2024.05.14 Diffusion 代码学习笔记

配环境

我个人用的是Geowizard的环境：https://github.com/fuxiao0719/GeoWizard。

出于方便考虑，用的pytorch官方的docker容器，因此python版本（3.10）和原作者（3.9）不同，其余都是一样的。

https://hub.docker.com/r/pytorch/pytorch/tags?page=&page_size=&ordering=&name=2.0.1

安装的时候，把requirements.txt 里面开头的torch和torchvision删掉就行了（因为我们的docker容器里已经有了，版本也是一样的）。

如果遇到以下两个报错可以这样解决：
apt install libgl1-mesa-glx # ImportError: libGL.so.1: cannot open shared object file: No such file or dir
apt-get install libxrender1 # ImportError: libXrender.so.1: cannot open shared object file: No such file or directory

hugging face的下载问题

把原网站换成镜像，不然下不下来。
pip install -U huggingface_hub
export HF_ENDPOINT=https://hf-mirror.com

入门学习

网站

就用这个网站的tutorial就行：
https://huggingface.co/docs/diffusers/tutorials

打不开的话看这个镜像：把huggingface.co换成hf-mirror.com即可
https://hf-mirror.com/docs/diffusers/tutorials/autopipeline
注意，没有魔法的话，镜像中还是无法显示图片。但代码文字都有，所以也可以将就着用了。

学习内容

我看的2024.05.15版本是这些内容：

学习建议

• 我先开始用的还是pycharm。后来感觉还是jupyter notebook方便一点。
• 每个教程可能会用到不同的stable diffusion模型，比如"stabilityai/stable-diffusion-xl-base-1.0"。 每次下载模型都很大，费时且费存储空间。
  • 如果只是学习的话，可以试试都用““CompVis/stable-diffusion-v1-4”， 如果报错了再试试"runwayml/stable-diffusion-v1-5"，实在不行再换成教程里的。这样就不用老下载新模型了
• 建议先看好所有要学的教程，把里面的模型都load完，然后再一个个仔细看，因为模型下载真的很费时

学习总结

diffusers的用法

• 简单的说，可以直接使用一个pipeline。也可以用scheduler和model。

• model一般包括一个最基本的UNet（输入current xt和t，输出predicted noise）

  • 如果是latent diffusion的话还有VAE
  • 如果是text conditioned的话还有tokenlizer和text_encoder

• 有的scheduler有noise scaler参数，比如这位：
  https://huggingface.co/docs/diffusers/v0.27.2/en/api/schedulers/unipc#diffusers.UniPCMultistepScheduler

• AutoPipeline就是，指定模型（比如runwayml/stable-diffusion-v1-5）和任务（比如text2img, img2img, 或inpainting），自动给你找到那个pipeline对应的类

• noise scheduler有一个add_noise方法，大概是下面这样：

  • 

其他

• 这个过程中会下载很多模型或数据集，默认的路径是：
  ~/.cache/huggingface/hub
• 可能需要pip install datasets, 下载的路径还是~/.cache/
• 如果还没研究明白怎么把模型上传到huggingface，可以先把training config里面的push_to_hub设置为False
• jupyter里面的PIL Image可以直接显示（运行代码image即可）；pycharm的话可以image.save(‘temp.png’), 然后看这个图

Understanding pipelines, models and schedulers

代码例子1：直接使用DDPM的pipeline

from diffusers import DDPMPipeline

ddpm = DDPMPipeline.from_pretrained("google/ddpm-cat-256", use_safetensors=True).to("cuda")
image = ddpm(num_inference_steps=25).images[0]
image
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代码例子2：分解DDPM的pipeline：先load scheduler 和model，然后使用

from diffusers import DDPMScheduler, UNet2DModel

scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256")
model = UNet2DModel.from_pretrained("google/ddpm-cat-256", use_safetensors=True).to("cuda")
scheduler.set_timesteps(50)

import torch

sample_size = model.config.sample_size
noise = torch.randn((1, 3, sample_size, sample_size), device="cuda")

input = noise

for t in scheduler.timesteps:
    with torch.no_grad():
        noisy_residual = model(input, t).sample
    previous_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
    input = previous_noisy_sample
from PIL import Image
import numpy as np

image = (input / 2 + 0.5).clamp(0, 1).squeeze()
image = (image.permute(1, 2, 0) * 255).round().to(torch.uint8).cpu().numpy()
image = Image.fromarray(image)
image
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代码例子2：stable diffusion的inference

https://hf-mirror.com/docs/diffusers/using-diffusers/write_own_pipeline

# https://huggingface.co/docs/diffusers/using-diffusers/write_own_pipeline


from PIL import Image
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler

'''Load Stuff'''
vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae", use_safetensors=True)
tokenizer = CLIPTokenizer.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(
    "CompVis/stable-diffusion-v1-4", subfolder="text_encoder", use_safetensors=True
)
unet = UNet2DConditionModel.from_pretrained(
    "CompVis/stable-diffusion-v1-4", subfolder="unet", use_safetensors=True
)

from diffusers import UniPCMultistepScheduler

scheduler = UniPCMultistepScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler")

torch_device = "cuda"
vae.to(torch_device)
text_encoder.to(torch_device)
unet.to(torch_device)

