app.py

import random

import gradio as gr
import numpy as np
import spaces
import torch
import torchvision.transforms as transforms
from PIL import Image
from torchmetrics.functional.image import structural_similarity_index_measure as ssim
from transformers import CLIPModel, CLIPProcessor

import sourcecode

device = "cuda" if torch.cuda.is_available() else "cpu"
model_path = "czl/stable-diffusion-v1-5"
clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14").to(device)
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")

if torch.cuda.is_available():
    torch.cuda.max_memory_allocated(device=device)
    pipe = sourcecode.pipe_img(
        model_path=model_path,
        device=device,
        use_torchcompile=False,
    )
else:
    pipe = sourcecode.pipe_img(
        model_path=model_path,
        device=device,
        apply_optimization=False,
    )

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024


@spaces.GPU
def infer(
    input_image,
    prompt1,
    prompt2,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    interpolation_step,
    num_inference_steps,
    num_interpolation_steps,
):
    device = "cuda" if torch.cuda.is_available() else "cpu"

    # Input Validation
    try:
        assert num_interpolation_steps % 2 == 0
    except AssertionError:
        raise ValueError("num_interpolation_steps must be an even number")

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    prompts = [prompt1, prompt2]
    generator = torch.Generator().manual_seed(seed)

    sample_mid_interpolation = num_interpolation_steps
    remove_n_middle = 0

    interpolated_prompt_embeds, prompt_metadata = sourcecode.interpolatePrompts(
        prompts,
        pipe,
        num_interpolation_steps,
        sample_mid_interpolation,
        remove_n_middle=remove_n_middle,
        device=device,
    )
    negative_prompts = [negative_prompt, negative_prompt]
    if negative_prompts != ["", ""]:
        interpolated_negative_prompts_embeds, _ = sourcecode.interpolatePrompts(
            negative_prompts,
            pipe,
            num_interpolation_steps,
            sample_mid_interpolation,
            remove_n_middle=remove_n_middle,
            device=device,
        )
    else:
        interpolated_negative_prompts_embeds, _ = [None] * len(
            interpolated_prompt_embeds
        ), None

    latents = torch.randn(
        (1, pipe.unet.config.in_channels, height // 8, width // 8),
        generator=generator,
    ).to(device)
    embed_pairs = zip(interpolated_prompt_embeds, interpolated_negative_prompts_embeds)
    embed_pairs_list = list(embed_pairs)
    # offset step by -1
    prompt_embeds, negative_prompt_embeds = embed_pairs_list[interpolation_step - 1]
    preprocess_input = transforms.Compose(
        [transforms.ToTensor(), transforms.Resize((512, 512))]
    )
    input_img_tensor = preprocess_input(input_image).unsqueeze(0)
    if negative_prompt_embeds is not None:
        npe = negative_prompt_embeds[None, ...]
    else:
        npe = None
    images_list = pipe(
        height=height,
        width=width,
        num_images_per_prompt=1,
        prompt_embeds=prompt_embeds[None, ...],
        negative_prompt_embeds=npe,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        generator=generator,
        latents=latents,
        image=input_img_tensor,
    )
    if images_list["nsfw_content_detected"][0]:
        image = Image.open("samples/unsafe.jpeg")
        return image, seed, "Unsafe content detected", "Unsafe content detected"
    else:
        image = images_list.images[0]

    pred_image = transforms.ToTensor()(image).unsqueeze(0)
    ssim_score = ssim(pred_image, input_img_tensor).item()
    real_inputs = clip_processor(
        text=prompts, padding=True, images=input_image, return_tensors="pt"
    ).to(device)
    real_output = clip_model(**real_inputs)
    synth_inputs = clip_processor(
        text=prompts, padding=True, images=image, return_tensors="pt"
    ).to(device)
    synth_output = clip_model(**synth_inputs)
    cos_sim = torch.nn.CosineSimilarity(dim=1)
    cosine_sim = (
        cos_sim(real_output.image_embeds, synth_output.image_embeds)
        .detach()
        .cpu()
        .numpy()
        .squeeze()
        * 100
    )

    return image, seed, round(ssim_score, 4), round(cosine_sim, 2)


examples1 = [
    "A photo of a garbage truck, dustcart",
    "A photo of a Shih-Tzu, a type of dog",
]
examples2 = [
    "A photo of a cassette player",
    "A photo of a beagle, a type of dog",
]


def update_steps(total_steps, interpolation_step):
    return gr.update(maximum=total_steps)


def update_format(image_format):
    return gr.update(format=image_format)


if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

with gr.Blocks(title="Generative Date Augmentation Demo") as demo:

    gr.Markdown(
        """
    # 
    """
    )
    with gr.Row():
        with gr.Column():

