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+comparison/preview.png filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+.cache/

LICENSE

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README.md

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+---
+license: apache-2.0
+base_model:
+  - Qwen/Qwen-Image
+library_name: diffusers
+pipeline_tag: image-to-image
+tags:
+  - anima
+  - qwen-image
+  - vae
+  - autoencoder
+  - comfyui
+  - anime
+  - illustration
+inference: false
+---
+
+# Qwanima-vae
+
+This is a decoder-only finetune for the Qwen-Image VAE used by
+[Anima](https://huggingface.co/circlestone-labs/Anima). It is directly inspired by
+[spacepxl's 2x Wan VAE upscaler](https://huggingface.co/spacepxl/Wan2.1-VAE-upscale2x), adapted to
+Qwen-Image and Anima using the methodology described by spacepxl. More detail about the original
+method is available on that model card.
+
+The motivation behind this project is mostly the same as spacepxl's. The Qwen-Image VAE produces a
+rather unpleasant dithered look. Since it was also made with photographs in mind, it
+can reconstruct fine detail as noisy or incoherent texture. This decoder finetune aims to reduce
+that and produce a cleaner image suited to Anima generations.
+
+The released checkpoint is the 45k EMA from the best 256px run. The encoder is unchanged from
+Qwen-Image and only the decoder was finetuned, so this uses the same 16-channel latent format and
+produces images at the same resolution as the original VAE.
+
+## Comparison
+
+![Original and fine-tuned VAE comparison](comparison/preview.png)
+
+## Model
+
+Inference is the same as with a normal VAE:
+
+```text
+Anima latent
+    ↓
+Qwen-Image VAE decoder (Anima finetuned)
+    ↓
+image
+```
+
+## Training
+
+Training looked roughly like this:
+
+```text
+training image
+    ↓
+frozen Qwen VAE encoder
+    ↓
+clean latent
+    ↓
+degradation via a learned proxy model
+    ↓
+trainable Qwen VAE decoder
+    ↓
+reconstruction
+```
+
+The training data used a split of 75% Booru Essence and 25% Cleveland Museum of Art images.
+Booru Essence covers the anime part of the distribution, while the museum data adds paintings,
+physical media, and other fine texture to prevent the decoder from forgetting those details.
+
+The final run used:
+
+- 256x256 training crops
+- Frozen Qwen-Image encoder
+- Anima degradation proxy, applied with probability 0.8 over timesteps 0–0.12
+- MSE loss at `1.0`
+- DINOv3 ViT-B feature loss at `8.0`, using patch tokens from all layers
+- Anti-aliased PatchGAN with LSGAN loss at `0.25`
+
+I settled on MSE at weight 1.0 because that gave the GAN enough freedom to produce details while
+still anchoring the result to the pixel target. I previously used L1, but it anchored the
+reconstruction too aggressively and hampered the GAN's ability to add detail in my shoddy testing.
+
+I won't pretend these are the optimal settings. This is simply what I ended up with in the end.
+
+45k was selected because it looked the best. Was going to train it to 100k steps switching to 512px at 50k but it was steadily degrading as training continued. I think GAN weight might've been too high.
+
+### Anti-aliased PatchGAN
+
+PatchGAN judges a grid of small local patches instead of reducing the entire image to a single
+real/fake score. That makes it useful for texture, but its stride-2 convolutions can also alias
+high frequencies while downsampling. The discriminator can then become sensitive to pixel-grid
+phase and mistake regular dithering or checkerboard patterns for convincing detail.
+
+To make that shortcut less attractive, both the real and generated images are low-pass filtered
+immediately before each of PatchGAN's three stride-2 stages. The filter is a fixed, normalized 3x3
+binomial kernel:
+
+```text
+[1, 2, 1]ᵀ × [1, 2, 1] / 16
+```
+
+This band-limits the signal before subsampling, encouraging the discriminator to judge coherent
+local structure instead of phase-locked pixel energy. The filtering only exists inside the
+discriminator: it does not blur the saved decoder output or the images used by MSE and DINO.
+
+The discriminator itself uses four spectral-normalized layers with 64 base channels. GAN training
+began at step 1,000, ramped to weight 0.25 over 2,000 steps, and used LSGAN.
+
+## Diffusers
+
+`diffusion_pytorch_model.safetensors` is a standard Diffusers checkpoint. Use it with:
+
+```python
+import torch
+from diffusers import AutoencoderKLQwenImage
+
+vae = AutoencoderKLQwenImage.from_pretrained(
+    "PATH_OR_REPO_ID",
+    torch_dtype=torch.bfloat16,
+).to("cuda")
+```
+
+## ComfyUI
+
+A converted BF16 version for ComfyUI is located here:
+
+```text
+comfyui/qwen_vae_anima.safetensors
+```
+
+Copy it into `ComfyUI/models/vae/`, refresh or restart ComfyUI, and load it with the normal
+Load VAE node.
+
+## 2x upscaling
+
+I mostly focused on 1x decoding for this project, so that is what I am releasing. This VAE does
+not change the output resolution and a 1024px Anima latent still decodes to a 1024px image.
+
+## Files
+
+```text
+.
+├── config.json
+├── diffusion_pytorch_model.safetensors
+├── comfyui/
+│   └── qwen_vae_anima.safetensors
+├── comparison/
+│   └── preview.png
+└── LICENSE
+```
+
+`diffusion_pytorch_model.safetensors` is the FP32 Diffusers checkpoint.
+
+`comfyui/qwen_vae_anima.safetensors` is the converted BF16 ComfyUI checkpoint.
+
+## Limitations
+
+Since this was trained mostly on anime and illustration it won't provide good results on images
+far outside that distribution. Photos and textured media can be oversmoothed or otherwise
+altered, and difficult high-frequency regions may develop grain or speckles. For a 2x VAE
+with broader material coverage, use
+[spacepxl's Wan2.1 VAE upscaler](https://huggingface.co/spacepxl/Wan2.1-VAE-upscale2x).
+
+## License
+
+Released under the [Apache License 2.0](LICENSE).

comfyui/qwen_vae_anima.safetensors

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config.json

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+{
+  "_class_name": "AutoencoderKLQwenImage",
+  "_diffusers_version": "0.40.0.dev0",
+  "attn_scales": [],
+  "base_dim": 96,
+  "dim_mult": [
+    1,
+    2,
+    4,
+    4
+  ],
+  "dropout": 0.0,
+  "latents_mean": [
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+    -0.7089,
+    -0.9113,
+    0.1075,
+    -0.1745,
+    0.9653,
+    -0.1517,
+    1.5508,
+    0.4134,
+    -0.0715,
+    0.5517,
+    -0.3632,
+    -0.1922,
+    -0.9497,
+    0.2503,
+    -0.2921
+  ],
+  "latents_std": [
+    2.8184,
+    1.4541,
+    2.3275,
+    2.6558,
+    1.2196,
+    1.7708,
+    2.6052,
+    2.0743,
+    3.2687,
+    2.1526,
+    2.8652,
+    1.5579,
+    1.6382,
+    1.1253,
+    2.8251,
+    1.916
+  ],
+  "num_res_blocks": 2,
+  "temperal_downsample": [
+    false,
+    true,
+    true
+  ],
+  "z_dim": 16
+}

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