diffusers-Load pipelines,models,and schedulers
https://huggingface.co/docs/diffusers/using-diffusers/loading
https://huggingface.co/docs/diffusers/using-diffusers/loading
有一种简便的方法用于推理是至关重要的。扩散系统通常由多个组件组成，如parameterized model、tokenizers和schedulers，它们以复杂的方式进行交互。这就是为什么我们设计了DiffusionPipeline，将整个扩散系统的复杂性包装成易于使用的API，同时保持足够的灵活性，以适应其他用例，例如将每个组件单独加载作为构建块来组装自己的扩散系统。
1.Diffusion Pipeline
DiffusionPipeline是扩散模型最简单最通用的方法。
from diffusers import DiffusionPipeline
repo_id = "runwayml/stable-diffusion-v1-5"
pipe = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)也可以使用特定的pipeline
from diffusers import StableDiffusionPipeline
repo_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)Community pipelines是原始实现不同于DiffusionPipeline，例如StableDiffusionControlNetPipeline.
1.1 local pipeline
from diffusers import DiffusionPipeline
repo_id = "./stable-diffusion-v1-5" # local path
stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)from_pretrained()方法在检测到本地路径时不会下载。
1.2 swap components in a pipeline
可以使用另一个兼容的组件来自定义任何流程的默认组件。定制非常重要，因为：
- 更改调度器对于探索生成速度和质量之间的权衡是重要的。
- 模型的不同组件通常是独立训练的，您可以用性能更好的组件替换掉现有组件。
- 在微调过程中，通常只有一些组件（如UNet或文本编码器）进行训练。
from diffusers import DiffusionPipeline
repo_id = "runwayml/stable-diffusion-v1-5"
stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)
stable_diffusion.scheduler.compatiblesfrom diffusers import DiffusionPipeline, EulerDiscreteScheduler, DPMSolverMultistepScheduler
repo_id = "runwayml/stable-diffusion-v1-5"
scheduler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler")
stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, scheduler=scheduler, use_safetensors=True)可以将PNDMScheduler更换为EulerDiscreteScheduler，在回传到DiffusionPipeline中。
1.3 safety checker
safety checker可以根据已知的NSFW内容检查生成的输出，
from diffusers import DiffusionPipeline
repo_id = "runwayml/stable-diffusion-v1-5"
stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, safety_checker=None, use_safetensors=True)1.4 reuse components across pipelines
可以在多个pipeline中可以重复使用相同的组件，以避免将权重加载到RAM中2次
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
model_id = "runwayml/stable-diffusion-v1-5"
stable_diffusion_txt2img = StableDiffusionPipeline.from_pretrained(model_id, use_safetensors=True)
components = stable_diffusion_txt2img.components可以将components传递到另一个pipeline中，无需将权重重新加载到RAM中：
stable_diffusion_img2img = StableDiffusionImg2ImgPipeline(**components)下面的方式更加灵活：
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
model_id = "runwayml/stable-diffusion-v1-5"
stable_diffusion_txt2img = StableDiffusionPipeline.from_pretrained(model_id, use_safetensors=True)
stable_diffusion_img2img = StableDiffusionImg2ImgPipeline(
vae=stable_diffusion_txt2img.vae,
text_encoder=stable_diffusion_txt2img.text_encoder,
tokenizer=stable_diffusion_txt2img.tokenizer,
unet=stable_diffusion_txt2img.unet,
scheduler=stable_diffusion_txt2img.scheduler,
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
)1.5 checkpoint variants
以torch.float16保存，节省一半的内存，但是无法训练，EMA不用于推理，用于微调模型。
2. models
from diffusers import UNet2DConditionModel
repo_id = "runwayml/stable-diffusion-v1-5"
model = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet", use_safetensors=True)所有的权重都存储在一个safetensors中， 可以用.from_single_file()来加载模型。safetensors安全且加载速度快。
2.1 load different stable diffusion formats
.ckpt也可以用from_single_file()，但最好转成hf格式，可以使用diffusers官方提供的服务转：https://huggingface.co/spaces/diffusers/sd-to-diffusers
也可以使用脚本转：https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py
python ../diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path temporalnetv3.ckpt --original_config_file cldm_v15.yaml --dump_path ./ --controlnetA1111 Lora文件，diffusers可以使用load_lora_weights()加载lora模型：
from diffusers import DiffusionPipeline, UniPCMultistepScheduler
import torch
pipeline = DiffusionPipeline.from_pretrained(
"andite/anything-v4.0", torch_dtype=torch.float16, safety_checker=None
).to("cuda")
pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
