README.md

---
license: other
license_name: circlestone-labs-non-commercial-license
license_link: https://huggingface.co/circlestone-labs/Anima/blob/main/LICENSE.md
base_model:
- circlestone-labs/Anima
base_model_relation: quantized
tags:
- comfyui
- diffusion-single-file
pipeline_tag: text-to-image
widget:
- output:
    url: images/20260518_112816-Anima_00001_.webp
  text: '-'
- output:
    url: images/20260518_112929-Anima_00001_.webp
  text: '-'
- output:
    url: images/20260518_112957-Anima_00001_.webp
  text: '-'
- output:
    url: images/20260518_113155-Anima_00001_.webp
  text: '-'
- output:
    url: images/20260518_113218-Anima_00001_.webp
  text: '-'
- output:
    url: images/20260518_113251-Anima_00001_.webp
  text: '-'
- output:
    url: images/20260518_113847-Anima_00001_.webp
  text: '-'
- output:
    url: images/20260518_113948-Anima_00001_.webp
  text: '-'
- output:
    url: images/20260518_131629-Anima_00001_.webp
  text: '-'
---

# Int8 Quantized model of ANIMA

<Gallery />

## Notice

ComfyUI has native INT8 support as of commit 1a510f0, but it currently rejects the `int8_rowwise` format and offers no real speedup over BF16.  
I have updated (int8rowwise](anima-base-v1.0-int8rowwise.safetensors) model for ComfyUI native support.
If you need better quality (but slower), please use the [int8convrot](anima-base-v1.0-int8convrot.safetensors) model.

## Generation Speed

### Test Environment

- ComfyUI commit 264b003
- [ComfyUI-INT8-Fast](https://github.com/BobJohnson24/ComfyUI-INT8-Fast) commit 7ff676c
- use `--fast fp16_accumulation fp8_matrix_mult cublas_ops --use-sage-attention --disable-dynamic-vram` options
- Tested on 18/05/26

|                       | RTX 3060              | RTX 3090              | RTX 5090              |
| --------------------- | --------------------- | --------------------- | --------------------- |
| **TDP**               | 170W                  | 280W                  | 400W                  |
| **PCIe**              | PCIe 3.0 x4           | PCIe 4.0 x8           | PCIe 4.0 x16          |
| **OS**                | Windows 11            | Ubuntu 24.04.4 LTS    | Ubuntu 24.04.4 LTS    |
| **Driver**            | 596.49                | 580.142               | 590.48.01             |
| **Python**            | 3.13.9                | 3.12.3                | 3.12.3                |
| **torch**             | 2.12.0+cu130          | 2.12.0+cu130          | 2.12.0+cu130          |
| **triton**            | 3.7.0.post26 (triton-window) | 3.7.0          | 3.7.0                 |
| **sageattention**     | [2.2.0+cu130](https://github.com/woct0rdho/SageAttention/releases/tag/v2.2.0-windows.post4) | 2.2.0  | 2.2.0                 |

### No LoRA

832×1216 · er_sde simple · CFG 5.0 · 30 steps · No LoRA

|                           | **RTX 3060**       | **RTX 3090**       | **RTX 5090**      |
| ------------------------- | ------------------ | ------------------ | ----------------- |
| **BF16** · w/o compile    | 0.73 it/s / 41.64s | 1.77 it/s / 18.15s | 5.09 it/s / 6.47s |
| **BF16** · w/ compile     | 0.95 it/s / 32.22s | 2.31 it/s / 14.16s | 6.35 it/s / 5.37s |
| **INT8** · w/o compile    | 0.87 it/s / 35.82s | 2.09 it/s / 15.67s | 6.32 it/s / 5.46s |
| **INT8** · w/ compile     | **1.13 it/s / 28.09s** | **2.84 it/s / 11.77s** | **8.51 it/s / 4.26s** |
| Δ w/o compile (BF16→INT8) | +19.18% / +14.00%  | +18.08% / +13.69%  | +24.17% / +15.61% |
| Δ w/ compile  (BF16→INT8) | **+18.95% / +12.82%**  | **+22.94% / +16.88%**  | **+34.02% / +20.67%** |

