app.py

#!/usr/bin/env python

from __future__ import annotations

import os
import random

import gradio as gr
import numpy as np
import PIL.Image
import spaces
import torch
from diffusers import AutoPipelineForText2Image

DESCRIPTION = "# Kandinsky 3"
if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"

MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
MAX_NUM_INFERENCE_STEPS = int(os.getenv("MAX_NUM_INFERENCE_STEPS", "1024"))

if torch.cuda.is_available():
    pipe = AutoPipelineForText2Image.from_pretrained(
        "kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16
    )
    pipe.enable_model_cpu_offload()


def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed


@spaces.GPU
def generate(
    prompt: str,
    negative_prompt: str = "",
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    guidance_scale: float = 3.0,
    num_inference_steps: int = 25,
    progress=gr.Progress(track_tqdm=True),
) -> PIL.Image.Image:
    if num_inference_steps > MAX_NUM_INFERENCE_STEPS:
        raise ValueError(f"Number of inference steps must be less than or equal to {MAX_NUM_INFERENCE_STEPS}")
    if width > MAX_IMAGE_SIZE or height > MAX_IMAGE_SIZE:
        raise ValueError(f"Image width and height must be less than or equal to {MAX_IMAGE_SIZE}")

    generator = torch.Generator().manual_seed(seed)
    return pipe(
        prompt,
        negative_prompt=negative_prompt,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        generator=generator,
        width=width,
        height=height,
    ).images[0]


examples = [
    "A photograph of the inside of a subway train. There are raccoons sitting on the seats. One of them is reading a newspaper. The window shows the city in the background.",
    "A beautiful landscape outdoors scene in the crochet knitting art style, drawing in style by Alfons Mucha",
    "gorgeous phoenix, cosmic, darkness, epic, cinematic, moonlight, stars, high - definition, texture,Oscar-Claude Monet",
    "a yellow house at the edge of the danish fjord, in the style of eiko ojala, ingrid baars, ad posters, mountainous vistas, george ault, realistic details, dark white and dark gray, 4k",
    "dragon fruit head, upper body, realistic, illustration by Joshua Hoffine Norman Rockwell, scary, creepy, biohacking, futurism, Zaha Hadid style",
    "Amazing playful nice cute strawberry character, dynamic poze, surreal fantazy garden background, gorgeous masterpice, award winning photo, soft natural lighting, 3d, Blender, Octane render, tilt - shift, deep field, colorful, I can't believe how beautiful this is, colorful, cute and sweet baby - loved photo",
    "beautiful fairy-tale desert, in the sky a wave of sand merges with the milky way, stars, cosmism, digital art, 8k",
    "Car, mustang, movie, person, poster, car cover, person, in the style of alessandro gottardo, gold and cyan, gerald harvey jones, reflections, highly detailed illustrations, industrial urban scenes",
    "cloud in blue sky, a red lip, collage art, shuji terayama, dreamy objects, surreal, criterion collection, showa era, intricate details, mirror",
]

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
    )
    with gr.Group():
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            run_button = gr.Button("Run", scale=0)
        result = gr.Image(label="Result", show_label=False)
        with gr.Accordion("Advanced options", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
            )
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            width = gr.Slider(
                label="Width",
                minimum=256,
                maximum=MAX_IMAGE_SIZE,
                step=32,
                value=1024,
            )
            height = gr.Slider(
                label="Height",
                minimum=256,
                maximum=MAX_IMAGE_SIZE,
                step=32,
                value=1024,
            )
            guidance_scale = gr.Slider(
                label="Guidance scale",
                minimum=1,
                maximum=20,
                step=0.1,
                value=3.0,
            )
            num_inference_steps = gr.Slider(
                label="Number of inference steps",
                minimum=10,
                maximum=MAX_NUM_INFERENCE_STEPS,
                step=1,
                value=25,
            )

    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=result,
        fn=generate,
        cache_examples=CACHE_EXAMPLES,
    )

    gr.on(
        triggers=[prompt.submit, negative_prompt.submit, run_button.click],
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=result,
        api_name="run",
    )

if __name__ == "__main__":
    demo.queue(max_size=20).launch()