'''Prepare and process input text'''
prompt = ["a photograph of an astronaut riding a horse"]
# prompt = ["a photograph of the cartoon character SpongeBob SqurePants"]
height = 512  # default height of Stable Diffusion
width = 512  # default width of Stable Diffusion
num_inference_steps = 25  # Number of denoising steps
guidance_scale = 7.5  # Scale for classifier-free guidance
generator = torch.manual_seed(1)  # Seed generator to create the initial latent noise
batch_size = len(prompt)

text_input = tokenizer(
    prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt"
)

with torch.no_grad():
    text_embeddings = text_encoder(text_input.input_ids.to(torch_device))[0]

max_length = text_input.input_ids.shape[-1]
uncond_input = tokenizer([""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt")
uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0]

text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

'''Diffuse'''
latents = torch.randn(
    (batch_size, unet.config.in_channels, height // 8, width // 8),
    generator=generator
).to(torch_device)

latents = latents * scheduler.init_noise_sigma

from tqdm.auto import tqdm

scheduler.set_timesteps(num_inference_steps)

for t in tqdm(scheduler.timesteps):
    # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
    latent_model_input = torch.cat([latents] * 2)

    latent_model_input = scheduler.scale_model_input(latent_model_input, timestep=t)

    # predict the noise residual
    with torch.no_grad():
        noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample

    # perform guidance
    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

    # compute the previous noisy sample x_t -> x_t-1
    latents = scheduler.step(noise_pred, t, latents).prev_sample

'''Decode the image: latent to rgb'''
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
with torch.no_grad():
    image = vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1).squeeze()
image = (image.permute(1, 2, 0) * 255).to(torch.uint8).cpu().numpy()
image = Image.fromarray(image)
image.save('temp.png')
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结果

下面是我seed为0和1分别生成的结果：
prompt：a photograph of an astronaut riding a horse

AutoPipeline

task = 'img2img' # text2img, img2img, inpainting
if task == 'text2img':
    from diffusers import AutoPipelineForText2Image
    import torch


    pipeline = AutoPipelineForText2Image.from_pretrained(
        "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
    ).to("cuda")
    prompt = "peasant and dragon combat, wood cutting style, viking era, bevel with rune"

    image = pipeline(prompt, num_inference_steps=25).images[0]
    image.save('temp.png')

if task == 'img2img':
    from diffusers import AutoPipelineForImage2Image
    import torch
    import requests
    from PIL import Image
    from io import BytesIO

    pipeline = AutoPipelineForImage2Image.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        torch_dtype=torch.float16,
        use_safetensors=True,
    ).to("cuda")
    prompt = "a portrait of a dog wearing a pearl earring"

    url = "https://upload.wikimedia.org/wikipedia/commons/thumb/0/0f/1665_Girl_with_a_Pearl_Earring.jpg/800px-1665_Girl_with_a_Pearl_Earring.jpg"

    response = requests.get(url)
    image = Image.open(BytesIO(response.content)).convert("RGB")
    image.thumbnail((768, 768))
    image.save('1665_Girl_with_a_Pearl_Earring.png')
    image = pipeline(prompt, image, num_inference_steps=200, strength=0.75, guidance_scale=10.5).images[0]
    image.save('temp.png')

if task == 'inpainting':
    from diffusers import AutoPipelineForInpainting
    from diffusers.utils import load_image
    import torch

    pipeline = AutoPipelineForInpainting.from_pretrained(
        # "stabilityai/stable-diffusion-xl-base-1.0",
        "runwayml/stable-diffusion-v1-5",
        torch_dtype=torch.float16, use_safetensors=True
    ).to("cuda")

    img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
    mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"

    init_image = load_image(img_url).convert("RGB")
    mask_image = load_image(mask_url).convert("RGB")
    init_image.save('init.png')
    mask_image.save('mask_image.png')
    prompt = "A majestic tiger sitting on a bench"
    image = pipeline(prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80).images[0]
    image.save('temp.png')

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结果

img2img: a portrait of a dog wearing a pearl earring

inpainiting的结果：A majestic tiger sitting on a bench

如果是随机mask来inpaint的结果：（注意，黑色是要保留的）

train a diffusion model

参考这个网站：
https://hf-mirror.com/docs/diffusers/tutorials/basic_training

注意，如果开头的config里面push_to_hub设置为True的话，则需要改一下hub_model_id, 同时需要前面的

from huggingface_hub import notebook_login
notebook_login()
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我试了一下有点问题，就直接设置为false了。

建议先把trianing跑完了，再仔细看trianing的代码.

核心代码：

for step, batch in enumerate(train_dataloader):
            clean_images = batch["images"]
            # Sample noise to add to the images
            noise = torch.randn(clean_images.shape, device=clean_images.device)
            bs = clean_images.shape[0]

            # Sample a random timestep for each image
            timesteps = torch.randint(
                0, noise_scheduler.config.num_train_timesteps, (bs,), device=clean_images.device,
                dtype=torch.int64
            )

            # Add noise to the clean images according to the noise magnitude at each timestep
            # (this is the forward diffusion process)
            noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)

            with accelerator.accumulate(model):
                # Predict the noise residual
                noise_pred = model(noisy_images, timesteps, return_dict=False)[0]
                loss = F.mse_loss(noise_pred, noise)
                accelerator.backward(loss)

                accelerator.clip_grad_norm_(model.parameters(), 1.0)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            progress_bar.update(1)
            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step}
            progress_bar.set_postfix(**logs)
            accelerator.log(logs, step=global_step)
            global_step += 1
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结果：