diffusers-torchao

Optimize image and video generation with diffusers, torchao, combining torch.compile() 🔥

We provide end-to-end inference and experimental training recipes to use torchao with diffusers in this repo. We demonstrate 53.88% speedup on Flux.1-Dev^* and 27.33% speedup on CogVideoX-5b when comparing compiled quantized models against their standard bf16 counterparts^**.

_{^*The experiments were run on a single H100, 80 GB GPU. ^**The experiments were run on a single A100, 80 GB GPU. For a single H100, the speedup is 33.04%}

Updates

• torchao is being integrated into diffusers as an official quantization backend. Be on the lookout for this PR to get merged.
• torchao will soon be added as a quantization backend in diffusers, making it even easier to use with diffusers.
• Check out our new AoT compilation and serialization guide to reduce framework overheads.

No-frills code:

from diffusers import FluxPipeline
+ from torchao.quantization import autoquant
import torch 

pipeline = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16
).to("cuda")
+ pipeline.transformer = autoquant(pipeline.transformer, error_on_unseen=False)
image = pipeline(
    "a dog surfing on moon", guidance_scale=3.5, num_inference_steps=50
).images[0]

Throw in torch.compile() to make it go brrr:

# If you are using "autoquant" then you should compile first and then
# apply autoquant.
+ pipeline.transformer.to(memory_format=torch.channels_last)
+ pipeline.transformer = torch.compile(
+    pipeline.transformer, mode="max-autotune", fullgraph=True
+)

This, alone, is sufficient to cut down inference time for Flux.1-Dev from 6.431 seconds to 3.483 seconds on an H100. Check out the inference directory for the code.

Note

Quantizing to a supported datatype and using base precision as fp16 can lead to overflows. The recommended base precision for CogVideoX-2b is fp16 while that of CogVideoX-5b is bf16. If comparisons were to be made in fp16, the speedup gains would be ~23% and ~32% respectively.

Table of contents

• Environment
• Benchmarking results
• Reducing quantization time and peak memory
• Training with FP8
• Serialization and loading quantized models
• Things to keep in mind when benchmarking
• Benefitting from torch.compile()

Environment

We conducted all our experiments on a single A100 (80GB) and H100 GPUs. Since we wanted to benefit from torch.compile(), we used relatively modern cards here. For older cards, same memory savings (demonstrated more below) can be obtained.

We always default to using the PyTorch nightly, updated diffusers and torchao codebases. We used CUDA 12.2.

Benchmarking results

We benchmark two models (Flux.1-Dev and CogVideoX) using different supported quantization datatypes in torchao. The results are as follows:

