subset. Flux-generated targets were not used in this 49K run.

Single-reference samples were placed into logical slot 0 and slot 1 with exact 50/50 closure. Two-reference samples preserved their ordered [0,1] mapping. The training run used batch size 8, BF16, two epochs, and 49,000 optimizer updates on one NVIDIA RTX PRO 6000.

Only the integrated native-reference/router parameter set was trainable. The postflight compared 688 frozen non-native tensors bit-for-bit against the warm start and confirmed that they did not change.

V4 ComfyUI assets

Install the updated package from:

comfyui/releases/v0.2.0-v4-49k/

The new package must coexist with the legacy V2 class IDs so old workflows continue to load. Use AnimaNativeRefV4Loader with AnimaNativeRefV4Generate1Ref or AnimaNativeRefV4Generate2Refs for the 49K checkpoint; the legacy AnimaNativeRefV2Loader and AnimaNativeRefV2Generate nodes remain for E180 only.

Validated workflows:

comfyui/workflows/v4-49k/anima_ref_v4_final_single_slot0.json
comfyui/workflows/v4-49k/anima_ref_v4_final_single_slot1.json
comfyui/workflows/v4-49k/anima_ref_v4_final_dual.json
comfyui/workflows/v4-49k/api/anima_ref_v4_final_single_slot0_api.json
comfyui/workflows/v4-49k/api/anima_ref_v4_final_single_slot1_api.json
comfyui/workflows/v4-49k/api/anima_ref_v4_final_dual_api.json

V4 quick start

1. Download the ZIP or tar.gz, then extract ComfyUI-Anima-Native-Reference to ComfyUI/custom_nodes/ComfyUI-Anima-Native-Reference.
2. With the Python interpreter used by ComfyUI, run python -m pip install -r ComfyUI/custom_nodes/ComfyUI-Anima-Native-Reference/requirements.txt.
3. Download the V4 checkpoint to ComfyUI/models/diffusion_models/; place qwen_3_06b_base.safetensors in ComfyUI/models/text_encoders/ and qwen_image_vae.safetensors in ComfyUI/models/vae/.
4. Restart ComfyUI.
5. Load the matching UI workflow: slot 0, slot 1, or dual reference. Machine-readable graphs are in the API workflow directory.

The official workflows set verify_release_sha256=true. The first load reads and hashes the complete checkpoint, Qwen encoder, and VAE, so it incurs extra one-time storage I/O before the cached runtime is ready.

Do not use the legacy V2-only runtime with the V4 checkpoint. That runtime requires exactly two images and does not carry the raw-Qwen/source-mask/clause- mask inputs required by the competitive router.

V4 validated sampling preset

The checkpoint-time evaluation used:

Setting	Value
Output size	256 × 256
Denoising steps	30
CFG	3.5
Flow shift	5.0
Native reference scale	1.0
Maximum area per reference	65,536 pixels

The training data retained original source images and used online aspect-ratio bucketing. The released quality claim is nevertheless limited to the 256-pixel nominal-area regime. Larger output sizes should be treated as experimental until separately trained and evaluated.

V4 qualitative progression

This board holds prompts and seeds fixed while showing the same training run at steps 12.25K, 24.5K, 36.75K, and 49K. It is a within-run training progression, not a cross-model A/B comparison.

V4 limitations

• Exact object count and small local edit instructions may fail.
• Fine facial details, ornaments, hands, logos, and text may drift.
• Two references can still leak pose, clothing, background, or style into the wrong requested role.
• Multiple characters can cause identity mixing.
• The model can over-copy one reference or under-use the other.
• Output quality varies by seed and prompt phrasing.
• Photorealistic identity transfer is outside the primary target domain.
• This release does not guarantee exact reconstruction of a reference image.

---

Legacy release: Anima Native Reference V2 (E180)

Anima Native Reference V2 E180 is an experimental two-reference, image-conditioned generation checkpoint developed as part of the NOOB2 Project — Character Reference Bypass Injector Research.

