Nanosaur 2 Review: What a 670M Anime Image Model Can (and Can’t) Do

Updated October 12, 2026

Nanosaur 2 is an unusually small, open illustration model built to explore how far a low-compute text-to-image system can go. Its 670M-parameter diffusion transformer is easy to download and has a documented ComfyUI workflow, but the project maintainer explicitly says it should not be expected to match mature models such as Anima in character knowledge or fine detail.

The useful question is not whether it replaces a larger anime model. It is whether its small footprint, open workflow and LoRA training path make it a worthwhile local experiment. The evidence so far points to a promising research model for character sketches and constrained hardware, with weaker backgrounds, anatomy consistency and seed variety.

Quick verdict

Metric	Official information
Model	670M-parameter illustration DiT
Text encoder / VAE	Gemma 3 270M / semantic DINOv2 VAE (129M)
Baseline	Euler Simple, 50 steps, CFG 4
Training cost	About US$600, per maintainer
License / purpose	MIT listed; model card says research purpose

Nanosaur 2 is worth trying if you want a compact illustration model to run locally, inspect or fine-tune. The official model card documents a 670M DiT, a 270M Gemma 3 text encoder, a 129M semantic VAE, 1024-pixel aspect buckets and a 50-step Euler Simple baseline. The card says the research run cost about $600 in compute.

How the model works

The official Hugging Face card describes Nanosaur2-670M as a text-to-image diffusion transformer intended for illustration and research. It lists a frozen Gemma 3 270M text encoder and a semantic DINOv2 VAE. The maintainer describes a 4-million-image illustration dataset and says the model was trained from scratch with a low compute budget.

You can write natural-language prompts or use tag-style prompts. The recommended baseline is Euler Simple, 50 steps, CFG 4 and a 1024-pixel aspect bucket. The card suggests quality tags such as “newest, masterpiece” and says some artist tags may need weighting. These are starting settings, not a tested universal recipe.

Install the base model first, then compare the four-step checkpoint

Nanosaur 2 is not a single SDXL checkpoint. It needs its own loader, text encoder and VAE. Keep one clean baseline workflow so that a broken extension or a fast checkpoint does not get mistaken for a limitation of the original model.

1. Download the support folder and the three components from the maintainer’s repository. Copy the complete nanosaur2_support folder into ComfyUI/custom_nodes; avoid a second nested copy of the folder.
2. Put each component in the matching folder below. Restart ComfyUI, then drag nanosaur2_workflow.json onto the canvas. A downloaded sample PNG can also carry the workflow, but a recompressed image copied from social media may lose that metadata.
3. Select the exact model filenames in Nanosaur2Loader. Start with batch size 1 and the supplied resolution. Run the unmodified workflow before adding LoRAs, upscalers or other custom nodes.
4. For the accelerated version, download its separate diffusion checkpoint and load nanosaur2_dmad_4step_workflow.json. Keep the text encoder and VAE selected as shown in that workflow. Simply changing the ordinary model from 50 to 4 steps is not equivalent.

Download	ComfyUI destination
nanosaur2_diffusion_model.safetensors	models/diffusion_models
nanosaur2_text_encoder.safetensors	models/text_encoders
nanosaur2_vae.safetensors	models/vae
nanosaur2_support	custom_nodes

nanosaur2_workflow.json · DMAD 4-step checkpoint · nanosaur2_dmad_4step_workflow.json

Workflow	Sampler / scheduler	Steps / CFG	First-run resolution
Base model	Euler / simple	50 / 4	1024-class aspect bucket
DMAD four-step	LCM / simple	4 / 1	832 × 1216

The downloadable four-step JSON uses LCM / simple, 4 steps, CFG 1, denoise 1 and an 832 × 1216 latent. Its positive conditioning is connected to both sampler conditioning inputs. Load that complete graph rather than transplanting the base model’s CFG 4 and negative-prompt settings. The base card specifies Euler / simple, 50 steps, CFG 4 and an inference shift of 3 built into the model.

