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from diffusers import DDPMPipeline
image_pipe = DDPMPipeline.from_pretrained("google/ddpm-celebahq-256")
image_pipe.to("cuda")
images = image_pipe().images
image_pipe
from diffusers import UNet2DModel
repo_id = "google/ddpm-church-256"
model = UNet2DModel.from_pretrained(repo_id)
model
model.config
model_random = UNet2DModel(**model.config)
model_random.save_pretrained("my_model")
model_random = UNet2DModel.from_pretrained("my_model")
import torch
torch.manual_seed(0)
noisy_sample = torch.randn(
1, model.config.in_channels, model.config.sample_size, model.config.sample_size
)
noisy_sample.shape
with torch.no_grad():
noisy_residual = model(sample=noisy_sample, timestep=2).sample
noisy_residual.shape
from diffusers import DDPMScheduler
scheduler = DDPMScheduler.from_config(repo_id)
scheduler.config
scheduler.save_config("my_scheduler")
new_scheduler = DDPMScheduler.from_config("my_scheduler")
less_noisy_sample = scheduler.step(
model_output=noisy_residual, timestep=2, sample=noisy_sample
).prev_sample
less_noisy_sample.shape
import PIL.Image
import numpy as np
def display_sample(sample, i):
image_processed = sample.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) * 127.5
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])
display(f"Image at step {i}")
display(image_pil)
model.to("cuda")
noisy_sample = noisy_sample.to("cuda")
import tqdm
sample = noisy_sample
for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):
# 1. predict noise residual
with torch.no_grad():
residual = model(sample, t).sample
# 2. compute less noisy image and set x_t -> x_t-1
sample = scheduler.step(residual, t, sample).prev_sample
# 3. optionally look at image
if (i + 1) % 50 == 0:
display_sample(sample, i + 1)
from diffusers import DDIMScheduler
scheduler = DDIMScheduler.from_config(repo_id)
scheduler.set_timesteps(num_inference_steps=50)
import tqdm
sample = noisy_sample
for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):
# 1. predict noise residual
with torch.no_grad():
residual = model(sample, t).sample
# 2. compute previous image and set x_t -> x_t-1
sample = scheduler.step(residual, t, sample).prev_sample
# 3. optionally look at image
if (i + 1) % 10 == 0:
display_sample(sample, i + 1) |