metadata
license: afl-3.0
language:
- en
library_name: diffusers
tags:
- Class conditioned Diffusion
- CIFAR10 Diffusion
Here is Custom Pipeline for Class conditioned diffusion model. For training script, pipeline, tutorial nb and sampling please check my Github Repo:- https://github.com/KetanMann/Class_Conditioned_Diffusion_Training_Script Here is Class Conditional Diffusion Pipeline and Sampling.
Firstly install Diffusers
!pip install git+https://github.com/huggingface/diffusers
Then login to your huggingface account.
from huggingface_hub import notebook_login
notebook_login()
Finally for sampling and model testing. Run these lines of code.
from diffusers import UNet2DModel, DDPMScheduler
from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from huggingface_hub import hf_hub_download
import torch
import os
from PIL import Image
import matplotlib.pyplot as plt
from typing import List, Optional, Tuple, Union
class DDPMPipelinenew(DiffusionPipeline):
def __init__(self, unet, scheduler, num_classes: int):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
self.num_classes = num_classes
self._device = unet.device # Ensure the pipeline knows the device
@torch.no_grad()
def __call__(
self,
batch_size: int = 64,
class_labels: Optional[torch.Tensor] = None,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
num_inference_steps: int = 1000,
output_type: Optional[str] = "pil",
return_dict: bool = True,
) -> Union[ImagePipelineOutput, Tuple]:
# Ensure class_labels is on the same device as the model
class_labels = class_labels.to(self._device)
if class_labels.ndim == 0:
class_labels = class_labels.unsqueeze(0).expand(batch_size)
else:
class_labels = class_labels.expand(batch_size)
# Sample gaussian noise to begin loop
if isinstance(self.unet.config.sample_size, int):
image_shape = (
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
image = randn_tensor(image_shape, generator=generator, device=self._device)
# Set step values
self.scheduler.set_timesteps(num_inference_steps)
for t in self.progress_bar(self.scheduler.timesteps):
# Ensure the class labels are correctly broadcast to match the input tensor shape
model_output = self.unet(image, t, class_labels).sample
image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
def to(self, device: torch.device):
self._device = device
self.unet.to(device)
return self
def load_pipeline(repo_id, num_classes, device):
unet = UNet2DModel.from_pretrained(repo_id, subfolder="unet").to(device)
scheduler = DDPMScheduler.from_pretrained(repo_id, subfolder="scheduler")
pipeline = DDPMPipelinenew(unet=unet, scheduler=scheduler, num_classes=num_classes)
return pipeline.to(device) # Move the entire pipeline to the device
def save_images_locally(images, save_dir, epoch, class_label):
os.makedirs(save_dir, exist_ok=True)
for i, image in enumerate(images):
image_path = os.path.join(save_dir, f"image_epoch{epoch}_class{class_label}_idx{i}.png")
image.save(image_path)
def generate_images(pipeline, class_label, batch_size, num_inference_steps, save_dir, epoch):
generator = torch.Generator(device=pipeline._device).manual_seed(0)
class_labels = torch.tensor([class_label] * batch_size).to(pipeline._device)
images = pipeline(
generator=generator,
batch_size=batch_size,
num_inference_steps=num_inference_steps,
class_labels=class_labels,
output_type="pil",
).images
save_images_locally(images, save_dir, epoch, class_label)
return images
def create_image_grid(images, grid_size, save_path):
assert len(images) == grid_size ** 2, "Number of images must be equal to grid_size squared"
width, height = images[0].size
grid_img = Image.new('RGB', (grid_size * width, grid_size * height))
for i, image in enumerate(images):
x = i % grid_size * width
y = i // grid_size * height
grid_img.paste(image, (x, y))
grid_img.save(save_path)
return grid_img
if __name__ == "__main__":
repo_id = "Ketansomewhere/cifar10_conditional_diffusion1"
num_classes = 10 # Adjust to your number of classes
batch_size = 64
num_inference_steps = 1000 # Can be as low as 50 for faster generation
save_dir = "generated_images"
epoch = 0
grid_size = 8 # 8x8 grid
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipeline = load_pipeline(repo_id, num_classes, device)
for class_label in range(num_classes):
images = generate_images(pipeline, class_label, batch_size, num_inference_steps, save_dir, epoch)
# Create and save the grid image
grid_img_path = os.path.join(save_dir, f"grid_image_class{class_label}.png")
grid_img = create_image_grid(images, grid_size, grid_img_path)
# Plot the grid image
plt.figure(figsize=(10, 10))
plt.imshow(grid_img)
plt.axis('off')
plt.title(f'Class {class_label}')
plt.savefig(os.path.join(save_dir, f"grid_image_class{class_label}.png"))
plt.show()
Also, check this nb for the above implementation testing.ipynb .