Flux.1 Dev Benchmarks

Additional Results

ckpt_id	batch_size	fuse	compile	compile_vae	quantization	sparsify	model_memory	inference_memory	time
black-forest-labs/FLUX.1-dev	4	True	True	False	fp8wo	False	22.368	35.616	16.204
black-forest-labs/FLUX.1-dev	8	False	False	False	None	False	31.438	47.509	49.438
black-forest-labs/FLUX.1-dev	8	False	True	False	None	False	31.439	47.506	31.685
black-forest-labs/FLUX.1-dev	1	False	True	False	int8dq	False	20.386	31.608	3.406
black-forest-labs/FLUX.1-dev	4	False	True	False	int8wo	False	20.387	31.609	16.08
black-forest-labs/FLUX.1-dev	8	False	True	False	fp8dq	False	20.357	36.425	23.393
black-forest-labs/FLUX.1-dev	8	True	True	False	int8dq	False	22.397	38.464	24.696
black-forest-labs/FLUX.1-dev	8	False	False	False	int8dq	False	20.386	36.458	333.567
black-forest-labs/FLUX.1-dev	4	True	False	False	fp8dq	False	22.361	35.826	26.259
black-forest-labs/FLUX.1-dev	8	False	True	False	int8dq	False	20.386	36.453	24.725
black-forest-labs/FLUX.1-dev	1	True	True	False	int8wo	False	22.396	35.616	4.574
black-forest-labs/FLUX.1-dev	1	False	True	False	fp8wo	False	20.363	31.607	4.395
black-forest-labs/FLUX.1-dev	8	True	False	False	int8wo	False	22.397	38.468	57.274
black-forest-labs/FLUX.1-dev	4	True	False	False	int8dq	False	22.396	35.616	219.687
black-forest-labs/FLUX.1-dev	4	False	False	False	None	False	31.438	39.49	24.828
black-forest-labs/FLUX.1-dev	1	True	True	False	fp8dq	False	22.363	35.827	3.192
black-forest-labs/FLUX.1-dev	1	False	False	False	fp8dq	False	20.356	31.817	8.622
black-forest-labs/FLUX.1-dev	8	False	False	False	fp8dq	False	20.357	36.428	55.097
black-forest-labs/FLUX.1-dev	4	False	False	False	int8wo	False	20.384	31.606	29.414
black-forest-labs/FLUX.1-dev	1	True	False	False	fp8wo	False	22.371	35.618	8.33
black-forest-labs/FLUX.1-dev	1	False	False	False	int8dq	False	20.386	31.608	130.498
black-forest-labs/FLUX.1-dev	8	True	True	False	fp8wo	False	22.369	38.436	31.718
black-forest-labs/FLUX.1-dev	4	False	False	False	fp8wo	False	20.363	31.607	26.61
black-forest-labs/FLUX.1-dev	1	True	False	False	int8wo	False	22.397	35.616	8.49
black-forest-labs/FLUX.1-dev	8	True	False	False	fp8dq	False	22.363	38.433	51.547
black-forest-labs/FLUX.1-dev	4	False	True	False	fp8dq	False	20.359	31.82	11.919
black-forest-labs/FLUX.1-dev	4	False	True	False	None	False	31.438	39.488	15.948
black-forest-labs/FLUX.1-dev	4	True	True	False	int8dq	False	22.397	35.616	12.594
black-forest-labs/FLUX.1-dev	1	True	True	False	fp8wo	False	22.369	35.616	4.326
black-forest-labs/FLUX.1-dev	4	True	False	False	int8wo	False	22.397	35.617	29.394
black-forest-labs/FLUX.1-dev	1	False	False	False	fp8wo	False	20.362	31.607	8.402
black-forest-labs/FLUX.1-dev	8	True	False	False	int8dq	False	22.397	38.468	322.688
black-forest-labs/FLUX.1-dev	1	False	False	False	int8wo	False	20.385	31.607	8.551
black-forest-labs/FLUX.1-dev	8	True	True	False	fp8dq	False	22.363	38.43	23.261
black-forest-labs/FLUX.1-dev	4	False	False	False	fp8dq	False	20.356	31.817	28.154
black-forest-labs/FLUX.1-dev	1	True	False	False	int8dq	False	22.397	35.616	119.736
black-forest-labs/FLUX.1-dev	8	True	False	False	fp8wo	False	22.369	38.441	51.311
black-forest-labs/FLUX.1-dev	4	False	True	False	fp8wo	False	20.363	31.607	16.232
black-forest-labs/FLUX.1-dev	4	True	True	False	int8wo	False	22.399	35.619	16.158
black-forest-labs/FLUX.1-dev	8	False	False	False	fp8wo	False	20.363	36.434	51.223
black-forest-labs/FLUX.1-dev	4	False	False	False	int8dq	False	20.385	31.607	221.588
black-forest-labs/FLUX.1-dev	1	True	False	False	fp8dq	False	22.364	35.829	7.34
black-forest-labs/FLUX.1-dev	1	False	False	False	None	False	31.438	33.851	6.573
black-forest-labs/FLUX.1-dev	4	True	True	False	fp8dq	False	22.363	35.827	11.885
black-forest-labs/FLUX.1-dev	1	False	True	False	int8wo	False	20.384	31.606	4.615
black-forest-labs/FLUX.1-dev	8	False	True	False	int8wo	False	20.386	36.453	31.159
black-forest-labs/FLUX.1-dev	1	True	True	False	int8dq	False	22.397	35.617	3.357
black-forest-labs/FLUX.1-dev	1	False	True	False	fp8dq	False	20.357	31.818	3.243
black-forest-labs/FLUX.1-dev	4	False	True	False	int8dq	False	20.384	31.606	12.513
black-forest-labs/FLUX.1-dev	8	False	True	False	fp8wo	False	20.363	36.43	31.783
black-forest-labs/FLUX.1-dev	1	False	True	False	None	False	31.438	33.851	4.209
black-forest-labs/FLUX.1-dev	8	False	False	False	int8wo	False	20.386	36.457	57.026
black-forest-labs/FLUX.1-dev	8	True	True	False	int8wo	False	22.397	38.464	31.216
black-forest-labs/FLUX.1-dev	4	True	False	False	fp8wo	False	22.368	35.616	26.716