The model generates a new anime illustration from pure noise while conditioning on:

• a text prompt;
• Reference Image 1, assigned to ordered slot 0;
• Reference Image 2, assigned to ordered slot 1.

The two reference slots do not have permanently assigned scene, character, identity, source, or style roles. Their order is preserved exactly, while the prompt determines how the model should interpret and combine them.

The Native Reference V2 modules are already integrated into the E180 diffusion checkpoint. No external LoRA, IP-Adapter, or separate reference-adapter weight file is required.

Research preview: This repository contains an experimental checkpoint, ComfyUI integration, reproducible workflows, and validation artifacts. It is not yet intended as a final production release.

About the “Bypass Injector” Name

In this project, bypass injector refers to an auxiliary visual-conditioning path that injects ordered reference features into the diffusion model through its native reference sequence and reference-attention modules.

It does not refer to bypassing safety systems, content filters, platform restrictions, or access controls.

Release Information

Item	Value
Model version	Native Reference V2 E180
Base architecture	Anima
Integrated checkpoint	anima-native-ref-v2-e180-step64080-256px.safetensors
Checkpoint step	64,080
Training/validation bucket	256-pixel-area buckets
Checkpoint size	4,271,362,542 bytes
Reference inputs	Exactly two ordered images
Generation path	Pure-noise text-to-image generation
Primary interface	ComfyUI
Status	Experimental research preview

Key Properties

• One integrated Native Reference V2 diffusion checkpoint
• Two explicitly ordered reference-image inputs
• Prompt-defined reference semantics
• Pure-noise generation rather than img2img
• Native V2 reference-latent sequence
• Native V2 reference-attention route
• No denoise-strength control
• No external LoRA or reference-adapter weight
• No stock ComfyUI KSampler path
• UI and local /prompt API workflows included
• Bounded runtime, prompt-embedding, and reference-latent caches
• Optional release SHA-256 verification

Model Architecture

This release uses the following integrated diffusion checkpoint:

anima-native-ref-v2-e180-step64080-256px.safetensors

This file is the diffusion model used for generation. It replaces the stock Anima diffusion-model checkpoint in this workflow.

Do not load an additional stock Anima diffusion checkpoint alongside it.

The following auxiliary Anima assets are still required:

qwen_3_06b_base.safetensors
qwen_image_vae.safetensors

The complete runtime consists of:

Integrated E180 Native Reference V2 DiT
+ Anima Qwen3-0.6B text encoder
+ Qwen-Image VAE

The two reference images are encoded by the VAE into two ordered reference latent streams. These streams are supplied to the diffusion model through its integrated Native Reference V2 sequence and reference-attention modules.

Generation starts from pure noise. The reference images are not used as an initial denoising canvas.

Consequently:

• this is not a conventional img2img workflow;
• there is no denoise-strength parameter;
• the workflow does not use the stock ComfyUI KSampler;
• the workflow does not load an external LoRA;
• the workflow does not load an external IP-Adapter;
• the workflow does not load a separate learned reference adapter.

A custom inference plugin is still required because the standard Anima runtime does not implement the additional Native Reference V2 modules contained in the integrated checkpoint.

Reference Semantics

The two reference inputs are defined only by their order:

Reference Image 1 → ordered slot 0
Reference Image 2 → ordered slot 1

The slots do not have hard-coded semantic roles.

They are not permanently defined as:

• scene and character;
• source and target;
• identity and style;
• foreground and background.

Instead, the prompt defines how the two references should be interpreted.

For example:

Use the first image as the scene template and the second image as the
character template. Generate an edited result where the character matches
Image 2 and the scene remains faithful to Image 1.

A different prompt may assign entirely different roles to the same two ordered slots.

The model preserves the input order, but the prompt determines the requested relationship between the images.

Included ComfyUI Nodes

The release contains two primary custom nodes.