# uncomment to download the safetensor weights
#!wget https://civitai.com/api/download/models/19998 -O howls_moving_castle.safetensors
pipeline.load_lora_weights(".", weight_name="howls_moving_castle.safetensors")
prompt = "masterpiece, illustration, ultra-detailed, cityscape, san francisco, golden gate bridge, california, bay area, in the snow, beautiful detailed starry sky"
negative_prompt = "lowres, cropped, worst quality, low quality, normal quality, artifacts, signature, watermark, username, blurry, more than one bridge, bad architecture"
images = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
width=512,
height=512,
num_inference_steps=25,
num_images_per_prompt=4,
generator=torch.manual_seed(0),
).images
from diffusers.utils import make_image_grid
make_image_grid(images, 2, 2)3.scheduler
scheduler没有参数化或训练；由配置文件定义。加载scheduler不会消耗大的内存，并且相同的配置文件可以用于各种不同的scheduler，比如下面的scheduler均可与StableDiffusionPipline兼容。
Diffusion流程本质上是由扩散模型和scheduler组成的集合，它们在一定程度上彼此独立。这意味着可以替换流程的某些部分，其中最好的例子就是scheduler。扩散模型通常只定义从噪声到较少噪声样本的前向传递过程，而调度器定义了整个去噪过程，包括：
去噪步骤是多少？随机的还是确定性的？用什么算法找到去噪样本？ 调度器可以非常复杂，并且经常在去噪速度和去噪质量之间进行权衡。
from diffusers import StableDiffusionPipeline
from diffusers import (
DDPMScheduler,
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
)
repo_id = "runwayml/stable-diffusion-v1-5"
ddpm = DDPMScheduler.from_pretrained(repo_id, subfolder="scheduler")
ddim = DDIMScheduler.from_pretrained(repo_id, subfolder="scheduler")
pndm = PNDMScheduler.from_pretrained(repo_id, subfolder="scheduler")
lms = LMSDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler")
euler_anc = EulerAncestralDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler")
euler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler")
dpm = DPMSolverMultistepScheduler.from_pretrained(repo_id, subfolder="scheduler")
# replace `dpm` with any of `ddpm`, `ddim`, `pndm`, `lms`, `euler_anc`, `euler`
pipeline = StableDiffusionPipeline.from_pretrained(repo_id, scheduler=dpm, use_safetensors=True)4.DiffusionPipline explained
作为一个类方法，DiffusionPipeline.from_pretrained()做两件事，1.下载推理所需的权重并缓存，一般存在在.cache文件中，2.将缓存文件中的model_index.json进行实例化。
feature_extractor--CLIPFeatureExtractor(transformers)；scheduler--PNDMScheduler；text_encoder--CLIPTextModel(transformers)；tokenizer--CLIPTokenizer(transformers)；unet--UNet2DConditionModel；vae--AutoencoderKL
{
"_class_name": "StableDiffusionPipeline",
"_diffusers_version": "0.6.0",
"feature_extractor": [
"transformers",
"CLIPImageProcessor"
],
"safety_checker": [
"stable_diffusion",
"StableDiffusionSafetyChecker"
],
"scheduler": [
"diffusers",
"PNDMScheduler"
],
"text_encoder": [
"transformers",
"CLIPTextModel"
],
"tokenizer": [
"transformers",
"CLIPTokenizer"
],
"unet": [
"diffusers",
"UNet2DConditionModel"
],
"vae": [
"diffusers",
"AutoencoderKL"
]
}下面是runway/stable-diffusion-v1-5的文件夹结构：
.
├── feature_extractor
│ └── preprocessor_config.json
├── model_index.json
├── safety_checker
│ ├── config.json
│ └── pytorch_model.bin
├── scheduler
│ └── scheduler_config.json
├── text_encoder
│ ├── config.json
│ └── pytorch_model.bin
├── tokenizer
│ ├── merges.txt
│ ├── special_tokens_map.json
│ ├── tokenizer_config.json
│ └── vocab.json
├── unet
│ ├── config.json
│ ├── diffusion_pytorch_model.bin
└── vae
├── config.json
├── diffusion_pytorch_model.bin可以查看组件的属性和配置：
pipeline.tokenizer
CLIPTokenizer(
name_or_path="/root/.cache/huggingface/hub/models--runwayml--stable-diffusion-v1-5/snapshots/39593d5650112b4cc580433f6b0435385882d819/tokenizer",
vocab_size=49408,
model_max_length=77,
is_fast=False,
padding_side="right",
truncation_side="right",
special_tokens={
"bos_token": AddedToken("<|startoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True),
"eos_token": AddedToken("<|endoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True),
"unk_token": AddedToken("<|endoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True),
"pad_token": "<|endoftext|>",
},
)