utils.py

import torch
import torchvision
from diffusers import StableDiffusionPipeline
from base64 import b64encode
from diffusers import AutoencoderKL, LMSDiscreteScheduler, UNet2DConditionModel
from transformers import CLIPTextModel, CLIPTokenizer, logging
from torchvision import transforms as tfms
from PIL import Image
from tqdm.auto import tqdm
from matplotlib import pyplot as plt



# Supress some unnecessary warnings when loading the CLIPTextModel
logging.set_verbosity_error()

# Set device
torch_device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
if "mps" == torch_device: os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = "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")

# 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")

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

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

# To the GPU we go!
vae = vae.to(torch_device)
text_encoder = text_encoder.to(torch_device)
unet = unet.to(torch_device);

def build_causal_attention_mask(bsz, seq_len, dtype):
    mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype)
    mask.fill_(torch.tensor(torch.finfo(dtype).min))  # fill with large negative number (acts like -inf)
    mask = mask.triu_(1)  # zero out the lower diagonal to enforce causality
    return mask.unsqueeze(1)  # add a batch dimension


# Prep Scheduler
def set_timesteps(scheduler, num_inference_steps):
    scheduler.set_timesteps(num_inference_steps)
    scheduler.timesteps = scheduler.timesteps.to(torch.float32) # minor fix to ensure MPS compatibility, fixed in diffusers PR 3925




def pil_to_latent(input_im):
    # Single image -> single latent in a batch (so size 1, 4, 64, 64)
    with torch.no_grad():
        latent = vae.encode(tfms.ToTensor()(input_im).unsqueeze(0).to(torch_device)*2-1) # Note scaling
    return 0.18215 * latent.latent_dist.sample()

def latents_to_pil(latents):
    # bath of latents -> list of images
    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]
    return pil_images
  
def get_output_embeds(input_embeddings):
    # CLIP's text model uses causal mask, so we prepare it here:
    bsz, seq_len = input_embeddings.shape[:2]
    causal_attention_mask = build_causal_attention_mask(bsz, seq_len, dtype=input_embeddings.dtype)

    # Getting the output embeddings involves calling the model with passing output_hidden_states=True
    # so that it doesn't just return the pooled final predictions:
    encoder_outputs = text_encoder.text_model.encoder(
        inputs_embeds=input_embeddings,
        attention_mask=None, # We aren't using an attention mask so that can be None
        causal_attention_mask=causal_attention_mask.to(torch_device),
        output_attentions=None,
        output_hidden_states=True, # We want the output embs not the final output
        return_dict=None,
    )

    # We're interested in the output hidden state only
    output = encoder_outputs[0]

    # There is a final layer norm we need to pass these through
    output = text_encoder.text_model.final_layer_norm(output)

    # And now they're ready!
    return output
  


#Generating an image with these modified embeddings

def generate_with_embs(text_embeddings, seed=45):
    height = 512                        # default height of Stable Diffusion
    width = 512                         # default width of Stable Diffusion
    num_inference_steps = 30            # Number of denoising steps
    guidance_scale = 7.5                # Scale for classifier-free guidance
    generator = torch.manual_seed(seed)   # Seed generator to create the inital latent noise
    batch_size = 1

    max_length = text_input.input_ids.shape[-1]
    uncond_input = tokenizer(
      [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
    )
    with torch.no_grad():
        uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0]
    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

    # Prep Scheduler
    set_timesteps(scheduler, num_inference_steps)

    # Prep latents
    latents = torch.randn(
    (batch_size, unet.in_channels, height // 8, width // 8),
    generator=generator,
    )
    latents = latents.to(torch_device)
    latents = latents * scheduler.init_noise_sigma

    # Loop
    for i, t in tqdm(enumerate(scheduler.timesteps), total=len(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)
        sigma = scheduler.sigmas[i]
        latent_model_input = scheduler.scale_model_input(latent_model_input, 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)

        # compute the previous noisy sample x_t -> x_t-1
        latents = scheduler.step(noise_pred, t, latents).prev_sample

    return latents_to_pil(latents)[0]
  


def blue_loss(images):
    # How far are the blue channel values to 0.9:
    error = torch.abs(images[:,2] - 0.9).mean() # [:,2] -> all images in batch, only the blue channel
    return error


def amber_loss(images):
  """Calculates the mean absolute error for amber color.