A small, repeatable prompt test

The following is a suggested baseline prompt, not a claimed test output. Keep one subject, one action and a simple background; add extra requirements only after the basic image works. Use the negative example with the base workflow.

newest, masterpiece, an adult woman explorer in a blue jacket, standing beside a quiet forest lake, medium shot, soft morning light, simple background, illustration

oldest, low quality

Compare four fixed seeds and inspect eyes, hands, clothing edges and the subject/background boundary at full size. Tags are useful for compact visual attributes; natural language is easier for a specific action. If a long prompt is ignored, remove competing details rather than piling on more quality tags. The model’s custom weighting affects cross-attention bias, so weights copied from another model need not behave identically.

If the workflow fails

• Missing Nanosaur2Loader: check custom-node startup errors and the folder placement, then restart.
• A missing model in the selector: check both the exact filename and its component folder. Do not put all three files in checkpoints.
• An out-of-memory error: reduce batch size to 1 and resolution before adding quantization. Model file size is not peak VRAM use; text encoding, attention, decoding and offloading also matter.
• Broken output after installing a patch: return to the original support folder and baseline workflow first. Quantized and inpainting variants have their own loader requirements.

What early users report

Community feedback is mixed but useful. In a September r/StableDiffusion discussion, one user said character rendering was impressive for the model size but described messy backgrounds, frequent duplicate composition and weak secondary characters. Another reported a 17-second 50-step generation on an 8GB RTX 3070; that is one user’s machine and settings, not a reproducible benchmark.

Small weights do not guarantee a fast or 2 GB workflow

The 670M figure counts the diffusion transformer, not the complete application. A simple two-byte-per-parameter estimate gives about 1.34 GB for those weights alone, before the 270M encoder, 129M VAE and runtime buffers. That estimate is not a measured VRAM requirement. The release thread discusses 4–8 GB cards and an 8 GB RTX 3070 run, but there is no maintainer-verified minimum GPU matrix. Older cards also need compatible PyTorch kernels and data types; “fits on disk” does not mean “runs well.”

The October 5 headline of seven images per second concerns a four-step configuration on an RTX 5090. It does not establish latency on a 4–8 GB GPU, and throughput must not be confused with the time to the first image, which includes loading and possibly compilation. Compare the same resolution, batch size and warm/cold state. Fewer sampling steps can accelerate iteration, but cannot guarantee identical detail or composition.

Reddit · 2026-09-26 · Reddit · 2026-10-05

Comparison with other image models

Model	Best fit	Evidence
Nanosaur 2 (670M)	Small local experiments, LoRA research	Small DiT; creator warns it trails Anima in character knowledge and detail
Anima 2.9B	Anime illustration and character knowledge	Larger; not tested head-to-head here
SDXL	General image generation and broad tooling	Different ecosystem; no matched benchmark found

Anima-2.9B is a third-party expansion and fine-tune of CircleStone Labs’ Anima, not simply another name for the original Anima. Its own card lists non-commercial terms. SDXL’s broad ecosystem is useful when compatible LoRAs and editing tools matter. Nanosaur’s case is strongest when model size, experimentation and a transparent ComfyUI graph are the deciding factors. This is an editorial use-case comparison, not a measured leaderboard.

HEIXUN/Anima-2.9B · SDXL 1.0

NSFW: availability, quality and hosted rules are different questions

The community launch presents Nanosaur 2 as uncensored, so adult illustration is part of the interest around the release. That description concerns the locally available model; it is not an official quality score. The maintainer’s card does not provide a controlled adult-content benchmark or a detailed moderation specification. The launch post’s dataset-size claim also differs from the current card, so the card is the better source for published training specifications.

For practical evaluation, the relevant questions are whether an adult-only request is followed, whether anatomy remains coherent and whether the result can be repeated across seeds. Public evidence is insufficient to give reliable pass rates for any of these. The same small-model limitations visible in ordinary illustrations may carry over. No controlled NSFW generation test was performed for this review. Local access does not determine what a cloud host or public gallery permits; check those rules before choosing a service.

