ComfyUI-Ascend-Int8-Test

ComfyUI Ascend INT8 Validation Nodes

This custom node pack is for deployment validation of ComfyUI + Ascend NPU + INT8 linear weight quantization.

It is intentionally safe on non-Ascend machines:

• NVIDIA/local development can import the nodes and use fallback_dequant_only.
• Ascend/FaaS deployment can use auto_torch_npu_with_fallback first.
• torch_npu_strict raises at runtime if torch_npu.npu_weight_quant_batchmatmul is missing, the input is not on npu, or the Ascend op fails.

Nodes

• Ascend INT8 Environment Report

  • Reports Python, torch, torch_npu, torch.npu, and required INT8 APIs.

• Load Diffusion Model (Ascend INT8)

  • Loads a file from models/diffusion_models.
  • Quantizes eligible Linear weights to int8 while the model is loaded.

• Load Checkpoint (Ascend INT8)

  • Loads a normal checkpoint from models/checkpoints.
  • Quantizes the diffusion model Linear weights only. CLIP and VAE are left on normal ComfyUI paths.

Backend Modes

• auto_torch_npu_with_fallback

  • Tries torch_npu.npu_weight_quant_batchmatmul on NPU inputs.
  • Falls back to dequantized torch.nn.functional.linear otherwise.

• torch_npu_strict

  • Requires the Ascend INT8 op. Use this for FaaS validation after basic import succeeds.

• fallback_dequant_only

  • Never calls torch_npu; useful for local NVIDIA smoke tests.

Current Scope

This is a validation plugin, not a production quantization implementation.

Known limitations:

• Only Linear layers are quantized.
• Weight quantization is online at model load time, so peak memory is not optimal.
• LoRA or other weight patches fall back to dequantized linear for correctness.
• Text encoders and VAE are not quantized by these loader nodes.
• Per-channel scale means one scale per output channel.

The main integration point for a custom quantization library is the INT8 matmul path in __init__.py.