With the newly added fp8dqrow scheme, we can bring down the inference latency to 2.966 seconds for Flux.1 Dev (batch size:1 , steps: 28, resolution: 1024) on an H100. fp8dqrow has more scales per tensors and less quantization error. Additional results:

Additional `fp8dqrow` results

	ckpt_id	batch_size	fuse	compile	compile_vae	quantization	sparsify	model_memory	inference_memory	time
0	black-forest-labs/FLUX.1-dev	4	True	True	True	fp8dqrow	False	22.377	35.83	11.441
1	black-forest-labs/FLUX.1-dev	1	False	True	True	fp8dqrow	False	20.368	31.818	2.981
2	black-forest-labs/FLUX.1-dev	4	True	True	False	fp8dqrow	False	22.378	35.829	11.682
3	black-forest-labs/FLUX.1-dev	1	False	True	False	fp8dqrow	False	20.37	31.82	3.039
4	black-forest-labs/FLUX.1-dev	4	False	True	False	fp8dqrow	False	20.369	31.818	11.692
5	black-forest-labs/FLUX.1-dev	4	False	True	True	fp8dqrow	False	20.367	31.817	11.421
6	black-forest-labs/FLUX.1-dev	1	True	True	True	fp8dqrow	False	22.379	35.831	2.966
7	black-forest-labs/FLUX.1-dev	1	True	True	False	fp8dqrow	False	22.376	35.827	3.03

Trade-offs, trade-offs, and more trade-offs

We know that the table included above is hard to parse. So, we wanted to include a couple of points that are worth noting.

• Select the quantization technique that gives you the best trade-off between memory and latency.
• A quantization technique may exhibit different optimal settings for a given batch size. For example, for a batch size of 4, int8dq gives best time without any QKV fusion. But for other batch sizes, that is not the case.

The section below, drives this point home.

Higher batch sizes like 16 and above

This is how the top-5 latency looks like:

Collapse table

	ckpt_id	batch_size	fuse	compile	compile_vae	quantization	sparsify	model_memory	inference_memory	time
0	black-forest-labs/FLUX.1-dev	16	False	True	True	fp8dq	False	20.356	52.704	45.004
1	black-forest-labs/FLUX.1-dev	16	False	True	True	fp8dqrow	False	20.368	52.715	45.521
2	black-forest-labs/FLUX.1-dev	16	True	True	False	fp8dq	False	22.363	52.464	45.614
3	black-forest-labs/FLUX.1-dev	16	False	True	False	fp8dq	False	20.356	50.458	45.865
4	black-forest-labs/FLUX.1-dev	16	False	True	False	fp8dqrow	False	20.367	50.469	46.392

But interestingly, if we use an exotic fpx scheme for quantization, we can afford lesser memory with an increase in the latency:

