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 DPMSolverMultistepScheduler, AutoencoderKL, DiffusionPipeline

DESCRIPTION = "# SDXL"
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", "1824"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
ENABLE_REFINER = os.getenv("ENABLE_REFINER", "1") == "1"
ENABLE_USE_LORA = os.getenv("ENABLE_USE_LORA", "1") == "1"
ENABLE_USE_VAE = os.getenv("ENABLE_USE_VAE", "1") == "1"

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

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 = "",
    prompt_2: str = "",
    negative_prompt_2: str = "",
    use_negative_prompt: bool = False,
    use_prompt_2: bool = False,
    use_negative_prompt_2: bool = False,
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    guidance_scale_base: float = 5.0,
    guidance_scale_refiner: float = 5.0,
    num_inference_steps_base: int = 25,
    num_inference_steps_refiner: int = 25,
    use_vae: bool = False,
    use_lora: bool = False,
    apply_refiner: bool = False,
    model = 'SG161222/Realistic_Vision_V6.0_B1_noVAE',
    vaecall = 'stabilityai/sd-vae-ft-mse',
    lora = 'amazonaws-la/juliette',
    lora_scale: float = 0.7,
) -> PIL.Image.Image:
    if torch.cuda.is_available():

        if not use_vae:  
            pipe = DiffusionPipeline.from_pretrained(model, torch_dtype=torch.float16)
            pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)

        if use_vae:
            vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16)
            pipe = DiffusionPipeline.from_pretrained(model, vae=vae, torch_dtype=torch.float16)
            pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
        
        if use_lora:
            pipe.load_lora_weights(lora)
            pipe.fuse_lora(lora_scale=0.7)
                                   
        if ENABLE_CPU_OFFLOAD:
            pipe.enable_model_cpu_offload()
            
        else:
            pipe.to(device)

        if USE_TORCH_COMPILE:
            pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
        
    generator = torch.Generator().manual_seed(seed)

    if not use_negative_prompt:
        negative_prompt = None  # type: ignore
    if not use_prompt_2:
        prompt_2 = None  # type: ignore
    if not use_negative_prompt_2:
        negative_prompt_2 = None  # type: ignore

    if not apply_refiner:
        return pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            prompt_2=prompt_2,
            negative_prompt_2=negative_prompt_2,
            width=width,
            height=height,
            guidance_scale=guidance_scale_base,
            num_inference_steps=num_inference_steps_base,
            generator=generator,
            output_type="pil",
        ).images[0]
    else:
        latents = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            prompt_2=prompt_2,
            negative_prompt_2=negative_prompt_2,
            width=width,
            height=height,
            guidance_scale=guidance_scale_base,
            num_inference_steps=num_inference_steps_base,
            generator=generator,
            output_type="latent",
        ).images
        image = refiner(
            prompt=prompt,
            negative_prompt=negative_prompt,
            prompt_2=prompt_2,
            negative_prompt_2=negative_prompt_2,
            guidance_scale=guidance_scale_refiner,
            num_inference_steps=num_inference_steps_refiner,
            image=latents,
            generator=generator,
        ).images[0]
        return image


examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
]

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():
        model = gr.Text(label='Modelo')
        vaecall = gr.Text(label='VAE')
        lora = gr.Text(label='LoRA')
        lora_scale = gr.Slider(
                label="Lora Scale",
                minimum=0.01,
                maximum=1,
                step=0.01,
                value=0.7,
            )
        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):
        with gr.Row():
            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
            use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False)
            use_negative_prompt_2 = gr.Checkbox(label="Use negative prompt 2", value=False)
        negative_prompt = gr.Text(
            label="Negative prompt",
            max_lines=1,
            placeholder="Enter a negative prompt",
            visible=False,
        )
        prompt_2 = gr.Text(
            label="Prompt 2",
            max_lines=1,
            placeholder="Enter your prompt",
            visible=False,
        )
        negative_prompt_2 = gr.Text(
            label="Negative prompt 2",
            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)
        with gr.Row():
            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,
            )
        use_vae = gr.Checkbox(label='Use VAE', value=False, visible=ENABLE_USE_VAE)
        use_lora = gr.Checkbox(label='Use Lora', value=False, visible=ENABLE_USE_LORA)
        apply_refiner = gr.Checkbox(label="Apply refiner", value=False, visible=ENABLE_REFINER)
        with gr.Row():
            guidance_scale_base = gr.Slider(
                label="Guidance scale for base",
                minimum=1,
                maximum=20,
                step=0.1,
                value=5.0,
            )
            num_inference_steps_base = gr.Slider(
                label="Number of inference steps for base",
                minimum=10,
                maximum=100,
                step=1,
                value=25,
            )
        with gr.Row(visible=False) as refiner_params:
            guidance_scale_refiner = gr.Slider(
                label="Guidance scale for refiner",
                minimum=1,
                maximum=20,
                step=0.1,
                value=5.0,
            )
            num_inference_steps_refiner = gr.Slider(
                label="Number of inference steps for refiner",
                minimum=10,
                maximum=100,
                step=1,
                value=25,
            )

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

    use_negative_prompt.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_negative_prompt,
        outputs=negative_prompt,
        queue=False,
        api_name=False,
    )
    use_prompt_2.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_prompt_2,
        outputs=prompt_2,
        queue=False,
        api_name=False,
    )
    use_negative_prompt_2.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_negative_prompt_2,
        outputs=negative_prompt_2,
        queue=False,
        api_name=False,
    )
    use_vae.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_vae,
        outputs=vaecall,
        queue=False,
        api_name=False,
    )
    use_lora.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_lora,
        outputs=lora,
        queue=False,
        api_name=False,
    )
    apply_refiner.change(
        fn=lambda x: gr.update(visible=x),
        inputs=apply_refiner,
        outputs=refiner_params,
        queue=False,
        api_name=False,
    )

    gr.on(
        triggers=[
            prompt.submit,
            negative_prompt.submit,
            prompt_2.submit,
            negative_prompt_2.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,
            prompt_2,
            negative_prompt_2,
            use_negative_prompt,
            use_prompt_2,
            use_negative_prompt_2,
            seed,
            width,
            height,
            guidance_scale_base,
            guidance_scale_refiner,
            num_inference_steps_base,
            num_inference_steps_refiner,
            use_vae,
            use_lora,
            apply_refiner,
            model,
            vaecall,
            lora,
            lora_scale,
        ],
        outputs=result,
        api_name="run",
    )

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