Nanosaur2-670M · r/StableDiffusion

Download, online availability and real costs

The weights and workflows can be downloaded without a model purchase. There is no official subscription or per-image API price for this release. Local costs are storage, electricity and hardware; rented-GPU costs also include model download time, idle time and persistent storage. The creator’s approximately US$600 training budget is not a user price or a promise that your own training run will cost the same.

The third-party Luminia/Nanosaur2 Space exists, but its runtime was PAUSED when checked on October 12, 2026. It is a community demonstration, not an official always-on service. No maintainer-operated public API or active hosted inference offer was verified. For dependable access, use local ComfyUI or a cloud machine you configure yourself; check the host’s content rules separately.

The base repository lists MIT licensing and describes the project as research-oriented. Keep the license notice with redistributed software or weights where required. Model licensing does not automatically settle rights in source images, characters or outputs. Anima-2.9B, mentioned below, has a different non-commercial license: do not assume all downloadable illustration models share the same terms.

Nanosaur2-670M · Luminia/Nanosaur2 · INT8-ConvRot

LoRA, inpainting and VAE extensions

For a style or narrow subject experiment, the base repository includes train_lora.py. It accepts an image folder with same-name .txt captions and writes a LoRA under models/loras. The documented loader is LoraLoaderModelOnly. Training memory and time are separate from inference requirements; a successful low-VRAM generation does not establish that the same GPU can train comfortably.

The community Inpaint-ControlNet package by levzalt adds a masked-editing route. Its instructions require replacing the entire nanosaur2_support package with the supplied version, not installing duplicate copies. Load its inpainting workflow, provide the source image and mask, and use source dimensions divisible by 16. Its default example is 896 × 1152. This is a community extension, not a general guarantee that every ControlNet model works with Nanosaur.

The community GAN-VAE is another optional experiment. Its author reports small reconstruction improvements on a limited holdout that was also used to select the checkpoint. That is weaker evidence than a broad image-quality benchmark. Test it against the original VAE on the same latents; do not expect a decoder change to fix a misunderstood prompt or fundamentally wrong anatomy.

train_lora.py · Inpaint-ControlNet · GAN-VAE

uv run python custom_nodes/nanosaur2_support/train_lora.py /path/to/images

Who should use it?

Try it if you have a modest GPU, want to learn ComfyUI model integration, are exploring small diffusion architectures, or plan to train a focused LoRA. Its separate components and documented workflow make it a useful hands-on project.

Choose a larger, more mature illustration model when you need fine backgrounds, stable multi-character scenes, polished hands or dependable prompt adherence. Nanosaur 2 is most compelling as a compact research base and a low-cost way to experiment, not as a drop-in production replacement.

Practical questions

Is this an upgrade to Nano Banana?

No. Nanosaur2-670M is an independent small illustration model. The similar-sounding names do not indicate a shared developer or model family.

Which version should I download first?

Use the base files and standard workflow to establish a working installation. Then add the separate DMAD checkpoint and its four-step graph for a speed/quality comparison.

Can I use an SDXL LoRA or ControlNet?

Do not assume compatibility. Architecture-specific weights need matching support. Use a Nanosaur-specific LoRA or the documented community inpainting package.

Can it reliably draw text and logos?

It is not a strong choice for typography. The community launch discussion explicitly notes weak text rendering. Generate the illustration, then add accurate lettering in an editor.

Is an online API available?

No official priced API was verified. The identified community Space was paused on October 12, 2026; a model repository is not itself an online generation service.

Does the four-step speed claim apply to my GPU?

No. The prominent claim is from a 5090 configuration. Measure your own warm and cold runs at the same resolution and batch size; do not infer laptop speed from it.

Bottom line

Official Hugging Face model card and download

Nanosaur 2 is an interesting small-model experiment with a real ComfyUI workflow and an unusually low stated training budget. Its strengths are accessibility, transparency and fine-tuning potential; its limits are still visible in composition, detail and the lack of independent benchmarks.

If you want to learn from a small model or test a LoRA idea, it is worth a local run. If you need predictable, polished images, use a mature alternative and treat Nanosaur 2 as an experiment alongside it.