Collapse table

	ckpt_id	batch_size	fuse	compile	compile_vae	quantization	sparsify	model_memory	inference_memory	time
0	black-forest-labs/FLUX.1-dev	16	False	True	True	fp6_e3m2	False	17.591	49.938	61.649
1	black-forest-labs/FLUX.1-dev	16	False	True	True	fp4_e2m1	False	14.823	47.173	61.75
2	black-forest-labs/FLUX.1-dev	16	True	True	False	fp6_e3m2	False	19.104	49.206	62.244
3	black-forest-labs/FLUX.1-dev	16	True	True	False	fp4_e2m1	False	15.827	45.929	62.296
4	black-forest-labs/FLUX.1-dev	16	False	True	False	fp6_e3m2	False	17.598	47.7	62.551

As a reference, with just torch.bfloat16 and SDPA, for a batch size of 16, we get:

	ckpt_id	batch_size	fuse	compile	compile_vae	quantization	sparsify	model_memory	inference_memory	time
0	black-forest-labs/FLUX.1-dev	16	False	False	False	None	False	31.438	61.548	97.545

Warning

Using fp4_e2m1 on the VAE negatively affects the image quality significantly.

Semi-structured sparsity + dynamic int8 quant

In our inference/benchmark_image.py script, there's an option to enable semi-structured sparsity with dynamic int8 quantization which is particularly suitable for larger batch sizes. You can enable it through the --sparsify flag. But we found that it significantly degrades image quality at the time of this writing.

Things to note:

• Only CUDA 12.4 and H100 and A100 devices support this option. You can use this Docker container: spsayakpaul/torchao-exps:latest. It has CUDA 12.4, torch nightlies, and other libraries installed to run the sparsity benchmark.
• Running with semi-structured sparsity and int8 dynamic quantization allows a batch size of 16.

The table below provides some benchmarks:

Sparsity Benchmarks

	ckpt_id	batch_size	fuse	compile	compile_vae	sparsify	time
0	black-forest-labs/FLUX.1-dev	16	True	True	True	True	50.62
1	black-forest-labs/FLUX.1-dev	16	False	True	True	True	51.167
2	black-forest-labs/FLUX.1-dev	16	True	True	False	True	51.418
3	black-forest-labs/FLUX.1-dev	16	False	True	False	True	51.941

Note

We can additionally compile the VAE too and it should work with most of the quantization schemes: pipeline.vae.decode = torch.compile(pipeline.vae.decode, mode="max-autotune", fullgraph=True), but the sake of simplicity, we decided to not include it.

