Anima is a 2-billion-parameter anime model built with Comfy Org, trained on millions of anime images and 800k non-anime artworks with no synthetic data - no AI-generated images were used to train it. This episode runs the whole path: text-to-image, image-to-image anime conversion driven by a Qwen3-VL caption pass, and tiled upscale. The model is a preview build, not an accelerated one, and it is small enough that the author reckons 6 GB of VRAM, possibly even 4 GB, can run it locally.

Evidence note: parameters below were parsed from the workflow JSON shipped with this episode (3 files); anything the video reports about results is attributed to the author.

When this is useful

  • You want an anime model that was not trained on AI-generated images.
  • You need to convert a photo or 3D render into anime style with denoise control.
  • Your target size exceeds what the model handles natively, so you need tiled upscale.

How the pipeline is built

The model itself

2B parameters, trained jointly with Comfy Org on millions of anime images plus 800k non-anime art images, zero synthetic data. In 2025 terms 2B is not large; the selling point is the data policy.

It is a preview build and explicitly not an accelerated model, so it needs a real step budget. The author expects a more stable release later.

The author reads it as NSFW-capable ("should support NSFW"), and frames it as worth trying for anime illustrators.

Text encoder and prompt language

The text encoder is Qwen3, and the author recommends English prompts for accuracy.

Native text-to-image size is limited, which is precisely why the upscale stage is part of the pipeline rather than an optional extra.

Image-to-image: denoise is the dial

For conversion the author lands on a denoise around 0.7 as the upper useful value: higher changes more, lower changes less.

A 1080p input fed straight in produces artifacts, so the image has to be passed through a pixel constraint before encoding. With that constraint the results are reasonable.

Captions come from a Qwen3-VL reverse-caption pass (the author calls it Tesser-style reverse prompting), which is what makes photo-to-anime conversion controllable without hand-written prompts.

Tiled upscale done carefully

The upscale is the usual tile pipeline: set the target size by pixels, split into roughly four tiles, and caption each tile from the reverse prompt.

The author raised the tile overlap to 0.35 specifically to stop seams appearing on this model.

Noise must stay low during the upscale pass or the tiles come out strange. After stitching, the author reports no obvious seams and a visible gain in detail.

Prompt style is flexible: natural language or tag-style both work, and the project page provides prompting guidance.

How to run it

  1. Open the text-to-image graph; write the prompt in English and check the subject details against the output.
  2. For conversion, open the image-to-image graph, load the source, and apply a pixel constraint before encoding.
  3. Set denoise around 0.7 - higher for more change, lower for less.
  4. Run a Qwen3-VL reverse-caption pass to get the prompt, then sample.
  5. Open the tiled-upscale graph, set the target size in pixels, split into about four tiles, raise overlap to 0.35, and keep noise low.
  6. Stitch and inspect the seams.

Parameter basis

Anima分块放大 - 26 nodes

Item Value (from JSON)
UNET loader anima-preview.safetensors / default
CLIP loader qwen_3_06b_base.safetensors / stable_diffusion
VAE qwen_image_vae.safetensors
KSampler steps 31 · cfg 4 · er_sde · simple · denoise 0.25
LoRA Gundam_RX78_Flux.safetensors @ 0.85

Anima图生图 - 13 nodes

Item Value (from JSON)
UNET loader anima-preview.safetensors / default
CLIP loader qwen_3_06b_base.safetensors / stable_diffusion
VAE qwen_image_vae.safetensors
KSampler steps 31 · cfg 4 · er_sde · simple · denoise 0.7

Anima文生图 - 10 nodes

Item Value (from JSON)
UNET loader anima-preview.safetensors / default
CLIP loader qwen_3_06b_base.safetensors / stable_diffusion
VAE qwen_image_vae.safetensors
KSampler steps 30 · cfg 4 · er_sde · simple · denoise 1
Latent size 1024x1024 · batch 1

Honest limits

  • Preview build, not an accelerated model: it needs a full step budget to look right.
  • Native resolution is small; upscaling is mandatory for production sizes.
  • Character/IP LoRAs carry their own licences - the graph shown uses a third-party Gundam LoRA as an example.
  • The 4-6 GB VRAM estimate is the author's inference from model and encoder file sizes, not a measured floor.

Episode resources