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

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pipeline = AutoPipelineForText2Image.from_pretrained(
"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
).to("cuda")
image = pipeline(
prompt_embeds=prompt_embeds, # generated from Compel
negative_prompt_embeds=negative_prompt_embeds, # generated from Compel
).images[0] ControlNet As you saw in the ControlNet section, these models offer a more flexible and accurate way to generate images by incorporating an additional conditioning image input. Each ControlNet model is pretrained on a particular type of conditioning image to generate new images that resemble it. For example, if you take a ControlNet model pretrained on depth maps, you can give the model a depth map as a conditioning input and it’ll generate an image that preserves the spatial information in it. This is quicker and easier than specifying the depth information in a prompt. You can even combine multiple conditioning inputs with a MultiControlNet! There are many types of conditioning inputs you can use, and 🤗 Diffusers supports ControlNet for Stable Diffusion and SDXL models. Take a look at the more comprehensive ControlNet guide to learn how you can use these models. Optimize Diffusion models are large, and the iterative nature of denoising an image is computationally expensive and intensive. But this doesn’t mean you need access to powerful - or even many - GPUs to use them. There are many optimization techniques for running diffusion models on consumer and free-tier resources. For example, you can load model weights in half-precision to save GPU memory and increase speed or offload the entire model to the GPU to save even more memory. PyTorch 2.0 also supports a more memory-efficient attention mechanism called scaled dot product attention that is automatically enabled if you’re using PyTorch 2.0. You can combine this with torch.compile to speed your code up even more: Copied from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16").to("cuda")
pipeline.unet = torch.compile(pipeline.unet, mode="reduce-overhead", fullgraph=True) For more tips on how to optimize your code to save memory and speed up inference, read the Memory and speed and Torch 2.0 guides.
Train a diffusion model

Unconditional image generation is a popular application of diffusion models that generates images that look like those in the dataset used for training. Typically, the best results are obtained from finetuning a pretrained model on a specific dataset. You can find many of these checkpoints on the Hub, but if you can’t find one you like, you can always train your own!
This tutorial will teach you how to train a UNet2DModel from scratch on a subset of the Smithsonian Butterflies dataset to generate your own 🦋 butterflies 🦋.
💡 This training tutorial is based on the Training with 🧨 Diffusers notebook. For additional details and context about diffusion models like how they work, check out the notebook!
Before you begin, make sure you have 🤗 Datasets installed to load and preprocess image datasets, and 🤗 Accelerate, to simplify training on any number of GPUs. The following command will also install TensorBoard to visualize training metrics (you can also use Weights & Biases to track your training).


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!pip install diffusers[training]
We encourage you to share your model with the community, and in order to do that, you’ll need to login to your Hugging Face account (create one here if you don’t already have one!). You can login from a notebook and enter your token when prompted:


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>>> from huggingface_hub import notebook_login

>>> notebook_login()
Or login in from the terminal:


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huggingface-cli login
Since the model checkpoints are quite large, install Git-LFS to version these large files:


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!sudo apt -qq install git-lfs
!git config --global credential.helper store

Training configuration

For convenience, create a TrainingConfig class containing the training hyperparameters (feel free to adjust them):


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>>> from dataclasses import dataclass


>>> @dataclass
... class TrainingConfig:
... image_size = 128 # the generated image resolution
... train_batch_size = 16
... eval_batch_size = 16 # how many images to sample during evaluation
... num_epochs = 50
... gradient_accumulation_steps = 1
... learning_rate = 1e-4
... lr_warmup_steps = 500
... save_image_epochs = 10
... save_model_epochs = 30
... mixed_precision = "fp16" # `no` for float32, `fp16` for automatic mixed precision
... output_dir = "ddpm-butterflies-128" # the model name locally and on the HF Hub

... push_to_hub = True # whether to upload the saved model to the HF Hub
... hub_private_repo = False
... overwrite_output_dir = True # overwrite the old model when re-running the notebook
... seed = 0


>>> config = TrainingConfig()

Load the dataset

You can easily load the Smithsonian Butterflies dataset with the 🤗 Datasets library:


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>>> from datasets import load_dataset

>>> config.dataset_name = "huggan/smithsonian_butterflies_subset"
>>> dataset = load_dataset(config.dataset_name, split="train")
💡 You can find additional datasets from the HugGan Community Event or you can use your own dataset by creating a local ImageFolder. Set config.dataset_name to the repository id of the dataset if it is from the HugGan Community Event, or imagefolder if you’re using your own images.
🤗 Datasets uses the Image feature to automatically decode the image data and load it as a PIL.Image which we can visualize:


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>>> import matplotlib.pyplot as plt

>>> fig, axs = plt.subplots(1, 4, figsize=(16, 4))
>>> for i, image in enumerate(dataset[:4]["image"]):
... axs[i].imshow(image)
... axs[i].set_axis_off()
>>> fig.show()

The images are all different sizes though, so you’ll need to preprocess them first:
Resize changes the image size to the one defined in config.image_size.
RandomHorizontalFlip augments the dataset by randomly mirroring the images.
Normalize is important to rescale the pixel values into a [-1, 1] range, which is what the model expects.