CogVideoX Benchmarks

CogVideoX Benchmarks

A100

model_type	compile	fuse_qkv	quantize_vae	quantization	model_memory	inference_memory	time
5B	False	False	False	fp16	19.764	31.746	258.962
5B	False	True	False	fp16	21.979	33.961	257.761
5B	True	False	False	fp16	19.763	31.742	225.998
5B	True	True	False	fp16	21.979	33.961	225.814
5B	False	False	False	bf16	19.764	31.746	243.312
5B	False	True	False	bf16	21.979	33.96	242.519
5B	True	False	False	bf16	19.763	31.742	212.022
5B	True	True	False	bf16	21.979	33.961	211.377
5B	False	False	False	int8wo	10.302	22.288	260.036
5B	False	True	False	int8wo	11.414	23.396	271.627
5B	True	False	False	int8wo	10.301	22.282	205.899
5B	True	True	False	int8wo	11.412	23.397	209.640
5B	False	False	False	int8dq	10.3	22.287	550.239
5B	False	True	False	int8dq	11.414	23.399	530.113
5B	True	False	False	int8dq	10.3	22.286	177.256
5B	True	True	False	int8dq	11.414	23.399	177.666
5B	False	False	False	int4wo	6.237	18.221	1130.86
5B	False	True	False	int4wo	6.824	18.806	1127.56
5B	True	False	False	int4wo	6.235	18.217	1068.31
5B	True	True	False	int4wo	6.825	18.809	1067.26
5B	False	False	False	int4dq	11.48	23.463	340.204
5B	False	True	False	int4dq	12.785	24.771	323.873
5B	True	False	False	int4dq	11.48	23.466	219.393
5B	True	True	False	int4dq	12.785	24.774	218.592
5B	False	False	False	fp6	7.902	19.886	283.478
5B	False	True	False	fp6	8.734	20.718	281.083
5B	True	False	False	fp6	7.9	19.885	205.123
5B	True	True	False	fp6	8.734	20.719	204.564
5B	False	False	False	autoquant	19.763	24.938	540.621
5B	False	True	False	autoquant	21.978	27.1	504.031
5B	True	False	False	autoquant	19.763	24.73	176.794
5B	True	True	False	autoquant	21.978	26.948	177.122
5B	False	False	False	sparsify	6.743	18.727	308.767
5B	False	True	False	sparsify	7.439	19.433	300.013
2B	False	False	False	fp16	12.535	24.511	96.918
2B	False	True	False	fp16	13.169	25.142	96.610
2B	True	False	False	fp16	12.524	24.498	83.938
2B	True	True	False	fp16	13.169	25.143	84.694
2B	False	False	False	bf16	12.55	24.528	93.896
2B	False	True	False	bf16	13.194	25.171	93.396
2B	True	False	False	bf16	12.486	24.526	81.224
2B	True	True	False	bf16	13.13	25.171	81.520
2B	False	False	False	fp6	6.125	18.164	95.684
2B	False	True	False	fp6	6.769	18.808	91.698
2B	True	False	False	fp6	6.125	18.164	72.261
2B	True	True	False	fp6	6.767	18.808	90.585
2B	False	False	False	int8wo	6.58	18.621	102.941
2B	False	True	False	int8wo	6.894	18.936	102.403
2B	True	False	False	int8wo	6.577	18.618	81.389
2B	True	True	False	int8wo	6.891	18.93	83.079
2B	False	False	False	int8dq	6.58	18.621	197.254
2B	False	True	False	int8dq	6.894	18.936	190.125
2B	True	False	False	int8dq	6.58	18.621	75.16
2B	True	True	False	int8dq	6.891	18.933	74.981
2B	False	False	False	int4dq	7.344	19.385	132.155
2B	False	True	False	int4dq	7.762	19.743	122.657
2B	True	False	False	int4dq	7.395	19.374	83.103
2B	True	True	False	int4dq	7.762	19.741	82.642
2B	False	False	False	int4wo	4.155	16.138	363.792
2B	False	True	False	int4wo	4.345	16.328	361.839
2B	True	False	False	int4wo	4.155	16.139	342.817
2B	True	True	False	int4wo	4.354	16.339	341.48
2B	False	False	False	autoquant	12.55	19.734	185.023
2B	False	True	False	autoquant	13.194	20.319	177.602
2B	True	False	False	autoquant	12.55	19.565	75.005
2B	True	True	False	autoquant	13.195	20.191	74.807
2B	False	False	False	sparsify	4.445	16.431	125.59
2B	False	True	False	sparsify	4.652	16.635	121.357

H100

model_type	compile	fuse_qkv	quantize_vae	quantization	model_memory	inference_memory	time
5B	False	True	False	fp16	21.978	33.988	113.945
5B	True	True	False	fp16	21.979	33.99	87.155
5B	False	True	False	bf16	21.979	33.988	112.398
5B	True	True	False	bf16	21.979	33.987	87.455
5B	False	True	False	fp8	11.374	23.383	113.167
5B	True	True	False	fp8	11.374	23.383	75.255
5B	False	True	False	int8wo	11.414	23.422	123.144
5B	True	True	False	int8wo	11.414	23.423	87.026
5B	True	True	False	int8dq	11.412	59.355	78.945
5B	False	True	False	int4dq	12.785	24.793	151.242
5B	True	True	False	int4dq	12.785	24.795	87.403
5B	False	True	False	int4wo	6.824	18.829	667.125

Through visual inspection of various outputs, we identified that the best results were achieved with int8 weight-only quantization, int8 dynamic quantization, fp8 (currently supported only on Hopper architecture), and autoquant. While the outputs sometimes differed visually from their standard fp16/bf16 counterparts, they maintained the expected quality. Additionally, we observed that int4 dynamic quantization generally produced satisfactory results in most cases, but showed greater deviation in structure, color, composition and motion.

