Wan2.2 — INT8 ConvRot Quantized
INT8 tensor-wise quantized versions of the Wan2.2 14B diffusion models for ComfyUI, with ConvRot (group size 256) applied for maximum quantization accuracy on Ampere GPUs (RTX 30XX series).
Quantized with silveroxides/convert_to_quant directly from true BF16 source weights — single quantization pass, no double-quantization issues.
Download — INT8 ConvRot Models
Diffusion Models (this repo)
- wan2.2_i2v_high_noise_14B_int8_convrot.safetensors
- wan2.2_i2v_low_noise_14B_int8_convrot.safetensors
Text Encoder (this repo)
The following files are unchanged from the official Comfy-Org release — download them separately:
LoRA
- wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors
- wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors
VAE
File Storage Location
ComfyUI/
├───📂 models/
│ ├───📂 diffusion_models/
│ │ ├─── wan2.2_i2v_low_noise_14B_int8_convrot.safetensors ← this repo
│ │ └─── wan2.2_i2v_high_noise_14B_int8_convrot.safetensors ← this repo
│ ├───📂 loras/
│ │ ├─── wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors
│ │ └─── wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors
│ ├───📂 text_encoders/
│ │ └─── umt5_xxl_int8_convrot.safetensors ← this repo
│ └───📂 vae/
│ └─── wan_2.1_vae.safetensors
Quantization Details
- Format: INT8 tensor-wise (
int8_tensorwise) - ConvRot group size: 256 — all Wan2.2 dimensions (
256,4096,5120,13824) divide cleanly into 256, so full-strength ConvRot is applied - Preset used:
--wan(skips embeddings, encoders, head layers) - Source: true BF16 weights — single quantization pass, no FP8→INT8 double-quantization
Notes
- Native INT8 support is available in recent ComfyUI builds (no extra custom nodes required for recent versions)
- For older ComfyUI builds, install BobJohnson24/ComfyUI-INT8-Fast
- These models are optimized for Ampere GPUs (RTX 30XX) — INT8 is significantly faster than FP8/FP4 on this generation
Disclaimer
Quantized versions of the original Wan2.2 models. All credit for the original models goes to their respective authors. Quantization may introduce minor differences in output quality compared to BF16/FP16 originals.
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