gradio_app.py

import os
import torch
import gradio as gr
from PIL import Image
from diffusers import StableDiffusionPipeline, DiffusionPipeline
from diffusers import AutoencoderKL, LMSDiscreteScheduler, UNet2DConditionModel
from tqdm.auto import tqdm
import torchvision.transforms as T
import torch.nn.functional as F
import gc

# Configure constants
HEIGHT, WIDTH = 512, 512
GUIDANCE_SCALE = 8
LOSS_SCALE = 200
NUM_INFERENCE_STEPS = 50
BATCH_SIZE = 1
DEFAULT_PROMPT = "A deadly witcher slinging a sword with a lion medallion in his neck, casting a fire spell from his hand in a snowy forest"

# Define the device
TORCH_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"

# Initialize the elastic transformer
elastic_transformer = T.ElasticTransform(alpha=550.0, sigma=5.0)

# Load the model
def load_model():
    pipe = DiffusionPipeline.from_pretrained(
        "CompVis/stable-diffusion-v1-4",
        torch_dtype=torch.float16 if TORCH_DEVICE == "cuda" else torch.float32
    ).to(TORCH_DEVICE)
    
    # Load textual inversion concepts
    try:
        pipe.load_textual_inversion("sd-concepts-library/rimworld-art-style", mean_resizing=False)
        pipe.load_textual_inversion("sd-concepts-library/hk-goldenlantern", mean_resizing=False)
        pipe.load_textual_inversion("sd-concepts-library/phoenix-01", mean_resizing=False)
        pipe.load_textual_inversion("sd-concepts-library/fractal-flame", mean_resizing=False)
        pipe.load_textual_inversion("sd-concepts-library/scarlet-witch", mean_resizing=False)
    except Exception as e:
        print(f"Warning: Could not load all textual inversion concepts: {e}")
        
    return pipe

# Helper functions
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))
    
    for i, img in enumerate(imgs):
        grid.paste(img, box=(i%cols*w, i//cols*h))
    return grid

def image_loss(images, loss_type):
    if loss_type == 'blue':
        # blue loss
        error = torch.abs(images[:,2] - 0.9).mean()
    elif loss_type == 'elastic':
        # elastic loss
        transformed_imgs = elastic_transformer(images)
        error = torch.abs(transformed_imgs - images).mean()
    elif loss_type == 'symmetry':
        flipped_image = torch.flip(images, [3])
        error = F.mse_loss(images, flipped_image)
    elif loss_type == 'saturation':
        # saturation loss
        transformed_imgs = T.functional.adjust_saturation(images, saturation_factor=10)
        error = torch.abs(transformed_imgs - images).mean()
    else:
        print("Error. Loss not defined")
        error = torch.tensor(0.0)

    return error

def latents_to_pil(latents, pipe):
    # batch of latents -> list of images
    latents = (1 / 0.18215) * latents
    with torch.no_grad():
        image = pipe.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 generate_image(pipe, seed_no, prompts, loss_type, loss_apply=False, progress=gr.Progress()):
    # Initialization and Setup
    generator = torch.manual_seed(seed_no)

    scheduler = LMSDiscreteScheduler(
        beta_start=0.00085, 
        beta_end=0.012, 
        beta_schedule="scaled_linear", 
        num_train_timesteps=1000
    )
    scheduler.set_timesteps(NUM_INFERENCE_STEPS)
    scheduler.timesteps = scheduler.timesteps.to(torch.float32)

    # Text Processing
    text_input = pipe.tokenizer(
        prompts, 
        padding='max_length', 
        max_length=pipe.tokenizer.model_max_length, 
        truncation=True, 
        return_tensors="pt"
    )
    input_ids = text_input.input_ids.to(TORCH_DEVICE)

    # Convert text inputs to embeddings
    with torch.no_grad():
        text_embeddings = pipe.text_encoder(input_ids)[0]

    # Handle padding and truncation of text inputs
    max_length = text_input.input_ids.shape[-1]
    uncond_input = pipe.tokenizer(
        [""] * BATCH_SIZE, 
        padding="max_length", 
        max_length=max_length, 
        return_tensors="pt"
    )

    with torch.no_grad():
        uncond_embeddings = pipe.text_encoder(uncond_input.input_ids.to(TORCH_DEVICE))[0]

    # Concatenate unconditioned and text embeddings
    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

    # Create random initial latents
    latents = torch.randn(
        (BATCH_SIZE, pipe.unet.config.in_channels, HEIGHT // 8, WIDTH // 8),
        generator=generator,
    )

