app.py

import gradio as gr
import os
import sys
from base64 import b64encode

import numpy as np
import torch
from diffusers import AutoencoderKL, LMSDiscreteScheduler, UNet2DConditionModel

from matplotlib import pyplot as plt
from pathlib import Path
from PIL import Image
from torch import autocast
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer, logging
import os
import cv2
import torchvision.transforms as T

torch.manual_seed(1)
logging.set_verbosity_error()
torch_device = "cuda" if torch.cuda.is_available() else "cpu"


# Load the autoencoder
vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder='vae')

# Load 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")

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

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

# Move everything to GPU
vae = vae.to(torch_device)
text_encoder = text_encoder.to(torch_device)
unet = unet.to(torch_device)

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

# 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


style_files = ['learned_embeds_animal_toys.bin','learned_embeds_fftstyle.bin',
          'learned_embeds_midjourney_style.bin','learned_embeds_oil_style.bin','learned_embeds_space-style.bin']

seed_values = [8,16,50,80,128]
height = 512                        # default height of Stable Diffusion
width = 512                         # default width of Stable Diffusion
num_inference_steps = 5            # Number of denoising steps
guidance_scale = 7.5                # Scale for classifier-free guidance
num_styles = len(style_files)

def get_style_embeddings(style_file):
    style_embed = torch.load(style_file)
    style_name = list(style_embed.keys())[0]
    return style_embed[style_name]

def get_EOS_pos_in_prompt(prompt):
    return len(prompt.split())+1


import torch.nn.functional as F


from torchvision.transforms import ToTensor
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 additional_guidance(latents, scheduler, noise_pred, t, sigma, custom_loss_fn, custom_loss_scale):
    #### ADDITIONAL GUIDANCE ###
    # Requires grad on the latents
    latents = latents.detach().requires_grad_()

    # Get the predicted x0:
    latents_x0 = latents - sigma * noise_pred

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

    # Calculate loss
    loss = custom_loss_fn(denoised_images) * custom_loss_scale

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

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


def generate_with_embs(text_embeddings, max_length, random_seed, loss_fn = None, custom_loss_scale=1.0):

    height = 512                        # default height of Stable Diffusion
    width = 512                         # default width of Stable Diffusion
    num_inference_steps = 5            # Number of denoising steps
    guidance_scale = 7.5                # Scale for classifier-free guidance

    generator = torch.manual_seed(random_seed)   # Seed generator to create the inital latent noise
    batch_size = 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)
        if loss_fn is not None:
            if i%2 == 0:
                latents, custom_loss = additional_guidance(latents, scheduler, noise_pred, t, sigma, loss_fn, custom_loss_scale)
                print(i, 'loss:', custom_loss.item())

        # 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 generate_image_custom_style(prompt, style_num=None, random_seed=41, custom_loss_fn = None, custom_loss_scale=1.0):
    eos_pos = get_EOS_pos_in_prompt(prompt)

    style_token_embedding = None
    if style_num:
        style_token_embedding = get_style_embeddings(style_files[style_num])

    # tokenize
    text_input = tokenizer(prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")
    max_length = text_input.input_ids.shape[-1]
    input_ids = text_input.input_ids.to(torch_device)

    # get token embeddings
    token_emb_layer = text_encoder.text_model.embeddings.token_embedding
    token_embeddings = token_emb_layer(input_ids)

    # Append style token towards the end of the sentence embeddings
    if style_token_embedding is not None:
        token_embeddings[-1, eos_pos, :] = style_token_embedding

    # combine with pos embs
    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)
    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:
    generated_image = generate_with_embs(modified_output_embeddings, max_length, random_seed, custom_loss_fn, custom_loss_scale)
    return generated_image


def show_images(images_list):
    # Let's visualize the four channels of this latent representation:
    fig, axs = plt.subplots(1, len(images_list), figsize=(16, 4))
    for c in range(len(images_list)):
        axs[c].imshow(images_list[c])
    plt.show()

def brilliance_loss(image, target_brilliance=10):
    # Calculate the standard deviation of color channels
    std_dev = torch.std(image, dim=(2, 3))
    # Calculate the mean standard deviation across the batch
    mean_std_dev = torch.mean(std_dev)
    # Calculate the loss as the absolute difference from the target brilliance.
    loss = torch.abs(mean_std_dev - target_brilliance)
    return loss

import numpy as np
from PIL import Image

import torch
from scipy.stats import wasserstein_distance

def exposure_loss(image, target_exposure = 3):
    # Calculate the brightness (exposure) of the image.
    image_brightness = torch.mean(image)
    
    # Calculate the loss as the absolute difference from the target exposure.
    loss = torch.abs(image_brightness - target_exposure)
    return loss

def color_diversity_loss(images):
    # Calculate color diversity by measuring the variance of color channels (R, G, B).
    color_variance = torch.var(images, dim=(2, 3), keepdim=True)
    # Sum the color variances for each channel to get the total color diversity.
    total_color_diversity = torch.sum(color_variance, dim=1)
    return total_color_diversity

def sharpness_loss(images):
    # Apply the Laplacian filter to the images to measure sharpness.
    laplacian_filter = torch.Tensor([[-1, -1, -1],
                                    [-1, 8, -1],
                                    [-1, -1, -1]]).view(1, 1, 3, 3).to(images.device)
    
