ComfyUI_stable_fast

Experimental usage of stable-fast and TensorRT.

Installation

git clone https://github.com/gameltb/ComfyUI_stable_fast custom_nodes/ComfyUI_stable_fast

stable-fast

You'll need to follow the guide below to enable stable fast node.

stable-fast installation

Note

Requires stable-fast >= 1.0.0 .

TensorRT(testing)

Note

Currently only tested on linux, Not tested on Windows.

The following needs to be installed when you use TensorRT.

pip install onnx zstandard
pip install --pre --upgrade --extra-index-url https://pypi.nvidia.com tensorrt==9.3.0.post12.dev1
pip install onnx-graphsurgeon polygraphy --extra-index-url https://pypi.ngc.nvidia.com

Usage

Please refer to the screenshot

stable-fast

It can work with Lora, ControlNet and lcm. SD1.5 and SSD-1B are supported. SDXL should work.
Run ComfyUI with --disable-cuda-malloc may be possible to optimize the speed further.

Note

FreeU and PatchModelAddDownscale are now supported experimentally, Just use the comfy node normally.
stable fast not work well with accelerate, So this node has no effect when the vram is low. For example: 6G vram card run SDXL.
stable fast will optimize the speed when generating images using the same model for the second time. if you switch models or Lora frequently, please consider disable enable_cuda_graph.
It is better to connect the Apply StableFast Unet node directly to the KSampler node, and there should be no nodes between them that will change the weight, such as the Load LoRA node, but for some nodes, placing it between them can prevent useless recompilation caused by modifying the node parameters, such as the FreeU node, you can try to use other nodes, but I can't guarantee that it will work properly.

TensorRT(testing)

Run ComfyUI with --disable-xformers --force-fp16 --fp16-vae and use Apply TensorRT Unet like Apply StableFast Unet.
The Engine will be cached in tensorrt_engine_cache.

Note

If you encounter an error after updating, you can try deleting the tensorrt_engine_cache.

Table

Features

	Stable Fast	TensorRT(UNET)	TensorRT(UNET_BLOCK)
SD1.5	✓	✓	✓
SDXL	untested(Should work)	WIP	untested
SSD-1B	✓	WIP	✓
Lora	✓	✓	✓
ControlNet Unet	✓	✓	✓
VAE decode	WIP	✓	✓
ControlNet Model	WIP	WIP	WIP

Nodes Tested

	Stable Fast	TensorRT(UNET)	TensorRT(UNET_BLOCK)
Load LoRA	✓	✓	✓
FreeU(FreeU_V2)	✓	✗	✓
PatchModelAddDownscale	✓	WIP	✓

Speed Test

GeForce RTX 3060 Mobile

GeForce RTX 3060 Mobile (80W) 6GB, Linux , torch 2.1.1, stable fast 0.0.14, tensorrt 9.2.0.post12.dev5, xformers 0.0.23.
workflow: SD1.5, 512x512 bantch_size 1, euler_ancestral karras, 20 steps, use fp16.

Test Stable Fast and xformers run ComfyUI with --disable-cuda-malloc.
Test TensorRT and pytorch run ComfyUI with --disable-xformers.

TensorRT Note

For the TensorRT first launch, it will take up to 10 minutes to build the engine; with timing cache, it will reduce to about 2–3 minutes; with engine cache, it will reduce to about 20–30 seconds for now.

Avg it/s

	Stable Fast (enable_cuda_graph)	TensorRT (UNET)	TensorRT (UNET_BLOCK)	pytorch cross attention	xformers
	10.10 it/s	10.95it/s	10.66it/s	7.02it/s	7.90it/s
enable FreeU	9.42 it/s	✗	10.04it/s	6.75it/s	7.54it/s
enable Patch Model Add Downscale	10.81 it/s	✗	11.30it/s	7.46it/s	8.41it/s

Avg time spent

workflow	Stable Fast (enable_cuda_graph)	TensorRT (UNET)	TensorRT (UNET_BLOCK)	pytorch cross attention	xformers
	2.21s (first 17s)	2.05s	2.10s	3.06s	2.76s
enable FreeU	2.35s (first 18.5s)	✗	2.24s	3.18s	2.88
enable Patch Model Add Downscale	2.08s (first 31.37s)	✗	2.03s	2.89s	2.61s

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