Node	Purpose
Anima Reference V2 Loader (E180)	Loads and caches the integrated E180 checkpoint, Anima Qwen3-0.6B text encoder, and Qwen-Image VAE.
Anima Reference V2 Generate (2 Refs)	Generates one image from a prompt and two explicitly ordered reference images.

Loader output

The Loader returns a custom pipeline object:

ANIMA_NATIVE_REF_V2_PIPELINE

It does not expose conventional ComfyUI MODEL, CLIP, and VAE outputs.

Generate inputs

The Generate node accepts:

• the loaded Native Reference V2 pipeline;
• Reference Image 1, ordered slot 0;
• Reference Image 2, ordered slot 1;
• positive prompt;
• negative prompt;
• seed;
• output width and height;
• sampling steps;
• CFG;
• flow shift;
• native reference scale;
• maximum preprocessing area for each reference.

The first release requires exactly two reference inputs.

Each input currently accepts exactly one image:

IMAGE [1, H, W, 3]

Batched inputs with B > 1 are rejected. For an animated or batched input, select one frame first with ImageFromBatch.

Repository Contents

.
├── checkpoints/
│   └── v2-e180/
│       └── anima-native-ref-v2-e180-step64080-256px.safetensors
│
├── comfyui/
│   ├── custom_nodes/
│   │   └── ComfyUI-Anima-Native-Reference/
│   ├── examples/
│   │   ├── anima_ref_v2_e180-workflow-thumbnail.jpg
│   │   └── e180-heldout-000052-comfy-output.png
│   ├── releases/
│   │   └── v0.1.0-e180/
│   ├── workflows/
│   │   ├── anima_ref_v2_e180.json
│   │   └── anima_ref_v2_e180_api.json
│   ├── MANIFEST.json
│   ├── README.md
│   ├── SHA256SUMS
│   └── TEST_REPORT.md
│
├── .gitattributes
└── README.md

Required Model Files

Place the required files in the following directories:

ComfyUI/
└── models/
    ├── diffusion_models/
    │   └── anima-native-ref-v2-e180-step64080-256px.safetensors
    ├── text_encoders/
    │   └── qwen_3_06b_base.safetensors
    └── vae/
        └── qwen_image_vae.safetensors

1. Integrated E180 checkpoint

File:

anima-native-ref-v2-e180-step64080-256px.safetensors

Repository path:

checkpoints/v2-e180/anima-native-ref-v2-e180-step64080-256px.safetensors

Destination:

ComfyUI/models/diffusion_models/

Size:

4,271,362,542 bytes

SHA-256:

1f970a7867dd7b65858d30b58135134ce84fd3b552ab27fc9f07e7f15209c6dd

This is the complete diffusion-model checkpoint used by the workflow.

Do not load a separate stock Anima diffusion checkpoint in addition to this file.

2. Anima text encoder

File:

qwen_3_06b_base.safetensors

Source:

Anima text encoders

Destination:

ComfyUI/models/text_encoders/

Size:

1,192,135,096 bytes

SHA-256:

cd2a512003e2f9f3cd3c32a9c3573f820bb28c940f73c57b1ddaa983d9223eba

3. Qwen-Image VAE

File:

qwen_image_vae.safetensors

Source:

Anima VAE files

Destination:

ComfyUI/models/vae/

Size:

253,806,246 bytes

SHA-256:

a70580f0213e67967ee9c95f05bb400e8fb08307e017a924bf3441223e023d1f

Requirements

• A current ComfyUI installation
• Python 3.10 or newer
• NVIDIA CUDA GPU
• BF16 support
• Sufficient system RAM for the cached runtime
• The exact E180, Qwen, and VAE files listed above

The first release deliberately validates the expected checkpoint layouts instead of silently loading similarly named but incompatible files.