    # Expand the filter to match the number of channels in the input image.
    laplacian_filter = laplacian_filter.expand(-1, images.shape[1], -1, -1)

    # Apply the convolution operation.
    laplacian = torch.abs(F.conv2d(images, laplacian_filter))

    # Calculate sharpness as the negative of the Laplacian variance.
    sharpness = torch.var(laplacian)
    return sharpness

def display_images_in_rows(images_with_titles, titles):
    num_images = len(images_with_titles)
    rows = 5  # Display 5 rows always
    columns = 1 if num_images == 5 else 2  # Use 1 column if there are 5 images, otherwise 2 columns
    fig, axes = plt.subplots(rows, columns + 1, figsize=(15, 5 * rows))  # Add an extra column for titles

    for r in range(rows):
        # Add the title on the extreme left in the middle of each picture
        axes[r, 0].text(0.5, 0.5, titles[r], ha='center', va='center')
        axes[r, 0].axis('off')
        
        # Add "Without Loss" label above the first column and "With Loss" label above the second column (if applicable)
        if columns == 2:
            axes[r, 1].set_title("Without Loss", pad=10)
            axes[r, 2].set_title("With Loss", pad=10)

        for c in range(1, columns + 1):
            index = r * columns + c - 1
            if index < num_images:
                image, _ = images_with_titles[index]
                axes[r, c].imshow(image)
                axes[r, c].axis('off')

    return fig
    # plt.show()


def image_generator(prompt="cat", loss_function=None):
    images_without_loss = []
    images_with_loss = []

    for i in range(num_styles):
        generated_img = generate_image_custom_style(prompt, style_num=i, random_seed=seed_values[i], custom_loss_fn=None)
        images_without_loss.append(generated_img)

        if loss_function:
            if loss_function == "Exposure":
                generated_img = generate_image_custom_style(prompt, style_num=i, random_seed=seed_values[i], custom_loss_fn=exposure_loss)
            elif loss_function == "Color Diversity":
                generated_img = generate_image_custom_style(prompt, style_num=i, random_seed=seed_values[i], custom_loss_fn=color_diversity_loss)
            elif loss_function == "Sharpness":
                generated_img = generate_image_custom_style(prompt, style_num=i, random_seed=seed_values[i], custom_loss_fn=sharpness_loss)
            elif loss_function == "Brilliance":
                generated_img = generate_image_custom_style(prompt, style_num=i, random_seed=seed_values[i], custom_loss_fn=brilliance_loss)
            images_with_loss.append(generated_img)

    generated_sd_images = []
    titles = ["animal toy", "fft style", "mid journey", "oil style", "Space style"]

    for i in range(len(titles)):
        generated_sd_images.append((images_without_loss[i], titles[i])) 
        if images_with_loss != []:
          generated_sd_images.append((images_with_loss[i], titles[i])) 

    return display_images_in_rows(generated_sd_images, titles)

# Create a wrapper function for image_generator()
def image_generator_wrapper(prompt="dog", selected_loss="None"):
    return image_generator(prompt, selected_loss)

icon_html = '<i class="fas fa-chart-bar"></i>'
title = f"""
<div style="background-color: #f5f1f2; padding: 10px; display: flex; align-items: center;">
    {icon_html} <span style="margin-left: 10px;">Image Generation using Stable Diffusion</span>
</div>
"""
description = f"""
<div style="background-color: #f1f1f5; padding: 10px; display: flex; align-items: center;">
    {icon_html}
    <span style="margin-left: 10px;">
        <p><strong>Embedding New Styles Into Stable Diffusion</strong></p>
        <p>Following are some Losses tried</p>
        <ul>
            <li>exposure : It helps control the overall exposure of generated images. It ensures that the contrast of the generated images align with the desired aesthetic, preventing overexposure or underexposure</li>
            <li>Brilliance:  Brilliance loss is a loss function that emphasizes the brilliance or luminance of specific image components, such as highlights. It can be used to highlight or enhance certain aspects of the generated artwork, adding a touch of brilliance or radiance to the final image.</li>
            <li>color diversity: Color diversity loss encourages the model to produce images with a wider range of colors and hues. It helps create visually diverse and vibrant artworks by minimizing color repetition and promoting a rich color palette in the generated images</li>
            <li>sharpness: Sharpness loss is used to enhance the level of detail and clarity in generated images. It encourages the model to produce crisp and well-defined visual elements, leading to sharper and more realistic results.</li>
        </ul>
    </span>
</div>
"""

demo = gr.Interface(image_generator_wrapper,
                    inputs=[gr.Textbox(label="Enter prompt for generating Image", type="text", value="A ballerina cat dancing in space"),
                            gr.Radio(["None", "Exposure", "Color Diversity", "Sharpness", "Brilliance"], value="None", label="Select Loss")],
                    outputs=gr.Plot(label="Generated Images"),
                    title=title,
                    description=description)
demo.launch()