ComfyUI-SNR-quant

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ComfyUI-SNR-quant

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Adaptive FP8/BF16 mixed precision quantization for ComfyUI diffusion models. SNR-based layer selection with outlier protection.

ComfyUI-SNR-quant 최신버젼 다운로드

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# SNR-Quant

ComfyUI nodes for adaptive FP8/BF16 mixed precision quantization.

## Features

- **Automatic model quantization** - Quantize any diffusion model to a target size
- **Adaptive threshold selection** - Automatically detects model "fragility" and adjusts quantization strategy
- **SNR-based layer selection** - Protects sensitive layers, quantizes robust ones
- **Outlier protection** - Layers with extreme weights are kept in BF16
- **Progress bar** - Shows quantization progress in ComfyUI console
- **CSV report** - Generates detailed report showing which layers were quantized
- **Re-quantization safe** - Already quantized FP8 layers are preserved (no size increase on re-run)

## Installation

1. Clone or copy this folder to `ComfyUI/custom_nodes/SNR-quant`
2. Restart ComfyUI

Dependencies (usually already installed):
- `torch>=2.1.0` (for FP8 support)
- `safetensors>=0.4.0`
- `numpy>=1.24.0`

## Usage

### SNR Quant (FP8/BF16)

Quantize a model to fit within a target size.

**Inputs:**
- `model_name` - Select from `ComfyUI/models/diffusion_models`
- `target_size_GB` - Target size in GB (e.g., 22.0)

**Output:**
- Saved directly to `ComfyUI/output/diffusion_models/quant_{model_name}_{target}GB.safetensors`
- CSV report: `ComfyUI/output/diffusion_models/quant_{model_name}_{target}GB_report.csv`

**Example:**
```
model_name: wan2.2_fp16.safetensors
target_size_GB: 22.0
→ output: quant_wan2.2_fp16_22.0GB.safetensors
→ report: quant_wan2.2_fp16_22.0GB_report.csv
```

### SNR Model Analyzer

Analyze a model's SNR and outlier distribution.

**Input:**
- `model_name` - Select from `ComfyUI/models/diffusion_models`

**Outputs:**
- `report` - Text report with statistics and quantization recommendations

## How It Works

```
Load model → Analyze layers → Detect fragility → Select layers → Quantize → Save
                ↓                    ↓              ↓
            SNR + Outlier      fragile/moderate/  FP8 or BF16
                               robust             per layer
```

### Fragility Detection

| Fragility | SNR Range | SNR Median | Strategy |
|-----------|-----------|------------|----------|
| Fragile | < 2 dB | < 32 dB | Protect more (90th percentile) |
| Moderate | - | - | Balanced (75th percentile) |
| Robust | > 5 dB | > 34 dB | Quantize more (50th percentile) |

### Layer Selection

1. Filter layers by outlier threshold (adaptive percentile)
2. Sort by SNR (descending - highest SNR = safest to quantize)
3. Select layers until target size is reached

### Size Calculation

- Baseline: All 2D layers as BF16 (2 bytes/param) + 1D layers as BF16
- Minimum: All 2D layers as FP8 (1 byte/param) + 1D layers as BF16
- If target < minimum: auto-adjust to minimum

### Re-quantization Behavior

When quantizing an already quantized model:
- Existing FP8 layers are **preserved** (not converted back to BF16)
- Only remaining BF16 layers are considered for further quantization
- File size will not increase on re-run

## Output Format

Quantized models are saved as `.safetensors` with mixed precision:
- FP8 layers: `torch.float8_e4m3fn`
- BF16 layers: `torch.bfloat16`
- 1D layers (bias, norms): always BF16

## CSV Report Format

The generated report includes:
- `layer_name` - Full layer path
- `precision` - Final precision (FP8, BF16, or FP8 (existing))
- `params_numel` - Number of parameters
- `SNR_dB` - Signal-to-Noise ratio (higher = safer to quantize)
- `outlier` - Outlier index (lower = safer to quantize)
- `precision_change` - Whether layer was quantized or preserved

## License

Apache-2.0

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