Installation

1. Install the custom node

Copy:

comfyui/custom_nodes/ComfyUI-Anima-Native-Reference/

into:

ComfyUI/custom_nodes/ComfyUI-Anima-Native-Reference/

Alternatively, use one of the packaged releases under:

comfyui/releases/v0.1.0-e180/

2. Install dependencies

Use the same Python interpreter that starts ComfyUI:

cd ComfyUI/custom_nodes/ComfyUI-Anima-Native-Reference
python -m pip install -r requirements.txt

The package does not pin or install PyTorch.

Do not replace an existing working ComfyUI CUDA installation of torch or torchvision.

3. Restart ComfyUI

Restart ComfyUI after installing the custom node and dependencies.

Workflows

Standard ComfyUI workflow

Load:

comfyui/workflows/anima_ref_v2_e180.json

Local API workflow

Use:

comfyui/workflows/anima_ref_v2_e180_api.json

The UI workflow and API workflow use different ComfyUI JSON formats. They should not be treated as interchangeable files.

Basic Usage

1. Install the custom node and restart ComfyUI.
2. Place the integrated E180 checkpoint in models/diffusion_models/.
3. Place Qwen3-0.6B in models/text_encoders/.
4. Place the Qwen-Image VAE in models/vae/.
5. Load the provided standard ComfyUI workflow.
6. Select one image in the Reference Image 1 node.
7. Select one image in the Reference Image 2 node.
8. Write a prompt that explicitly describes how the two references should be used.
9. Select the validated generation settings.
10. Queue the workflow.

Do not load a separate Anima base diffusion model. The integrated E180 checkpoint is the diffusion model used by this workflow.

Prompt Examples

General two-reference generation

Create a new anime illustration using both reference images. Preserve the
relevant visual information from Reference Image 1 and Reference Image 2 while
following this instruction: show the referenced character in a coherent new
composition with clean anime rendering and detailed eyes.

Scene from Image 1 and character from Image 2

Use the first image as the scene template and the second image as the
character template. Generate an edited result where the character matches
Image 2 and the scene remains faithful to Image 1.

Character design from both references

Create a new anime illustration that combines the character identity and
costume details shown across both reference images. Preserve the most
recognizable facial features, hairstyle, colors, and accessories while
placing the character in a new full-body composition.

These prompts are examples only. They do not permanently assign semantic roles to either reference slot.

Validated Settings

Setting	Value
Output size	256 × 256
Steps	40
CFG	1.0
Flow shift	5.0
Native reference scale	1.0
Maximum area per reference	65,536 pixels
Ordered slot IDs	[0, 1]
Attention mode	torch
Default offload mode	balanced

The checkpoint was trained and validated with 256-pixel-area buckets.

Larger dimensions that are multiples of 16 are accepted by the current implementation, but they should be treated as experimental rather than as a validated quality guarantee.

Memory Modes

Mode	Behavior
balanced	Keeps the DiT on the GPU and moves Qwen and the VAE to the GPU only during their respective phases.
high_vram	Keeps the DiT, Qwen, and VAE on the GPU for faster repeated generation.
text_encoder_cpu	Runs text encoding on the CPU as a slower fallback for constrained GPU memory.

The plugin maintains bounded caches for:

• loaded model runtimes;
• prompt embeddings;
• preprocessed reference latents.

Changing the prompt, seed, reference images, or sampling settings does not normally require reading the 4.27 GB checkpoint from disk again.

Verified Held-Out Output

The image above is the generated-only result for held-out integration case 000052.

Prompt:

Use the first image as the scene template and the second image as the
character template. Generate an edited result where the character matches
Image 2 and the scene remains faithful to Image 1.

Generation settings:

Setting	Value
Seed	20260720
Resolution	256 × 256
Steps	40
CFG	1.0
Flow shift	5.0
Native reference scale	1.0
Reference maximum area	65,536
Ordered slots	[0, 1]

The release package contains the generated frame rather than a combined Reference 1 / Reference 2 / Generated comparison sheet.