    # Move latents to device and apply noise scaling
    if TORCH_DEVICE == "cuda":
        latents = latents.to(torch.float16)
    latents = latents.to(TORCH_DEVICE)
    latents = latents * scheduler.init_noise_sigma

    # Diffusion Process
    for i, t in progress.tqdm(enumerate(scheduler.timesteps), total=len(scheduler.timesteps)):
        # Process the latent model input
        latent_model_input = torch.cat([latents] * 2)
        sigma = scheduler.sigmas[i]
        latent_model_input = scheduler.scale_model_input(latent_model_input, t)

        with torch.no_grad():
            noise_pred = pipe.unet(
                latent_model_input, 
                t, 
                encoder_hidden_states=text_embeddings
            )["sample"]

        # Apply noise prediction
        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
        noise_pred = noise_pred_uncond + GUIDANCE_SCALE * (noise_pred_text - noise_pred_uncond)

        # Apply loss if requested
        if loss_apply and i % 5 == 0:
            latents = latents.detach().requires_grad_()
            latents_x0 = latents - sigma * noise_pred

            # Use VAE to decode the image
            denoised_images = pipe.vae.decode((1 / 0.18215) * latents_x0).sample / 2 + 0.5

            # Apply loss
            loss = image_loss(denoised_images, loss_type) * LOSS_SCALE
            print(f"Step {i}, Loss: {loss.item()}")

            # Compute gradients for optimization
            cond_grad = torch.autograd.grad(loss, latents)[0]
            latents = latents.detach() - cond_grad * sigma**2

        # Update latents using the scheduler
        latents = scheduler.step(noise_pred, t, latents).prev_sample

    return latents

def generate_images(prompt, loss_type, apply_loss, seeds, pipe):
    latents_collect = []
    
    # Convert comma-separated string to list and clean
    seeds = [int(seed.strip()) for seed in seeds.split(',') if seed.strip()]
    
    if not seeds:
        seeds = [1000]  # Default seed if none provided
        
    # List of SD concepts (can be empty if not used)
    sdconcepts = [''] * len(seeds)
    
    # Generate images for each seed
    for seed_no, sd in zip(seeds, sdconcepts):
        # Clear CUDA cache
        if TORCH_DEVICE == "cuda":
            torch.cuda.empty_cache()
            gc.collect()
            torch.cuda.empty_cache()
        
        # Generate image
        prompts = [f'{prompt} {sd}']
        latents = generate_image(pipe, seed_no, prompts, loss_type, loss_apply=apply_loss)
        latents_collect.append(latents)
    
    # Stack latents and convert to images
    latents_collect = torch.vstack(latents_collect)
    images = latents_to_pil(latents_collect, pipe)
    
    # Create image grid
    if len(images) > 1:
        result = image_grid(images, 1, len(images))
        return result
    else:
        return images[0]

# Gradio Interface
def create_interface():
    pipe = load_model()
    
    with gr.Blocks(title="Stable Diffusion Text Inversion with Loss Functions") as app:
        gr.Markdown("""

        # Stable Diffusion Text Inversion with Loss Functions

        

        Generate images using Stable Diffusion with various loss functions to guide the diffusion process.

        """)
        
        with gr.Row():
            with gr.Column():
                prompt = gr.Textbox(
                    label="Prompt", 
                    value=DEFAULT_PROMPT,
                    lines=3
                )
                
                loss_type = gr.Radio(
                    label="Loss Type",
                    choices=["N/A", "blue", "elastic", "symmetry", "saturation"],
                    value="N/A"
                )
                
                apply_loss = gr.Checkbox(
                    label="Apply Loss Function", 
                    value=False
                )
                
                seeds = gr.Textbox(
                    label="Seeds (comma-separated)",
                    value="3000,2000,1000",
                    lines=1
                )
                
                generate_btn = gr.Button("Generate Images")
                
            with gr.Column():
                output_image = gr.Image(label="Generated Image")
                
        generate_btn.click(
            fn=lambda p, lt, al, s: generate_images(p, lt, al, s, pipe),
            inputs=[prompt, loss_type, apply_loss, seeds],
            outputs=output_image
        )
        
        gr.Markdown("""

        ## About the Loss Functions

        

        - **Blue**: Encourages more blue tones in the image

        - **Elastic**: Creates distortion effects by minimizing differences with elastically transformed versions

        - **Symmetry**: Encourages symmetrical images by minimizing differences with horizontally flipped versions

        - **Saturation**: Increases color saturation in the image

        

        Set "N/A" and uncheck "Apply Loss Function" for normal image generation.

        """)
    
    return app

if __name__ == "__main__":
    # Create and launch the interface
    app = create_interface()
    app.launch()