  Args:
    images: A tensor of shape (batch_size, channels, height, width).
    target_red: Target red value for amber.
    target_green: Target green value for amber.
    target_blue: Target blue value for amber.

  Returns:
    The mean absolute error.
     #target_red=0.8, target_green=0.6, target_blue=0.4
  """

  red_error = torch.abs(images[:, 0] - 0.12).mean()
  green_error = torch.abs(images[:, 1] - 0.2).mean()
  blue_error = torch.abs(images[:, 2] - 0.15).mean()

  # You can adjust weights for each channel if needed
  amber_error = (red_error + green_error + blue_error) / 3
  return amber_error


def generate_with_custom_loss(text_embeddings, text_input, generator, loss_fn):
    blue_loss_scale = 60

    height = 256  # default height of Stable Diffusion
    width = 256  # default width of Stable Diffusion
    num_inference_steps = 15  # Number of denoising steps
    guidance_scale = 7.5  # Scale for classifier-free guidance
    # generator = torch.manual_seed(32)  # Seed generator to create the inital latent noise
    batch_size = 1

    max_length = text_input.input_ids.shape[-1]
    uncond_input = tokenizer(
        [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
    )
    with torch.no_grad():
        uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0]
    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

    # Prep Scheduler
    set_timesteps(scheduler, num_inference_steps)

    # Prep latents
    latents = torch.randn(
        (batch_size, unet.in_channels, height // 8, width // 8),
        generator=generator,
    )
    latents = latents.to(torch_device)
    latents = latents * scheduler.init_noise_sigma

    # Loop
    for i, t in tqdm(enumerate(scheduler.timesteps), total=len(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)
        sigma = scheduler.sigmas[i]
        latent_model_input = scheduler.scale_model_input(latent_model_input, 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)

        #### ADDITIONAL GUIDANCE ###
        if i % 10 == 0:
            # Requires grad on the latents
            latents = latents.detach().requires_grad_()

            # Get the predicted x0:
            latents_x0 = latents - sigma * noise_pred
            # latents_x0 = scheduler.step(noise_pred, t, latents).pred_original_sample

            # Decode to image space
            denoised_images = vae.decode((1 / 0.18215) * latents_x0).sample / 2 + 0.5  # range (0, 1)

            # Calculate loss
            loss = loss_fn(denoised_images) * blue_loss_scale

            # Occasionally print it out
            if i % 10 == 0:
                print(i, 'loss:', loss.item())

            # Get gradient
            cond_grad = torch.autograd.grad(loss, latents)[0]

            # Modify the latents based on this gradient
            latents = latents.detach() - cond_grad * sigma ** 2

        # compute the previous noisy sample x_t -> x_t-1
        latents = scheduler.step(noise_pred, t, latents).prev_sample

    return latents_to_pil(latents)[0]
def gen_image_as_per_prompt(prompt, style, seed, custom_loss=None):
    # prompt = 'dog as Wolverine'

    generator = torch.manual_seed(seed)

    # Tokenize
    text_input = tokenizer(prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True,
                           return_tensors="pt")
    input_ids = text_input.input_ids.to(torch_device)

    # Access the embedding layer
    token_emb_layer = text_encoder.text_model.embeddings.token_embedding

    # Get token embeddings
    token_embeddings = token_emb_layer(input_ids)

    # The new embedding - special style
    if style:
        style_embed = torch.load(style)
        keys = list(style_embed.keys())
        replacement_token_embedding = style_embed[keys[0]].to(torch_device)

        # The new embedding. In this case just the input embedding of token 2368...mixing CAT
        # replacement_token_embedding = text_encoder.get_input_embeddings()(torch.tensor(2368, device=torch_device))

        # Insert this into the token embeddings (
        token_embeddings[0, torch.where(input_ids[0] == 6829)] = replacement_token_embedding.to(torch_device)

    # get pos embed
    pos_emb_layer = text_encoder.text_model.embeddings.position_embedding
    position_ids = text_encoder.text_model.embeddings.position_ids[:, :77]
    position_embeddings = pos_emb_layer(position_ids)

    # Combine with pos embs
    input_embeddings = token_embeddings + position_embeddings

    #  Feed through to get final output embs
    modified_output_embeddings = get_output_embeds(input_embeddings)

    if custom_loss is not None:
        image = generate_with_custom_loss(modified_output_embeddings, text_input, generator, custom_loss)
    else:
        image = generate_with_embs(modified_output_embeddings, text_input, generator)

    return image