sayakpaul/diffusers-qa-chatbot-artifacts · Datasets at Hugging Face

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repo_id = "./stable-diffusion-v1-5"
stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True) The from_pretrained() method won’t download any files from the Hub when it detects a local path, but this also means it won’t download and cache the latest changes to a checkpoint. Swap components in a pipeline You can customize the default components of any pipeline with another compatible component. Customization is important because: Changing the scheduler is important for exploring the trade-off between generation speed and quality. Different components of a model are typically trained independently and you can swap out a component with a better-performing one. During finetuning, usually only some components - like the UNet or text encoder - are trained. To find out which schedulers are compatible for customization, you can use the compatibles method: Copied from diffusers import DiffusionPipeline

repo_id = "runwayml/stable-diffusion-v1-5"
stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)
stable_diffusion.scheduler.compatibles Let’s use the SchedulerMixin.from_pretrained() method to replace the default PNDMScheduler with a more performant scheduler, EulerDiscreteScheduler. The subfolder="scheduler" argument is required to load the scheduler configuration from the correct subfolder of the pipeline repository. Then you can pass the new EulerDiscreteScheduler instance to the scheduler argument in DiffusionPipeline: Copied from diffusers import DiffusionPipeline, EulerDiscreteScheduler

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) Safety checker Diffusion models like Stable Diffusion can generate harmful content, which is why 🧨 Diffusers has a safety checker to check generated outputs against known hardcoded NSFW content. If you’d like to disable the safety checker for whatever reason, pass None to the safety_checker argument: Copied from diffusers import DiffusionPipeline

repo_id = "runwayml/stable-diffusion-v1-5"
stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, safety_checker=None, use_safetensors=True)
"""
You have disabled the safety checker for <class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline'> by passing `safety_checker=None`. Ensure that you abide by the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend keeping the safety filter enabled in all public-facing circumstances, disabling it only for use cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .
""" Reuse components across pipelines You can also reuse the same components in multiple pipelines to avoid loading the weights into RAM twice. Use the components method to save the components: Copied 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 Then you can pass the components to another pipeline without reloading the weights into RAM: Copied stable_diffusion_img2img = StableDiffusionImg2ImgPipeline(**components) You can also pass the components individually to the pipeline if you want more flexibility over which components to reuse or disable. For example, to reuse the same components in the text-to-image pipeline, except for the safety checker and feature extractor, in the image-to-image pipeline: Copied 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,
) Checkpoint variants A checkpoint variant is usually a checkpoint whose weights are: Stored in a different floating point type for lower precision and lower storage, such as torch.float16, because it only requires half the bandwidth and storage to download. You can’t use this variant if you’re continuing training or using a CPU. Non-exponential mean averaged (EMA) weights, which shouldn’t be used for inference. You should use these to continue fine-tuning a model. 💡 When the checkpoints have identical model structures, but they were trained on different datasets and with a different training setup, they should be stored in separate repositories instead of variations (for example, stable-diffusion-v1-4 and stable-diffusion-v1-5). Otherwise, a variant is identical to the original checkpoint. They have exactly the same serialization format (like Safetensors), model structure, and weights that have identical tensor shapes. checkpoint type weight name argument for loading weights original diffusion_pytorch_model.bin floating point diffusion_pytorch_model.fp16.bin variant, torch_dtype non-EMA diffusion_pytorch_model.non_ema.bin variant There are two important arguments to know for loading variants: torch_dtype defines the floating point precision of the loaded checkpoints. For example, if you want to save bandwidth by loading a fp16 variant, you should specify torch_dtype=torch.float16 to convert the weights to fp16. Otherwise, the fp16 weights are converted to the default fp32 precision. You can also load the original checkpoint without defining the variant argument, and convert it to fp16 with torch_dtype=torch.float16. In this case, the default fp32 weights are downloaded first, and then they’re converted to fp16 after loading. variant defines which files should be loaded from the repository. For example, if you want to load a non_ema variant from the diffusers/stable-diffusion-variants repository, you should specify variant="non_ema" to download the non_ema files. Copied from diffusers import DiffusionPipeline
import torch

