style_guidence.py

import torch
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, UNet2DConditionModel, LMSDiscreteScheduler
from tqdm.auto import tqdm
from torch import autocast
from PIL import Image
from matplotlib import pyplot as plt
import numpy
from torchvision import transforms as tfms
import shutil
# For video display:
import cv2
from IPython.display import HTML
from base64 import b64encode
import os 
from utils import color_loss,pil_to_latent,sketch_loss
# Set device
torch_device =  "cpu"

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

scheduler.set_timesteps(15)

token_emb_layer = text_encoder.text_model.embeddings.token_embedding
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)
     
     

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 = text_encoder.text_model._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

def set_timesteps(scheduler, num_inference_steps):
    scheduler.set_timesteps(num_inference_steps)
    scheduler.timesteps = scheduler.timesteps.to(torch.float32)

# 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 latents_to_pil(latents):
      # bath of latents -> list of images
  latents = (1 / 0.18215) * latents
  with torch.no_grad():
    image = vae.decode(latents)
  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 generate_with_embs(text_embeddings,text_input, seed,num_inference_steps):
    
    height = 512                        # default height of Stable Diffusion
    width = 512                         # default width of Stable Diffusion
    num_inference_steps = num_inference_steps        # 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
    latents = latents * scheduler.sigmas[0] # Need to scale to match k

    # 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)
        latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
        # 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
        latents = scheduler.step(noise_pred, i, latents)["prev_sample"]
    return latents_to_pil(latents)[0]

def generate_with_prompt_style(prompt, style, num_of_inf_steps=50,seed = 42):

    prompt = prompt + ' in style of s'
    embed = torch.load(style)
    print("Keys",embed.keys())
    text_input = tokenizer(prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")
    # for t in text_input['input_ids'][0][:20]: # We'll just look at the first 7 to save you from a wall of '<|endoftext|>'
    #     print(t, tokenizer.decoder.get(int(t)))
    input_ids = text_input.input_ids.to(torch_device)

    token_embeddings = token_emb_layer(input_ids)
    # The new embedding - our special birb word
    replacement_token_embedding = embed[list(embed.keys())[0]].to(torch_device)

    # Insert this into the token embeddings
    token_embeddings[0, torch.where(input_ids[0]==338)] = replacement_token_embedding.to(torch_device)

    # 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)

    # And generate an image with this:
    return generate_with_embs(modified_output_embeddings, text_input, seed,num_of_inf_steps)


# prompt = 'A man sipping wine wearing a spacesuit on the moon'
# image = generate_with_prompt_style(prompt, '/home/deepanshudashora/Documents/Stable_Diffusion/caitlin_fairchild.bin')

# image.save("output.png")