It should therefore be interpreted as a verified integration and reproducibility artifact, not as a complete standalone qualitative comparison.

Integration Verification

The packaged runtime was validated using the exact release files on an NVIDIA RTX PRO 6000 Blackwell.

The validation covered:

• loading the complete integrated E180 checkpoint;
• loading the Qwen3-0.6B text encoder;
• loading the Qwen-Image VAE;
• two-reference pure-noise generation;
• fixed ordered slots [0, 1];
• the native V2 reference sequence;
• the native V2 reference-attention route;
• prompt and reference-latent caching;
• repeated runtime reuse;
• UI workflow schema validation;
• local /prompt API execution;
• generated-image saving;
• Unicode Chinese and Japanese prompt preservation.

The generated ComfyUI output for held-out case 000052 was pixel-identical to the accepted native command-line result when decoded to RGB pixels:

Maximum absolute pixel difference: 0
Mean absolute pixel difference:    0
Nonzero channel values:            0

Pixel SHA-256:

20ba2661e8667bea3448c05021d5694d470474f7d39105ef1ee54096c394d04f

These results verify runtime and workflow parity. They should not be interpreted as a comprehensive benchmark of character-reference quality.

Intended Uses

This release is intended for research and experimental creative workflows involving:

• anime character-reference conditioning;
• two-reference character and scene composition;
• character appearance transfer;
• costume and accessory reference;
• prompt-controlled changes to pose and composition;
• visual-development workflows;
• evaluation of native multi-reference conditioning;
• research into alternatives to per-character LoRA training;
• development and testing of Anima Native Reference V2 inference.

Out-of-Scope Uses

This release is not designed to guarantee:

• exact identity reconstruction;
• biometric identification;
• exact face replication;
• reliable photorealistic identity transfer;
• exact reproduction of small accessories;
• exact reproduction of complex clothing;
• reliable multi-character identity assignment;
• accurate text or logo rendering;
• unrestricted production deployment;
• compatibility with arbitrary Anima checkpoints;
• compatibility with unrelated diffusion architectures.

Known Limitations

• Exactly two reference inputs are supported.
• Each reference input currently accepts one image only.
• Reference slots are ordered but have no automatically inferred semantic roles.
• The release accepts the exact E180 Native Reference V2 model layout.
• Arbitrary stock Anima or future Native Reference checkpoints are not automatically supported.
• CUDA BF16 is required by the initial release.
• The plugin does not expose the conventional ComfyUI MODEL / CLIP / VAE / KSampler workflow path.
• The trained and validated output scale is based on 256-pixel-area buckets.
• Larger outputs remain experimental.
• Fine facial features may drift.
• Hair ornaments and small accessories may be changed or omitted.
• Clothing information from the two references may become mixed.
• Complex backgrounds may leak into the generated image.
• Extreme poses or camera angles may reduce character consistency.
• Multiple characters may lead to identity mixing.
• The generated composition may over-copy one of the references.
• Output quality may vary between random seeds.
• Photorealistic references are outside the primary target domain.
• The model does not guarantee exact reconstruction of either reference.

Responsible Use

Only use reference images that you own or are authorized to use.

Do not use this model to:

• impersonate real people;
• misrepresent generated content as authentic evidence;
• harass or defame individuals;
• violate privacy or publicity rights;
• reproduce private or sensitive imagery without permission;
• infringe copyright or contractual restrictions;
• create illegal or exploitative content.

This model is not a safety classifier and does not guarantee that generated outputs will be appropriate, accurate, or lawful.

Users are responsible for applying suitable content moderation, human review, and access controls in downstream applications.

Runtime Provenance

The Native Reference V2 inference implementation includes a pinned and namespaced runtime snapshot derived in part from:

akatsuki-neo/anima-edit

Audited source commit:

2ae811d296ff4159c6024c4a86415d19961a388c

The vendored runtime:

• contains no model weights;
• uses a private namespace to avoid collisions with other custom nodes;
• retains upstream provenance and licensing information;
• is verified against a per-file integrity manifest before import.

