app.py

if torch.cuda.is_available():
    pipe = StableDiffusionXLPipeline.from_pretrained(
        "RunDiffusion/Juggernaut-X-v10",
        torch_dtype=torch.float16
    )
    #pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
    
    
    #pipe.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
    #pipe.set_adapters("dalle")

    pipe.to("cuda")
    
    

import gradio as gr
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler, LCMScheduler, AutoencoderKL,DiffusionPipeline
import torch
import numpy as np
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
import spaces
import os
import random
import uuid

def save_image(img):
    unique_name = str(uuid.uuid4()) + ".png"
    img.save(unique_name)
    return unique_name

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

MAX_SEED = np.iinfo(np.int32).max
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)


JX_pipe = StableDiffusionXLPipeline.from_pretrained(
        "RunDiffusion/Juggernaut-X-Hyper",
        vae=vae,
        torch_dtype=torch.float16,
    )
JX_pipe.to("cuda")


J10_pipe = StableDiffusionXLPipeline.from_pretrained(
        "RunDiffusion/Juggernaut-X-v10",
        vae=vae,
        torch_dtype=torch.float16,
    )
J10_pipe.to("cuda")


J9_pipe = StableDiffusionXLPipeline.from_pretrained(
        "RunDiffusion/Juggernaut-X-v10",
        vae=vae,
        torch_dtype=torch.float16,
        use_safetensors=True,
        add_watermarker=False,
        variant="fp16"
    )
J9_pipe.to("cuda")

@spaces.GPU
def run_comparison(prompt: str,
    negative_prompt: str = "",
    use_negative_prompt: bool = False,
    num_inference_steps: int = 30,
    num_images_per_prompt: int = 2,
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    guidance_scale: float = 3,
    randomize_seed: bool = False,
    progress=gr.Progress(track_tqdm=True),
):
    seed = int(randomize_seed_fn(seed, randomize_seed))
    if not use_negative_prompt:
        negative_prompt = ""
    image_r3 = JX_pipe(prompt=prompt,
        negative_prompt=negative_prompt,
        width=width,
        height=height,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        num_images_per_prompt=num_images_per_prompt,
        cross_attention_kwargs={"scale": 0.65},
        output_type="pil",
    ).images
    image_paths_r3 = [save_image(img) for img in image_r3]

    image_r4 = JX10_pipe(prompt=prompt,
        negative_prompt=negative_prompt,
        width=width,
        height=height,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        num_images_per_prompt=num_images_per_prompt,
        cross_attention_kwargs={"scale": 0.65},
        output_type="pil",
    ).images
    image_paths_r4 = [save_image(img) for img in image_r4]

    image_r5 = JX9_pipe(prompt=prompt,
        negative_prompt=negative_prompt,
        width=width,
        height=height,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        num_images_per_prompt=num_images_per_prompt,
        cross_attention_kwargs={"scale": 0.65},
        output_type="pil",
    ).images
    image_paths_r5 = [save_image(img) for img in image_r5]
    return image_paths_r3, image_paths_r4,image_paths_r5, seed

examples = ["A dignified beaver wearing glasses, a vest, and colorful neck tie.",
"The spirit of a tamagotchi wandering in the city of Barcelona",
"an ornate, high-backed mahogany chair with a red cushion",
"a sketch of a camel next to a stream",
"a delicate porcelain teacup sits on a saucer, its surface adorned with intricate blue patterns",
"a baby swan grafitti",
"A bald eagle made of chocolate powder, mango, and whipped cream"
]

with gr.Blocks() as demo:
    gr.Markdown("## One step SDXL comparison 🦶")
    gr.Markdown('Compare SDXL variants and distillations able to generate images in a single diffusion step')
    prompt = gr.Textbox(label="Prompt")
    run = gr.Button("Run")
    with gr.Accordion("Advanced options", open=False):
        use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
        negative_prompt = gr.Text(
            label="Negative prompt",
            lines=4,
            max_lines=6,
            value="""(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, (NSFW:1.25)""",
            placeholder="Enter a negative prompt",
            visible=True,
        )
        with gr.Row():
            num_inference_steps = gr.Slider(
                label="Steps",
                minimum=10,
                maximum=60,
                step=1,
                value=30,
            )
        with gr.Row():
            num_images_per_prompt = gr.Slider(
                label="Images",
                minimum=1,
                maximum=5,
                step=1,
                value=2,
            )
        seed = gr.Slider(
            label="Seed",
            minimum=0,
            maximum=MAX_SEED,
            step=1,
            value=0,
            visible=True
        )
        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
        with gr.Row(visible=True):
            width = gr.Slider(
                label="Width",
                minimum=512,
                maximum=2048,
                step=8,
                value=1024,
            )
            height = gr.Slider(
                label="Height",
                minimum=512,
                maximum=2048,
                step=8,
                value=1024,
            )
        with gr.Row():
            guidance_scale = gr.Slider(
                label="Guidance Scale",
                minimum=0.1,
                maximum=20.0,
                step=0.1,
                value=6,
            )

    with gr.Row():
        with gr.Column():
            image_r3 = gr.Gallery(label="RealVisXL V3",columns=1, preview=True,)
            gr.Markdown("## [RealVisXL V3](https://huggingface.co)")
        with gr.Column():
            image_r4 = gr.Gallery(label="RealVisXL V4",columns=1, preview=True,)
            gr.Markdown("## [RealVisXL V4](https://huggingface.co)")
        with gr.Column():
            image_r5 = gr.Gallery(label="Playground v2.5",columns=1, preview=True,)
            gr.Markdown("## [Playground v2.5](https://huggingface.co)")    
    image_outputs = [image_r3, image_r4, image_r5]
    gr.on(
        triggers=[prompt.submit, run.click],
        fn=run_comparison,
        inputs=[
           prompt,
           negative_prompt,
           use_negative_prompt,
           num_inference_steps,
           num_images_per_prompt,
           seed,
           width,
           height,
           guidance_scale,
           randomize_seed,
       ],
        outputs=image_outputs
    )
    use_negative_prompt.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_negative_prompt,
        outputs=negative_prompt,
        api_name=False,
    )
    gr.Examples(
        examples=examples,
        fn=run_comparison,
        inputs=prompt,
        outputs=image_outputs,
        cache_examples=False,
        run_on_click=True
    )
if __name__ == "__main__":
    demo.queue(max_size=20).launch(show_api=False, debug=False)