check.py

from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler
import torch
from diffusers import StableDiffusionPipeline
from PIL import Image

def image_grid(imgs, rows, cols):
    assert len(imgs) == rows*cols

    w, h = imgs[0].size
    grid = Image.new('RGB', size=(cols*w, rows*h))
    grid_w, grid_h = grid.size
    
    for i, img in enumerate(imgs):
        grid.paste(img, box=(i%cols*w, i//cols*h))
    return grid

# 1. Load the autoencoder model which will be used to decode the latents into image space. 
vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae")

# 2. Load the tokenizer and text encoder to tokenize and encode the text. 
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")

text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")

# 3. The UNet model for generating the latents.
unet = UNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="unet")

from diffusers import LMSDiscreteScheduler

scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)

torch_device = "cuda"
vae.to(torch_device)
text_encoder.to(torch_device)
unet.to(torch_device)

prompt = ["a photograph of an astronaut riding a horse"]

height = 512                        # default height of Stable Diffusion
width = 512                         # default width of Stable Diffusion

num_inference_steps = 100           # Number of denoising steps

guidance_scale = 7.5                # Scale for classifier-free guidance

generator = torch.manual_seed(0)    # Seed generator to create the inital latent noise

batch_size = len(prompt)
text_input = tokenizer(prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")

text_embeddings = text_encoder(text_input.input_ids.to(torch_device))[0]
max_length = text_input.input_ids.shape[-1]
uncond_input = tokenizer(
    [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0]
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
latents = torch.randn(
    (batch_size, unet.in_channels, height // 8, width // 8),
    generator=generator,
)
latents = latents.to(torch_device)
scheduler.set_timesteps(num_inference_steps)
latents = latents * scheduler.init_noise_sigma
from tqdm.auto import tqdm

scheduler.set_timesteps(num_inference_steps)

for t in tqdm(scheduler.timesteps):
    # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
    latent_model_input = torch.cat([latents] * 2)

    latent_model_input = scheduler.scale_model_input(latent_model_input, timestep=t)

    # predict the noise residual
    with torch.no_grad():
        noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample

    # perform guidance
    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
    print('noise_pred',noise_pred.shape)
    # compute the previous noisy sample x_t -> x_t-1
    latents = scheduler.step(noise_pred, t, latents).prev_sample
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
with torch.no_grad():
    image = vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
images = (image * 255).round().astype("uint8")
pil_images = [Image.fromarray(image) for image in images]
pil_images[0]

pil_images[0].save(f'image_astronaut.png')