K-【学习Diffusers 一】随机种子控制生成 加载自己的UNet

1 控制随机种子
generator = torch.Generator("cuda").manual_seed(1024)

# 1 导入torch,pipline
import torch 
from diffusers import StableDiffusionPipeline
from accelerate import Accelerator

# 2 生成种子1024是宇航员种子
generator = torch.Generator("cuda").manual_seed(1024) # 

# 3 导入模型名
model_id = "runwayml/stable-diffusion-v1-5"

# 4 生成器送入pipline
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, generator=generator)

# 5 pipe送入cuda
pipe = pipe.to("cuda")

# 6 生成提示信息
prompt = "a photo of an astronaut riding a horse on mars"
# 'An astronaut riding a horse on Mars'

# 7 开始生成
image = pipe(prompt, generator=generator).images[0] 
 
# 8保存图
image.save("astronaut_rides_horse.png")

2 采用自己的unet或pipeline方法

from diffusers_inheritv2 import StableDiffusionPipeline 
from diffusers_inheritv2.models.unet_2d_condition import UNet2DConditionModel as NewUNet

# 生成pipline
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, generator=generator)

# 生成unet
unet = NewUNet.from_pretrained(model_id, subfolder="unet").to(torch.float16)  # -> yw

# unet送入pipe
pipe.unet = unet.to(torch.float16)