With the newly added fp8dqrow scheme, the inference latency is 76.70 seconds for CogVideoX-5b (batch size: 1 , steps: 50, frames: 49, resolution: 720x480) on an H100. fp8dqrow has more scales per tensors and less quantization error. The quality, from visual inspection, is very close to fp16/bf16 and better than int8 in many cases.

TorchAO also supports arbitary exponent and mantissa bits for floating point types, which provides experimental freedom to find the best settings for your models. Here, we also share results with fp6_e3m2, fp5_e2m2 and fp4_e2m1. We find that fp6 and fp5 quantizations can preserve good generation quality and match the expectation from fp16 precision most of the time. To achieve a balance between speed and quality, the recommended quantization dtypes for lower VRAM GPUs are int8dq, fp8dqrow, fp6_e3m2 and autoquant which, when compiled, are faster or close in performance to their bf16 counterparts.

Additional `fp8dqrow`, `fp6_e3m2`, `fp5_e2m2` and `fp4_e2m1` benchmarks

H100

model_type	compile	fuse_qkv	quantize_vae	quantization	model_memory	inference_memory	time
5B	False	False	False	fp8dqrow	10.28	22.291	122.99
5B	False	True	False	fp8dqrow	11.389	23.399	118.205
5B	True	False	False	fp8dqrow	10.282	22.292	76.777
5B	True	True	False	fp8dqrow	11.391	23.4	76.705

A100

model_type	compile	fuse_qkv	quantize_vae	quantization	model_memory	inference_memory	time
5B	False	False	False	fp6_e3m2	7.798	21.028	287.842
5B	True	False	False	fp6_e3m2	7.8	21.028	208.499
5B	False	True	False	fp6_e3m2	8.63	23.243	285.294
5B	True	True	False	fp6_e3m2	8.631	23.243	208.513
5B	False	False	False	fp5_e2m2	6.619	21.02	305.401
5B	True	False	False	fp5_e2m2	6.622	21.021	217.707
5B	False	True	False	fp5_e2m2	7.312	23.237	304.725
5B	True	True	False	fp5_e2m2	7.312	23.237	213.837
5B	False	False	False	fp4_e2m1	5.423	21.012	282.835
5B	True	False	False	fp4_e2m1	5.422	21.013	207.719
5B	False	True	False	fp4_e2m1	5.978	23.228	280.262
5B	True	True	False	fp4_e2m1	5.977	23.227	207.520

Note

From our testing and feedback from various folks that tried out torchao quantization after the release of CogVideoX, it was found that Ampere and above architectures had the best support for quantization dtypes. For other architectures such as Turing or Volta, quantizing the models did not help save memory or the inference errored out. It was particularly pointed out to be erroneous with the Apple mps backend. Support for other architectures will only get better with time.

CogVideoX memory savings

• From the table, it can be seen that the memory required to load the standard bf16 model into memory is about 19.7 GB, and to run inference is about 31.7 GB. To keep the quality on par, let's quantize using int8 weight-only. This requires about 10.3 GB to load the memory in model, and 22.2 GB to run inference:

Code

import torch
from diffusers import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel, CogVideoXPipeline
from diffusers.utils import export_to_video
from transformers import T5EncoderModel
from torchao.quantization import quantize_, int8_weight_only

model_id = "THUDM/CogVideoX-5b"

text_encoder = T5EncoderModel.from_pretrained(model_id, subfolder="text_encoder", torch_dtype=torch.bfloat16)
quantize_(text_encoder, int8_weight_only())

transformer = CogVideoXTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16)
quantize_(transformer, int8_weight_only())

vae = AutoencoderKLCogVideoX.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.bfloat16)
quantize_(vae, int8_weight_only())