License

Integrated Model Checkpoint

The integrated E180 checkpoint is distributed separately from the custom-node source code.

Its use remains subject to:

• the terms published with this repository;
• the applicable terms of the underlying Anima model and assets;
• the repository access conditions;
• any other applicable third-party licenses.

Access to the gated repository does not grant rights beyond the applicable license terms.

Users should review the current upstream and repository licenses before:

• redistributing the checkpoint;
• creating or distributing derivative models;
• hosting a public inference service;
• using the model commercially.

Required Anima Assets

The Qwen3 text encoder and Qwen-Image VAE are downloaded from the Anima repository and remain subject to their corresponding upstream terms.

Custom Node and Runtime Code

The packaged ComfyUI integration and its vendored inference-source components are provided under the licenses included in their respective LICENSE files.

The included runtime source retains the Apache-2.0 license and upstream attribution where applicable.

A software license applying to the inference code does not override the license governing the model checkpoint or required upstream model assets.

Access and Integrity

The model repository may require users to:

• sign in to Hugging Face;
• accept the repository access conditions;
• provide the information requested by the repository owner.

Keep Hugging Face access tokens outside workflow JSON files and scripts.

Checksum files are included for validating release artifacts:

comfyui/SHA256SUMS
comfyui/custom_nodes/ComfyUI-Anima-Native-Reference/SHA256SUMS
comfyui/releases/v0.1.0-e180/SHA256SUMS
comfyui/releases/v0.2.0-v4-49k/SHA256SUMS

Verify downloaded files before installation or redistribution.

Acknowledgements

This project was made possible through the contributions of many developers, researchers, and testers.

• Yidhar and Nebulae contributed to model training and dataset development.
• spawner provided important inspiration and portions of the code through the akatsuki-neo/anima-edit project.
• potato contributed to the early development of the project.
• GHOSTLXH, 年糕特工队, 轻松, Free Will, and 韩小强 contributed testing, feedback, and early validation.

We also thank CircleStone Labs and Comfy Org for the development and release of Anima, and the broader ComfyUI developer community for the inference framework and ecosystem.

Special thanks to Comfy.org for providing GPU sponsorship.

Project Credits

• Project: NOOB2 Project
• Model and integration release: LAXMAYDAY / Laxhar Lab
• Base architecture: Anima
• Primary interface: ComfyUI

Citation

@misc{noob2_anima_native_reference_v2_2026,
  title        = {NOOB2 Project: Anima Native Reference V2 E180},
  author       = {LAXMAYDAY and Laxhar Lab},
  year         = {2026},
  howpublished = {Hugging Face model repository},
  note         = {Experimental two-reference native-conditioning checkpoint for Anima}
}

Changelog

v0.2.0-v4-49k

• Added the integrated V4 Scaled Mixture 49K checkpoint.
• Added one-reference slot-0 and slot-1 generation nodes.
• Added prompt-directed dual-reference competitive routing.
• Added three UI workflows and three /prompt API workflows.
• Retained the V2 E180 checkpoint, class IDs, workflows, and release.

v0.1.0-e180

• Released the integrated Native Reference V2 E180 checkpoint.
• Added two explicitly ordered reference-image inputs.
• Added prompt-defined reference semantics.
• Added the Native Reference V2 Loader node.
• Added the Native Reference V2 Generate node.
• Added the standard ComfyUI workflow.
• Added the local /prompt API workflow.
• Added balanced, high-VRAM, and CPU text-encoder modes.
• Added bounded runtime and conditioning caches.
• Added fail-closed checkpoint-layout validation.
• Added optional release SHA-256 verification.
• Added runtime and workflow validation tests.
• Added a verified held-out generated output.
• Added pixel-exact comparison against the accepted native CLI result.
• Added manifests, provenance records, and release checksums.