### Hires LoRA

832×1216 · er_sde simple · CFG 5.0 · 30 steps · Hires LoRA

|                                  | **RTX 3060**            | **RTX 3090**            | **RTX 5090**            |
| -------------------------------- | ----------------------- | ----------------------- | ----------------------- |
| **BF16** · w/o compile           | 0.73 it/s / 41.73s      | 1.78 it/s / 18.13s      | 5.01 it/s / 7.01s       |
| **BF16** · w/ compile            | 0.95 it/s / 32.22s      | 2.04 it/s / 16.00s      | 6.21 it/s / 5.47s       |
| **INT8 (Stoch.)** · w/o compile  | 0.87 it/s / 36.93s      | 2.07 it/s / 15.75s      | 6.41 it/s / 6.07s       |
| **INT8 (Stoch.)** · w/ compile   | **1.04 it/s / 31.47s**      | **2.44 it/s / 13.52s**      | **7.13 it/s / 5.23s**       |
| **INT8 (Dyn.)** · w/o compile    | 0.70 it/s / 44.45s      | 1.67 it/s / 19.32s      | 4.96 it/s / 7.42s       |
| **INT8 (Dyn.)** · w/ compile     | 0.83 it/s / 37.66s      | 2.00 it/s / 16.28s      | 5.76 it/s / 6.05s       |
| Δ Stoch. · w/o compile           | +19.18% / +11.51%       | +16.29% / +13.07%       | +27.94% / +13.41%       |
| Δ Stoch. · w/ compile            | **+9.47% / +2.33%**         | **+19.61% / +15.50%**       | **+14.81% / +4.39%**        |
| Δ Dyn.   · w/o compile           | −4.11% / −6.52%         | −6.18% / −6.56%         | −1.00% / −5.85%         |
| Δ Dyn.   · w/ compile            | −12.63% / −16.88%       | −1.96% / −1.75%         | −7.25% / −10.60%        |

### Turbo LoRA

832×1216 · er_sde simple · CFG 1.0 · 10 steps · Turbo LoRA

|                                  | **RTX 3060**            | **RTX 3090**            | **RTX 5090**            |
| -------------------------------- | ----------------------- | ----------------------- | ----------------------- |
| **BF16** · w/o compile           | 1.44 it/s / 7.57s       | 3.55 it/s / 4.10s       | 10.21 it/s / 1.59s      |
| **BF16** · w/ compile            | ***1.88 it/s / 5.92s***       | 4.60 it/s / 3.05s       | ***12.76 it/s / 1.38s***      |
| **INT8 (Stoch.)** · w/o compile  | 1.73 it/s / 8.55s       | 4.15 it/s / 3.32s       | 13.08 it/s / 1.78s      |
| **INT8 (Stoch.)** · w/ compile   | ***2.38 it/s / 6.75s***       | **5.67 it/s / 2.66s**       | ***14.44 it/s / 1.73s***      |
| **INT8 (Dyn.)** · w/o compile    | 1.38 it/s / 8.74s       | 3.32 it/s / 3.85s       | 10.00 it/s / 1.82s      |
| **INT8 (Dyn.)** · w/ compile     | 1.38 it/s / 8.94s       | 4.69 it/s / 3.01s       | 13.59 it/s / 1.53s      |
| Δ Stoch. · w/o compile           | +20.14% / −12.95%       | +16.90% / +19.02%       | +28.11% / −11.95%       |
| Δ Stoch. · w/ compile            | ***+26.60% / −14.02%***       | **+23.26% / +12.79%**       | ***+13.17% / −25.36%***       |
| Δ Dyn.   · w/o compile           | −4.17% / −15.46%        | −6.48% / +6.10%         | −2.06% / −14.47%        |
| Δ Dyn.   · w/ compile            | −26.60% / −51.01%       | +1.96% / +1.31%         | +6.50% / −10.87%        |

> Δ it/s = (INT8 − BF16) / BF16 × 100 · Δ Time = (BF16 − INT8) / BF16 × 100 · positive = INT8 faster

## How to use

1. Cloning [ComfyUI-INT8-Fast](https://github.com/BobJohnson24/ComfyUI-INT8-Fast) to `custom_nodes` directory.
2. Recommend to run ComfyUI with `--disable-dynamic-vram` option.
3. Use `Load Diffusion Model INT8 (W8A8)` node to model loading and set `on_the_fly_qunatization` to **False** (default).
   ![image](https://cdn-uploads.huggingface.co/production/uploads/63fbf6951b4b1bd4e706fed1/AdyBv-qYZjb0DabsMnyKB.png)
4. Recommend to use "Stochastic" for lora.


## Quantized layers

### INT8Tensorwise
```json
{
  "format": "comfy_quant",
  "block_names": ["net.blocks."],
  "rules": [
    { "policy": "keep", "match": ["blocks.0", "adaln_modulation", ".mlp.layer2"] },
    { "policy": "int8_tensorwise", "match": ["q_proj", "k_proj", "v_proj", "output_proj", ".mlp"] }
  ]
}
```

### INT8Rowwise
```json
{
  "format": "comfy_quant",
  "block_names": ["net.blocks."],
  "rules": [
    { "policy": "keep", "match": [
            "blocks.0.", "blocks.27.", "adaln_modulation",
            ".0.mlp", ".1.mlp", ".2.mlp", ".3.mlp"
    ]},
    { "policy": "int8_rowwise", "match": ["q_proj", "k_proj", "v_proj", "output_proj", ".mlp"] }
  ]
}
```