【深度学习】PixArt-Sigma 实战【3】速度测试

css 复制代码
import time

import torch
from diffusers import Transformer2DModel, PixArtSigmaPipeline
from diffusers import ConsistencyDecoderVAE

device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
weight_dtype = torch.float16

pipe = PixArtSigmaPipeline.from_pretrained(
    "./PixArt-Sigma-XL-2-1024-MS",
    torch_dtype=weight_dtype,
    use_safetensors=True,
)
pipe.to(device)

# transformer = Transformer2DModel.from_pretrained(
#     # "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
#     # "/ssd/xiedong/PixArt/PixArt-Sigma-XL-2-2K-MS",
#     "/ssd/xiedong/PixArt/PixArt-Sigma-XL-2-2K-MS",
#     subfolder='transformer',
#     torch_dtype=weight_dtype,
# )
# pipe = PixArtSigmaPipeline.from_pretrained(
#     # "PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers",
#     "/ssd/xiedong/PixArt/PixArt-sigma/output/pixart_sigma_sdxlvae_T5_diffusers",
#     transformer=transformer,
#     torch_dtype=weight_dtype,
#     use_safetensors=True,
# )
# pipe.vae = ConsistencyDecoderVAE.from_pretrained("/ssd/xiedong/PixArt/consistency-decoder", torch_dtype=torch.float16)
# pipe.to(device)

# Enable memory optimizations.
# pipe.enable_model_cpu_offload()

time1 = time.time()
prompt = "A small cactus with a happy face in the Sahara desert."
image = pipe(prompt).images[0]
time2 = time.time()
print(f"time use:{time2 - time1}")
image.save("./catcus.png")

time1 = time.time()
prompt = "A small cactus with a happy face in the Sahara desert."
image = pipe(prompt).images[0]
time2 = time.time()
print(f"time use:{time2 - time1}")
image.save("./catcus.png")

A100速度 20轮4.4秒。

Loading pipeline components...: 0%| | 0/5 [00:00<?, ?it/s]You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the legacy (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set legacy=False. This should only be set if you understand what it means, and thouroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565

Loading pipeline components...: 60%|██████ | 3/5 [00:01<00:01, 1.65it/s]

Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]

Loading checkpoint shards: 50%|█████ | 1/2 [00:01<00:01, 1.83s/it]

Loading checkpoint shards: 100%|██████████| 2/2 [00:03<00:00, 1.70s/it]

Loading pipeline components...: 100%|██████████| 5/5 [00:11<00:00, 2.29s/it]

100%|██████████| 20/20 [00:05<00:00, 3.89it/s]

time use:6.027105093002319

100%|██████████| 20/20 [00:04<00:00, 4.94it/s]

time use:4.406545162200928

相关推荐
东临碣石8244 分钟前
【AI论文】EnerVerse-AC:用行动条件来构想具身环境
人工智能
lqjun08271 小时前
PyTorch实现CrossEntropyLoss示例
人工智能·pytorch·python
心灵彼岸-诗和远方1 小时前
芯片生态链深度解析(三):芯片设计篇——数字文明的造物主战争
人工智能·制造
小蜗笔记1 小时前
显卡、Cuda和pytorch兼容问题
人工智能·pytorch·python
高建伟-joe1 小时前
内容安全:使用开源框架Caffe实现上传图片进行敏感内容识别
人工智能·python·深度学习·flask·开源·html5·caffe
Cloud Traveler2 小时前
迁移学习:解锁AI高效学习与泛化能力的密钥
人工智能·学习·迁移学习
IT_xiao小巫2 小时前
AI 实践探索:辅助生成测试用例
人工智能·测试用例
一切皆有可能!!2 小时前
ChromaDB 向量库优化技巧实战
人工智能·语言模型
星川皆无恙2 小时前
大模型学习:Deepseek+dify零成本部署本地运行实用教程(超级详细!建议收藏)
大数据·人工智能·学习·语言模型·架构
观测云2 小时前
观测云产品更新 | 安全监测、事件中心、仪表板AI智能分析等
人工智能·安全