# Create pipeline and run inference
pipe = CogVideoXPipeline.from_pretrained(
    model_id,
    text_encoder=text_encoder,
    transformer=transformer,
    vae=vae,
    torch_dtype=torch.bfloat16,
).to("cuda")

prompt = "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance."
video = pipe(prompt=prompt, num_inference_steps=1).frames[0]
export_to_video(video, "output.mp4", fps=8)

• Let's enable CPU offloading for models as described in diffusers-specific optimizations. Initially, no models are loaded onto the GPU and everything resides on the CPU. It requires about 10.3 GB to keep all components on the CPU. However, the peak memory used during inference drops to 12.4 GB. Note that inference will be slightly slower due to the time required to move different modeling components between CPU to GPU and back.

pipe = CogVideoXPipeline.from_pretrained(
    model_id,
    text_encoder=text_encoder,
    transformer=transformer,
    vae=vae,
    torch_dtype=torch.bfloat16,
- ).to("cuda")
+ )

+ pipe.enable_model_cpu_offload()

• Let's enable VAE tiling as described in diffusers-specific optimizations to further reduce memory usage at inference to 7.9 GB.

pipe = ...
pipe.enable_model_cpu_offload()

+ pipe.vae.enable_tiling()

• Instead of pipe.enable_model_cpu_offload(), one can use pipe.enable_sequential_cpu_offload() that brings down memory usage to 4.8 GB without quantization and 3.1 GB with quantization. Note that sequential cpu offloading comes at a tradeoff with much more time required during inference. You are required to install accelerate from source until next release for this to work without any errors.

pipe = ...
- pipe.enable_model_cpu_offload()
+ pipe.enable_sequential_cpu_offload()

+ pipe.vae.enable_tiling()

Note

We use torch.cuda.max_memory_allocated() to report the peak memory values.

Diffusers-specific optimizations

For supported architectures, memory requirements could further be brought down using Diffusers-supported functionality:

• pipe.enable_model_cpu_offload(): Only keeps the active Diffusers-used models (text encoder, transformer/unet, vae) on device
• pipe.enable_sequential_cpu_offload(): Similar to above, but performs cpu offloading more aggressively by only keeping active torch modules on device
• pipe.vae.enable_vae_tiling(): Enables tiled encoding/decoding by breaking up latents into smaller tiles and performing respective operation on each tile
• pipe.vae.enable_vae_slicing(): Helps keep memory usage constant when generating more than one image/video at a time

Autoquant and autotuning

Given these many options around quantization, which one do I choose for my model? Enter "autoquant". It tries to quickly and accurately quantize your model. By the end of the process, it creates a "quantization plan" which can be accessed through AUTOQUANT_CACHE and reused.

So, we would essentially do after performing quantization with autoquant and benchmarking:

from torchao.quantization.autoquant import AUTOQUANT_CACHE
import pickle 

with open("quantization-cache.pkl", "wb") as f:
    pickle.dump(AUTOQUANT_CACHE)

And then to reuse the plan, we would do in our final codebase:

from torchao.quantization.autoquant import AUTOQUANT_CACHE
with open("quantization-cache.pkl", "rb") as f:
    AUTOQUANT_CACHE.update(pickle.load(f))

Know more about "autoquant" here.

Another useful (but time-consuming) feature of torchao is "autotuning". It tunes the int_scaled_matmul kernel for int8 dynamic + int8 weight quantization for the shape at runtime (given the shape of tensor passed to int_scaled_matmul op). Through this process, it tries to identify the most efficient kernel configurations for a given model and inputs.

To launch quantization benchmarking with autotuning, we need to enable the TORCHAO_AUTOTUNER_ENABLE. So, essentially: TORCHAO_AUTOTUNER_ENABLE=1 TORCHAO_AUTOTUNER_DATA_PATH=my_data.pkl python my_script.py. And when it's done, we can simply reuse the configs it found by doing: TORCHAO_AUTOTUNER_DATA_PATH=my_data.pkl python my_script.py.

If you're using autotuning, keep in mind that it only works for intX quantization, for now and it is quite time-consuming.