pipeline = AutoPipelineForText2Image.from_pretrained(

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

).to("cuda")

image = pipeline(

prompt_embeds=prompt_embeds, # generated from Compel

negative_prompt_embeds=negative_prompt_embeds, # generated from Compel

).images[0] ControlNet As you saw in the ControlNet section, these models offer a more flexible and accurate way to generate images by incorporating an additional conditioning image input. Each ControlNet model is pretrained on a particular type of conditioning image to generate new images that resemble it. For example, if you take a ControlNet model pretrained on depth maps, you can give the model a depth map as a conditioning input and it’ll generate an image that preserves the spatial information in it. This is quicker and easier than specifying the depth information in a prompt. You can even combine multiple conditioning inputs with a MultiControlNet! There are many types of conditioning inputs you can use, and 🤗 Diffusers supports ControlNet for Stable Diffusion and SDXL models. Take a look at the more comprehensive ControlNet guide to learn how you can use these models. Optimize Diffusion models are large, and the iterative nature of denoising an image is computationally expensive and intensive. But this doesn’t mean you need access to powerful - or even many - GPUs to use them. There are many optimization techniques for running diffusion models on consumer and free-tier resources. For example, you can load model weights in half-precision to save GPU memory and increase speed or offload the entire model to the GPU to save even more memory. PyTorch 2.0 also supports a more memory-efficient attention mechanism called scaled dot product attention that is automatically enabled if you’re using PyTorch 2.0. You can combine this with torch.compile to speed your code up even more: Copied from diffusers import AutoPipelineForText2Image

import torch

pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16").to("cuda")

pipeline.unet = torch.compile(pipeline.unet, mode="reduce-overhead", fullgraph=True) For more tips on how to optimize your code to save memory and speed up inference, read the Memory and speed and Torch 2.0 guides.

Train a diffusion model

Unconditional image generation is a popular application of diffusion models that generates images that look like those in the dataset used for training. Typically, the best results are obtained from finetuning a pretrained model on a specific dataset. You can find many of these checkpoints on the Hub, but if you can’t find one you like, you can always train your own!

This tutorial will teach you how to train a UNet2DModel from scratch on a subset of the Smithsonian Butterflies dataset to generate your own 🦋 butterflies 🦋.

💡 This training tutorial is based on the Training with 🧨 Diffusers notebook. For additional details and context about diffusion models like how they work, check out the notebook!

Before you begin, make sure you have 🤗 Datasets installed to load and preprocess image datasets, and 🤗 Accelerate, to simplify training on any number of GPUs. The following command will also install TensorBoard to visualize training metrics (you can also use Weights & Biases to track your training).

Copied

!pip install diffusers[training]

We encourage you to share your model with the community, and in order to do that, you’ll need to login to your Hugging Face account (create one here if you don’t already have one!). You can login from a notebook and enter your token when prompted:

Copied

>>> from huggingface_hub import notebook_login

>>> notebook_login()

Or login in from the terminal:

Copied

huggingface-cli login

Since the model checkpoints are quite large, install Git-LFS to version these large files:

Copied

!sudo apt -qq install git-lfs

!git config --global credential.helper store

Training configuration

For convenience, create a TrainingConfig class containing the training hyperparameters (feel free to adjust them):

Copied

>>> from dataclasses import dataclass

>>> @dataclass

... class TrainingConfig:

... image_size = 128 # the generated image resolution

... train_batch_size = 16

... eval_batch_size = 16 # how many images to sample during evaluation

... num_epochs = 50

... gradient_accumulation_steps = 1

... learning_rate = 1e-4

... lr_warmup_steps = 500

... save_image_epochs = 10

... save_model_epochs = 30

... mixed_precision = "fp16" # `no` for float32, `fp16` for automatic mixed precision

... output_dir = "ddpm-butterflies-128" # the model name locally and on the HF Hub

... push_to_hub = True # whether to upload the saved model to the HF Hub

... hub_private_repo = False

... overwrite_output_dir = True # overwrite the old model when re-running the notebook

... seed = 0

>>> config = TrainingConfig()

Load the dataset

You can easily load the Smithsonian Butterflies dataset with the 🤗 Datasets library:

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>>> from datasets import load_dataset

>>> config.dataset_name = "huggan/smithsonian_butterflies_subset"

>>> dataset = load_dataset(config.dataset_name, split="train")

💡 You can find additional datasets from the HugGan Community Event or you can use your own dataset by creating a local ImageFolder. Set config.dataset_name to the repository id of the dataset if it is from the HugGan Community Event, or imagefolder if you’re using your own images.

🤗 Datasets uses the Image feature to automatically decode the image data and load it as a PIL.Image which we can visualize:

Copied

>>> import matplotlib.pyplot as plt

>>> fig, axs = plt.subplots(1, 4, figsize=(16, 4))

>>> for i, image in enumerate(dataset[:4]["image"]):

... axs[i].imshow(image)

... axs[i].set_axis_off()

>>> fig.show()

The images are all different sizes though, so you’ll need to preprocess them first:

Resize changes the image size to the one defined in config.image_size.

RandomHorizontalFlip augments the dataset by randomly mirroring the images.

Normalize is important to rescale the pixel values into a [-1, 1] range, which is what the model expects.