# load fp16 variant
stable_diffusion = DiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16, use_safetensors=True
)
# load non_ema variant
stable_diffusion = DiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", variant="non_ema", use_safetensors=True
) To save a checkpoint stored in a different floating-point type or as a non-EMA variant, use the DiffusionPipeline.save_pretrained() method and specify the variant argument. You should try and save a variant to the same folder as the original checkpoint, so you can load both from the same folder: Copied from diffusers import DiffusionPipeline

# save as fp16 variant
stable_diffusion.save_pretrained("runwayml/stable-diffusion-v1-5", variant="fp16")
# save as non-ema variant
stable_diffusion.save_pretrained("runwayml/stable-diffusion-v1-5", variant="non_ema") If you don’t save the variant to an existing folder, you must specify the variant argument otherwise it’ll throw an Exception because it can’t find the original checkpoint: Copied # 👎 this won't work
stable_diffusion = DiffusionPipeline.from_pretrained(
"./stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
)
# 👍 this works
stable_diffusion = DiffusionPipeline.from_pretrained(
"./stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16, use_safetensors=True
) Models Models are loaded from the ModelMixin.from_pretrained() method, which downloads and caches the latest version of the model weights and configurations. If the latest files are available in the local cache, from_pretrained() reuses files in the cache instead of re-downloading them. Models can be loaded from a subfolder with the subfolder argument. For example, the model weights for runwayml/stable-diffusion-v1-5 are stored in the unet subfolder: Copied from diffusers import UNet2DConditionModel

repo_id = "runwayml/stable-diffusion-v1-5"
model = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet", use_safetensors=True) Or directly from a repository’s directory: Copied from diffusers import UNet2DModel

repo_id = "google/ddpm-cifar10-32"
model = UNet2DModel.from_pretrained(repo_id, use_safetensors=True) You can also load and save model variants by specifying the variant argument in ModelMixin.from_pretrained() and ModelMixin.save_pretrained(): Copied from diffusers import UNet2DConditionModel

model = UNet2DConditionModel.from_pretrained(
"runwayml/stable-diffusion-v1-5", subfolder="unet", variant="non_ema", use_safetensors=True
)
model.save_pretrained("./local-unet", variant="non_ema") Schedulers Schedulers are loaded from the SchedulerMixin.from_pretrained() method, and unlike models, schedulers are not parameterized or trained; they are defined by a configuration file. Loading schedulers does not consume any significant amount of memory and the same configuration file can be used for a variety of different schedulers.
For example, the following schedulers are compatible with StableDiffusionPipeline, which means you can load the same scheduler configuration file in any of these classes: Copied from diffusers import StableDiffusionPipeline
from diffusers import (
DDPMScheduler,
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
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) DiffusionPipeline explained As a class method, DiffusionPipeline.from_pretrained() is responsible for two things: Download the latest version of the folder structure required for inference and cache it. If the latest folder structure is available in the local cache, DiffusionPipeline.from_pretrained() reuses the cache and won’t redownload the files. Load the cached weights into the correct pipeline class - retrieved from the model_index.json file - and return an instance of it. The pipelines’ underlying folder structure corresponds directly with their class instances. For example, the StableDiffusionPipeline corresponds to the folder structure in runwayml/stable-diffusion-v1-5. Copied from diffusers import DiffusionPipeline

repo_id = "runwayml/stable-diffusion-v1-5"
pipeline = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)
print(pipeline) You’ll see pipeline is an instance of StableDiffusionPipeline, which consists of seven components: "feature_extractor": a CLIPImageProcessor from 🤗 Transformers. "safety_checker": a component for screening against harmful content. "scheduler": an instance of PNDMScheduler. "text_encoder": a CLIPTextModel from 🤗 Transformers. "tokenizer": a CLIPTokenizer from 🤗 Transformers. "unet": an instance of UNet2DConditionModel. "vae": an instance of AutoencoderKL. Copied StableDiffusionPipeline {
"feature_extractor": [
"transformers",
"CLIPImageProcessor"
],
"safety_checker": [
"stable_diffusion",

repo_id = "./stable-diffusion-v1-5"

stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True) The from_pretrained() method won’t download any files from the Hub when it detects a local path, but this also means it won’t download and cache the latest changes to a checkpoint. Swap components in a pipeline You can customize the default components of any pipeline with another compatible component. Customization is important because: Changing the scheduler is important for exploring the trade-off between generation speed and quality. Different components of a model are typically trained independently and you can swap out a component with a better-performing one. During finetuning, usually only some components - like the UNet or text encoder - are trained. To find out which schedulers are compatible for customization, you can use the compatibles method: Copied from diffusers import DiffusionPipeline

repo_id = "runwayml/stable-diffusion-v1-5"

stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)

stable_diffusion.scheduler.compatibles Let’s use the SchedulerMixin.from_pretrained() method to replace the default PNDMScheduler with a more performant scheduler, EulerDiscreteScheduler. The subfolder="scheduler" argument is required to load the scheduler configuration from the correct subfolder of the pipeline repository. Then you can pass the new EulerDiscreteScheduler instance to the scheduler argument in DiffusionPipeline: Copied from diffusers import DiffusionPipeline, EulerDiscreteScheduler

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) Safety checker Diffusion models like Stable Diffusion can generate harmful content, which is why 🧨 Diffusers has a safety checker to check generated outputs against known hardcoded NSFW content. If you’d like to disable the safety checker for whatever reason, pass None to the safety_checker argument: Copied from diffusers import DiffusionPipeline

repo_id = "runwayml/stable-diffusion-v1-5"

stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, safety_checker=None, use_safetensors=True)

"""

You have disabled the safety checker for <class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline'> by passing `safety_checker=None`. Ensure that you abide by the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend keeping the safety filter enabled in all public-facing circumstances, disabling it only for use cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .

""" Reuse components across pipelines You can also reuse the same components in multiple pipelines to avoid loading the weights into RAM twice. Use the components method to save the components: Copied 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 Then you can pass the components to another pipeline without reloading the weights into RAM: Copied stable_diffusion_img2img = StableDiffusionImg2ImgPipeline(**components) You can also pass the components individually to the pipeline if you want more flexibility over which components to reuse or disable. For example, to reuse the same components in the text-to-image pipeline, except for the safety checker and feature extractor, in the image-to-image pipeline: Copied 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,

) Checkpoint variants A checkpoint variant is usually a checkpoint whose weights are: Stored in a different floating point type for lower precision and lower storage, such as torch.float16, because it only requires half the bandwidth and storage to download. You can’t use this variant if you’re continuing training or using a CPU. Non-exponential mean averaged (EMA) weights, which shouldn’t be used for inference. You should use these to continue fine-tuning a model. 💡 When the checkpoints have identical model structures, but they were trained on different datasets and with a different training setup, they should be stored in separate repositories instead of variations (for example, stable-diffusion-v1-4 and stable-diffusion-v1-5). Otherwise, a variant is identical to the original checkpoint. They have exactly the same serialization format (like Safetensors), model structure, and weights that have identical tensor shapes. checkpoint type weight name argument for loading weights original diffusion_pytorch_model.bin floating point diffusion_pytorch_model.fp16.bin variant, torch_dtype non-EMA diffusion_pytorch_model.non_ema.bin variant There are two important arguments to know for loading variants: torch_dtype defines the floating point precision of the loaded checkpoints. For example, if you want to save bandwidth by loading a fp16 variant, you should specify torch_dtype=torch.float16 to convert the weights to fp16. Otherwise, the fp16 weights are converted to the default fp32 precision. You can also load the original checkpoint without defining the variant argument, and convert it to fp16 with torch_dtype=torch.float16. In this case, the default fp32 weights are downloaded first, and then they’re converted to fp16 after loading. variant defines which files should be loaded from the repository. For example, if you want to load a non_ema variant from the diffusers/stable-diffusion-variants repository, you should specify variant="non_ema" to download the non_ema files. Copied from diffusers import DiffusionPipeline

import torch

# load fp16 variant

stable_diffusion = DiffusionPipeline.from_pretrained(

"runwayml/stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16, use_safetensors=True

)

