一个比SDXL更快的模型——Stable Cascade【必坑指北】

2024年的春节假期，AIGC界又发生了重大革命性事件。

OpenAI 发布了首款文生视频模型------Sora。简单来说就是，AI视频要变天了！之前的SVD，还是Google的Lumiere最多就几十帧，大约十秒左右，但是Sora却是SOTA级别且达到60s长度的文生视频模型。
Stability AI 开源 Stable Cascade，重新修改了diffusion架构，并且速度比原来的sdxl更快。不过目前huggingface下的模型，我仍建议使用 20GB以上的VRAM，而X 平台用户 @GozukaraFurkan 发文表示它只需要大约 9GB 的 GPU 内存，且速度依旧能保持得较好，具体优化需等GitHub发布了。
Apple 开售 Vision Pro，继 Meta Quest 3 之后又一位元宇宙的重量级选手。虽然Pico和黑鲨暂时退热了，但是硅谷依旧热情高涨，值得向往学习。

部署

javascript 复制代码

#install `diffusers` from this branch while the PR is WIP
pip install git+https://github.com/kashif/diffusers.git@wuerstchen-v3

官方最新的有问题，会提示 If you want to instead overwrite randomly initialized weights, please make sure to pass both low_cpu_mem_usage=False and ignore_mismatched_sizes=True.

perl 复制代码

# 参考 https://github.com/kijai/ComfyUI-DiffusersStableCascade/issues/13
pip install --force-reinstall --no-deps git+https://github.com/huggingface/diffusers.git@a3dc21385b7386beb3dab3a9845962ede6765887

代码

ini 复制代码

import torch
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline

device = "cuda"
dtype = torch.bfloat16
num_images_per_prompt = 2

prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=dtype).to(device)
decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade",  torch_dtype=dtype).to(device)

prompt = "Anthropomorphic cat dressed as a pilot"
negative_prompt = ""

with torch.cuda.amp.autocast(dtype=dtype):
    prior_output = prior(
        prompt=prompt,
        height=1024,
        width=1024,
        negative_prompt=negative_prompt,
        guidance_scale=4.0,
        num_images_per_prompt=num_images_per_prompt,
    )
    decoder_output = decoder(
        image_embeddings=prior_output.image_embeddings,
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=0.0,
        output_type="pil",
    ).images

代码

ini 复制代码

import torch
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline

device = "cuda"
num_images_per_prompt = 2

prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=torch.bfloat16).to(device)
decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade",  torch_dtype=torch.float16).to(device)

prompt = "Anthropomorphic cat dressed as a pilot"
negative_prompt = ""

prior_output = prior(
    prompt=prompt,
    height=1024,
    width=1024,
    negative_prompt=negative_prompt,
    guidance_scale=4.0,
    num_images_per_prompt=num_images_per_prompt,
    num_inference_steps=20
)
decoder_output = decoder(
    image_embeddings=prior_output.image_embeddings.half(),
    prompt=prompt,
    negative_prompt=negative_prompt,
    guidance_scale=0.0,
    output_type="pil",
    num_inference_steps=10
).images

#Now decoder_output is a list with your PIL images