Huggingface的方式安装
在Huggingface个人目录下有一个token号，这个tokens号要在服务器登陆的过程中进行添加；
1，在服务器登陆要输入huggingface登陆：
huggingface-cli login # READ 权限的就够了 运行完命令，按照提示将之前的token复制过来，然后 Y ，回车，登陆成功！
2，相关包安装
pip install --upgrade diffusers transformers scipy pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113 pip install accelerate # 这个加速包可以不装，但是他一直提示看着难受的就装了 3，运行模式
import torch from torch import autocast from diffusers import StableDiffusionPipeline model_id = "CompVis/stable-diffusion-v1-1" device = "cuda" pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True) pipe = pipe.to(device) prompt = "a photo of an astronaut riding a horse on mars" with autocast("cuda"): image = pipe(prompt, guidance_scale=7.5)["sample"][0] image.save("astronaut_rides_horse.png") 哈哈，报错，error key ‘sample’
打印下pipe看看
只有两个key：images 和 nsfw_content_detected
那改成 images试试
import torch from torch import autocast from diffusers import StableDiffusionPipeline model_id = "CompVis/stable-diffusion-v1-1" device = "cuda" pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True) pipe = pipe.to(device) prompt = "a photo of an astronaut riding a horse on mars" with autocast("cuda"): image = pipe(prompt, guidance_scale=7.5)["images"][0] image.save("astronaut_rides_horse.png") 第一次运行会下载模型，需要点时间，生成的时候显存大概占用12G
成功了，看下生成的怎么样。
马马虎虎，还有很大优化空间。
补充
显卡显存不足10G的话可以改成float16 precision
import torch from torch import autocast from diffusers import StableDiffusionPipeline model_id = "CompVis/stable-diffusion-v1-1" device = "cuda" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) #pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True) pipe = pipe.to(device) prompt = "a photo of an astronaut riding a horse on mars" with autocast("cuda"): image = pipe(prompt, guidance_scale=7.5)["images"][0] image.save("astronaut_rides_horse.png") #To swap out the noise scheduler, pass it to from_pretrained:
要交换噪声调度器，将其传递给from_pretrained：
import torch from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler model_id = "CompVis/stable-diffusion-v1-1" # Use the K-LMS scheduler here instead scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000) pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, use_auth_token=True) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" with autocast("cuda"): image = pipe(prompt, guidance_scale=7.5)["images"][0] image.save("astronaut_rides_horse.png")
哈哈啊哈啊
PS：huggingface-cli: command not found
参考
CompVis/stable-diffusion-v1-1（Hugging Face）
模型方法–Stable Diffusion
CompVis/stable-diffusion（github）
Stable Diffusion with ? Diffusers