# load non_ema variant

stable_diffusion = DiffusionPipeline.from_pretrained(

"runwayml/stable-diffusion-v1-5", variant="non_ema", use_safetensors=True

) To save a checkpoint stored in a different floating-point type or as a non-EMA variant, use the DiffusionPipeline.save_pretrained() method and specify the variant argument. You should try and save a variant to the same folder as the original checkpoint, so you can load both from the same folder: Copied from diffusers import DiffusionPipeline

# save as fp16 variant

stable_diffusion.save_pretrained("runwayml/stable-diffusion-v1-5", variant="fp16")

# save as non-ema variant

stable_diffusion.save_pretrained("runwayml/stable-diffusion-v1-5", variant="non_ema") If you don’t save the variant to an existing folder, you must specify the variant argument otherwise it’ll throw an Exception because it can’t find the original checkpoint: Copied # 👎 this won't work

stable_diffusion = DiffusionPipeline.from_pretrained(

"./stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True

)

# 👍 this works

stable_diffusion = DiffusionPipeline.from_pretrained(

"./stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16, use_safetensors=True

) Models Models are loaded from the ModelMixin.from_pretrained() method, which downloads and caches the latest version of the model weights and configurations. If the latest files are available in the local cache, from_pretrained() reuses files in the cache instead of re-downloading them. Models can be loaded from a subfolder with the subfolder argument. For example, the model weights for runwayml/stable-diffusion-v1-5 are stored in the unet subfolder: Copied from diffusers import UNet2DConditionModel

repo_id = "runwayml/stable-diffusion-v1-5"

model = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet", use_safetensors=True) Or directly from a repository’s directory: Copied from diffusers import UNet2DModel

repo_id = "google/ddpm-cifar10-32"

model = UNet2DModel.from_pretrained(repo_id, use_safetensors=True) You can also load and save model variants by specifying the variant argument in ModelMixin.from_pretrained() and ModelMixin.save_pretrained(): Copied from diffusers import UNet2DConditionModel

model = UNet2DConditionModel.from_pretrained(

"runwayml/stable-diffusion-v1-5", subfolder="unet", variant="non_ema", use_safetensors=True

)

model.save_pretrained("./local-unet", variant="non_ema") Schedulers Schedulers are loaded from the SchedulerMixin.from_pretrained() method, and unlike models, schedulers are not parameterized or trained; they are defined by a configuration file. Loading schedulers does not consume any significant amount of memory and the same configuration file can be used for a variety of different schedulers.

For example, the following schedulers are compatible with StableDiffusionPipeline, which means you can load the same scheduler configuration file in any of these classes: Copied from diffusers import StableDiffusionPipeline

from diffusers import (

DDPMScheduler,

DDIMScheduler,

PNDMScheduler,

LMSDiscreteScheduler,

EulerAncestralDiscreteScheduler,

EulerDiscreteScheduler,

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) DiffusionPipeline explained As a class method, DiffusionPipeline.from_pretrained() is responsible for two things: Download the latest version of the folder structure required for inference and cache it. If the latest folder structure is available in the local cache, DiffusionPipeline.from_pretrained() reuses the cache and won’t redownload the files. Load the cached weights into the correct pipeline class - retrieved from the model_index.json file - and return an instance of it. The pipelines’ underlying folder structure corresponds directly with their class instances. For example, the StableDiffusionPipeline corresponds to the folder structure in runwayml/stable-diffusion-v1-5. Copied from diffusers import DiffusionPipeline

repo_id = "runwayml/stable-diffusion-v1-5"

pipeline = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)

print(pipeline) You’ll see pipeline is an instance of StableDiffusionPipeline, which consists of seven components: "feature_extractor": a CLIPImageProcessor from 🤗 Transformers. "safety_checker": a component for screening against harmful content. "scheduler": an instance of PNDMScheduler. "text_encoder": a CLIPTextModel from 🤗 Transformers. "tokenizer": a CLIPTokenizer from 🤗 Transformers. "unet": an instance of UNet2DConditionModel. "vae": an instance of AutoencoderKL. Copied StableDiffusionPipeline {

"feature_extractor": [

"transformers",

"CLIPImageProcessor"

],

"safety_checker": [

"stable_diffusion",