            input_image = gr.Image(type="pil", label="Image to Augment")

            with gr.Row():
                prompt1 = gr.Text(
                    label="Prompt for the image to synthesize. (Actual class)",
                    show_label=True,
                    max_lines=1,
                    placeholder="Enter Prompt for the image to synthesize. (Actual class)",
                    container=False,
                )
            with gr.Row():
                prompt2 = gr.Text(
                    label="Prompt to augment against. (Confusing class)",
                    show_label=True,
                    max_lines=1,
                    placeholder="Enter Prompt to augment against. (Confusing class)",
                    container=False,
                )
            with gr.Row():
                gr.Examples(
                    examples=[
                        "samples/n03417042_5234.JPEG",
                        "samples/n02086240_2799.JPEG",
                    ],
                    inputs=[input_image],
                    label="Example Images",
                )
                gr.Examples(
                    examples=examples1,
                    inputs=[prompt1],
                    label="Example for Prompt 1 (Actual class)",
                )
                gr.Examples(
                    examples=examples2,
                    inputs=[prompt2],
                    label="Example for Prompt 2 (Confusing class)",
                )

            with gr.Row():
                num_interpolation_steps = gr.Slider(
                    label="Total Interpolation Steps",
                    minimum=2,
                    maximum=128,
                    step=2,
                    value=16,
                )
                interpolation_step = gr.Slider(
                    label="Sample Interpolation Step",
                    minimum=1,
                    maximum=16,
                    step=1,
                    value=8,
                )
                num_interpolation_steps.change(
                    fn=update_steps,
                    inputs=[num_interpolation_steps, interpolation_step],
                    outputs=[interpolation_step],
                )
                run_button = gr.Button("Run", scale=0)
            with gr.Accordion("Advanced Settings", open=True):
                negative_prompt = gr.Text(
                    label="Negative prompt",
                    max_lines=1,
                    placeholder="Enter a negative prompt",
                    visible=False,
                )
                seed = gr.Slider(
                    label="Seed",
                    minimum=0,
                    maximum=MAX_SEED,
                    step=1,
                    value=0,
                )
                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                gr.Markdown("Negative Prompt: ")
                with gr.Row():
                    negative_prompt = gr.Text(
                        label="Negative Prompt",
                        show_label=True,
                        max_lines=1,
                        value="blurry image, disfigured, deformed, distorted, cartoon, drawings",
                        container=False,
                    )
                with gr.Row():
                    width = gr.Slider(
                        label="Width",
                        minimum=256,
                        maximum=MAX_IMAGE_SIZE,
                        step=32,
                        value=512,
                    )
                    height = gr.Slider(
                        label="Height",
                        minimum=256,
                        maximum=MAX_IMAGE_SIZE,
                        step=32,
                        value=512,
                    )
                with gr.Row():
                    guidance_scale = gr.Slider(
                        label="Guidance scale",
                        minimum=0.0,
                        maximum=10.0,
                        step=0.1,
                        value=8.0,
                    )
                    num_inference_steps = gr.Slider(
                        label="Number of inference steps",
                        minimum=1,
                        maximum=80,
                        step=1,
                        value=25,
                    )
                with gr.Row():
                    image_type = gr.Radio(
                        choices=[
                            "webp",
                            "png",
                            "jpeg",
                        ],
                        label="Download Image Format",
                        value="jpeg",
                    )
        with gr.Column():
            result = gr.Image(label="Result", show_label=False, format="jpeg")
            image_type.change(
                fn=update_format,
                inputs=[image_type],
                outputs=[result],
            )
            gr.Markdown(
                """
                Metadata:
                """
            )
            with gr.Row():
                show_seed = gr.Label(label="Seed:", value="Randomized seed")
                ssim_score = gr.Label(
                    label="SSIM Score:", value="Generate to see score"
                )
                cos_sim = gr.Label(label="CLIP Score:", value="Generate to see score")
            if power_device == "GPU":
                gr.Markdown(
                    f"""
Currently running on {power_device}.
                    """
                )
            else:
                gr.Markdown(
                    f"""
Currently running on {power_device}.
Note: Running on CPU will take longer (approx. 6 minutes with default settings).
                    """
                )
            gr.Markdown(
                """
-
            """
            )
        run_button.click(
            fn=infer,
            inputs=[
                input_image,
                prompt1,
                prompt2,
                negative_prompt,
                seed,
                randomize_seed,
                width,
                height,
                guidance_scale,
                interpolation_step,
                num_inference_steps,
                num_interpolation_steps,
            ],
            outputs=[result, show_seed, ssim_score, cos_sim],
        )

demo.queue().launch(show_error=True)