网页界面设计的功能性主要体现在信息的哪两个方面培训班线上优化

1. 普通模型

Optimum是huggingface transformers库的一个扩展包，用来提升模型在指定硬件上的训练和推理性能。Optimum支持多种硬件，不同硬件下的安卓方式如下：

如果是国内安装的话，记得加上-i https://pypi.tuna.tsinghua.edu.cn/simple。
hugging face目前是被墙的状态，在使用示例代码时，需要将模型离线下载下来使用。
如下图，模型离线下载下来后的测试代码如下：

对比原模型，提速约一倍：

2. stable diffusion

首先安装diffusion库：pip install optimum[diffusers]
下载模型文件：

hugging face上是如下这个：

unet模型有3个多G，下载好后按照上面文件夹的格式放在程序的目录下。接下来是代码：

from optimum.intel import OVStableDiffusionPipeline
model_id = "echarlaix/stable-diffusion-v1-5-openvino"
pipeline = OVStableDiffusionPipeline.from_pretrained(model_id)
prompt = "sailing ship in storm by Rembrandt"
images = pipeline(prompt).images

如果我们是从pytorch模型export进来的，注意保存一下ov的模型：

model_id = "runwayml/stable-diffusion-v1-5"
pipeline = OVStableDiffusionPipeline.from_pretrained(model_id, export=True)
# Don't forget to save the exported model
pipeline.save_pretrained("openvino-sd-v1-5")

然后固定尺寸，加速推理：

# Define the shapes related to the inputs and desired outputs
batch_size = 1
num_images_per_prompt = 1
height = 512
width = 512# Statically reshape the model
pipeline.reshape(batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images_per_prompt)
# Compile the model before the first inference
pipeline.compile()# Run inference
images = pipeline(prompt, height=height, width=width, num_images_per_prompt=num_images_per_prompt).images

如果要添加Textual Inversion

pipeline.clear_requests()# Load textual inversion into stable diffusion pipeline
pipeline.load_textual_inversion("sd-concepts-library/cat-toy", "<cat-toy>")# Compile the model before the first inference
pipeline.compile()
image2 = pipeline(prompt, num_inference_steps=50).images[0]
image2.save("stable_diffusion_v1_5_with_textual_inversion.png")

如果是image-to-image：

import requests
import torch
from PIL import Image
from io import BytesIO
from optimum.intel import OVStableDiffusionImg2ImgPipelinemodel_id = "runwayml/stable-diffusion-v1-5"
pipeline = OVStableDiffusionImg2ImgPipeline.from_pretrained(model_id, export=True)url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
response = requests.get(url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")
init_image = init_image.resize((768, 512))
prompt = "A fantasy landscape, trending on artstation"
image = pipeline(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images[0]
image.save("fantasy_landscape.png")

结果如图：