Note

Autoquant and autotuning are two different features.

Reducing quantization time and peak memory

If we keep the model on CPU and quantize it, it takes a long time while keeping the peak memory minimum. How about we do both i.e., quantize fast while keeping peak memory to a bare minimum?

It is possible to pass a device argument to the quantize_() method of torchao. It basically moves the model to CUDA and quantizes each parameter individually:

quantize_(model, int8_weight_only(), device="cuda")

Here's a comparison:

Quantize on CPU:
  - Time taken: 10.48 s
  - Peak memory: 6.99 GiB
Quantize on CUDA:
  - Time taken: 1.96 s
  - Peak memory: 14.50 GiB
Move to CUDA and quantize each param individually:
  - Time taken: 1.94 s
  - Peak memory: 8.29 GiB

Check out this pull request for more details.

Training with FP8

Check out the training directory.

Serialization and loading quantized models

Check out our serialization and loading guide here.

Things to keep in mind when benchmarking

In this section, we provide a non-exhaustive overview of the things we learned during the benchmarking process.

• Expected gains and their ceiling are dependent on the hardware being used. For example, compute density of the operations popped on a GPU has an effect on on the speedup. For the same code, you may see better numbers on an A100 than H100, simply because the operations weren't compute-dense enough for H100. In these situations, bigger batch sizes might make the effect of using a better GPU like H100 more pronounced.

• Shapes matter. Not all models are created equal. Certain shapes are friendlier in order for quantization to show its benefits over others. Usually, bigger shapes benefit quantization, resulting into speedups. The thinner the dimensions, the less pronounced the effects of quantization, especially for precisions like int8. In our case, using quantization on smaller models like PixArt-Sigma wasn't particularly beneficial. This is why, torchao provides an "autoquant" option that filters out smaller layers to exclude from quantization.

• Small matmuls. If the matmuls of the underlying are small enough or the performance without quantization isn't bottlenecked by weight load time, these techniques may reduce performance.

• Cache compilation results. torch.compile() can take long just like any other deep-learning compiler. So, it is always recommended to cache the compilation results. Refer to the official guide to know more. Additionally, we can configure the ENABLE_AOT_AUTOGRAD_CACHE flag for faster compilation times.

• Compilation is a time-consuming process. The first time we compile, it takes a lot of time because a lot of things are getting figured out under the hood (best kernel configs, fusion strategies, etc.). The subsequent runs will be significantly faster, though. Also, for the benchmarking scripts provided in inference/, we run a couple of warmup runs to reduce the variance in our numbers as much as possible. So, if you are running the benchmarks, do expect them to take long.

Benefitting from torch.compile()

In this section, we provide a rundown of the scenarios that may prevent your model to optimally benefit from torch.compile(). This is very specific to torch.compile() and the FluxPipeline.

• Ensure there are no graph-breaks when torch.compile() is applied on the model. Briefly, graph-breaks introduce unnecessary overheads blocking torch.compile() to obtain a full and dense graph of your model. In the case of Flux, we identified that it came from position embeddings, which was fixed in the following PRs: #9307 and #9321. Thanks to Yiyi.

• Use the torch.profiler.profile() to get a kernel trace to identify if there is any graph break. You could use a script like this. This will give you a JSON file which you can upload to https://ui.perfetto.dev/ to view the trace. Additionally, use this guide to validate the memory wins when using torchao for quantization and combining it with torch.compile().

• Finally, this torch.compile() manual is a gem of a reading to get an idea of how to go about approaching the profiling process.

Acknowledgement

We acknowledge the generous help and guidance provided by the PyTorch team throughout the development of this project:

• Christian Puhrsch for guidance on removing graph-breaks and general torch.compile() stuff
• Jerry Zhang for different torchao stuff (microbenchmarks, serialization, misc discussions)
• Driss Guessous for all things FP8
• Jesse Cai for help on int8_dynamic_activation_int8_semi_sparse_weight
• Mark Saroufim for reviews, discussions, and navigation