【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\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\

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
Flynn Liang23 分钟前
AI 应用的安全红线:Spring AI 下的 Prompt 注入防护、PII 脱敏、输出审计与密钥管理
人工智能·安全·spring·数据泄露·springai·prompt注入
朗迪锋28 分钟前
SoErgo重塑“四特”场景下的人机协同与安全边界
人工智能·安全·人机工程
stormzhangV35 分钟前
未来十年的建议
人工智能·aigc·agent
深圳行云创新1 小时前
智能体架构之争:单Agent加Skills,还是多加几个Agent
人工智能·架构·软件工程
用户360055579001 小时前
Kestrel_VLLM - 31 · MTP 头上 GPU:一个独立槽位,与一条抽出来共用的注意力
人工智能
老金带你玩AI1 小时前
Codex改了又测、测了又改,我一查Token才发现这个坑
人工智能
IT_陈寒1 小时前
当Java的hashCode遇上Lombok,我的缓存被全清了
前端·人工智能·后端
刘胡子大叔1 小时前
评测驱动优化:失败样本回流 Prompt 的闭环
人工智能·prompt
这个DBA有点耶1 小时前
内核级AI与多模融合:AI数据库在海外场景的落地要点
数据库·人工智能·架构