app.py

import os
import spaces
import torch
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
import gradio as gr
import random
import tqdm

# Enable TQDM progress tracking
tqdm.monitor_interval = 0

#HF_TOKEN import
HF_TOKEN = os.getenv("HF_TOKEN")

# Load the diffusion pipeline
pipe = StableDiffusionXLPipeline.from_single_file(
    "https://huggingface.co/kayfahaarukku/AkashicPulse-v1.0/blob/main/AkashicPulse-v1.0-ft-ft.safetensors",  # Fixed URL
    torch_dtype=torch.float16,
    custom_pipeline="lpw_stable_diffusion_xl",
    use_safetensors=True,
    use_auth_token=HF_TOKEN,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)

# Function to generate an image
@spaces.GPU  # Adjust the duration as needed
def generate_image(prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress=gr.Progress()):
    try:
        pipe.to('cuda')  # Move the model to GPU when the function is called
        
        if randomize_seed:
            seed = random.randint(0, 99999999)
        if use_defaults:
            prompt = f"{prompt}, best quality, amazing quality, very aesthetic"
            negative_prompt = f"nsfw, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract], {negative_prompt}"
        generator = torch.manual_seed(seed)
        
        def callback(step, timestep, latents):
            progress(step / num_inference_steps)
            return
        
        width, height = map(int, resolution.split('x'))
        image = pipe(
            prompt, 
            negative_prompt=negative_prompt,
            width=width,
            height=height, 
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            generator=generator,
            callback=callback,
            callback_steps=1
        ).images[0]

        torch.cuda.empty_cache()

        metadata_text = f"{prompt}\nNegative prompt: {negative_prompt}\nSteps: {num_inference_steps}, Sampler: Euler a, Size: {width}x{height}, Seed: {seed}, CFG scale: {guidance_scale}"

        return image, seed, metadata_text
    except Exception as e:
        return None, seed, f"Error during generation: {str(e)}"

# Define Gradio interface
def interface_fn(prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress=gr.Progress()):
    image, seed, metadata_text = generate_image(prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress)
    if image is None:
        return gr.update(value=None), seed, gr.update(value=metadata_text)
    return image, seed, gr.update(value=metadata_text)

def reset_inputs():
    return gr.update(value=''), gr.update(value=''), gr.update(value=True), gr.update(value='832x1216'), gr.update(value=4), gr.update(value=28), gr.update(value=0), gr.update(value=True), gr.update(value='')

with gr.Blocks(title="irAsu 1.0 Demo", theme="NoCrypt/[email protected]") as demo:
    gr.HTML(
        "<h1>irAsu 1.0 Demo</h1>"
        "<p>This demo is intended to showcase what the model is capable of and is not intended to be the main generation platform. "
        "Results produced with Diffusers are not the best, and it's highly recommended for you to get the model running inside "
        "Stable Diffusion WebUI or ComfyUI.</p>"
    )
    with gr.Row():
        with gr.Column():
            prompt_input = gr.Textbox(lines=2, placeholder="Enter prompt here", label="Prompt")
            negative_prompt_input = gr.Textbox(lines=2, placeholder="Enter negative prompt here", label="Negative Prompt")
            use_defaults_input = gr.Checkbox(label="Use Default Quality Tags and Negative Prompt", value=True)
            resolution_input = gr.Radio(
                choices=[
                    "1024x1024", "1152x896", "896x1152", "1216x832", "832x1216",
                    "1344x768", "768x1344", "1536x640", "640x1536"
                ],
                label="Resolution",
                value="832x1216"
            )
            guidance_scale_input = gr.Slider(minimum=1, maximum=20, step=0.5, label="Guidance Scale", value=4)
            num_inference_steps_input = gr.Slider(minimum=1, maximum=100, step=1, label="Number of Inference Steps", value=28)
            seed_input = gr.Slider(minimum=0, maximum=999999999, step=1, label="Seed", value=0, interactive=True)
            randomize_seed_input = gr.Checkbox(label="Randomize Seed", value=True)
            generate_button = gr.Button("Generate")
            reset_button = gr.Button("Reset")

        with gr.Column():
            output_image = gr.Image(type="pil", label="Generated Image")
            with gr.Accordion("Parameters", open=False):
                gr.Markdown("This parameter is compatible with Stable Diffusion WebUI's parameter importer.")
                metadata_textbox = gr.Textbox(lines=6, label="Image Parameters", interactive=False, max_lines=6)
            gr.Markdown(
                """
                ### Recommended prompt formatting:
                `1girl/1boy, character name, from what series, everything else in any order, best quality, amazing quality, very aesthetic,`

                **PS:** `best quality, amazing quality, very aesthetic,` is automatically added when "Use Default Quality Tags and Negative Prompt" is enabled

                ### Recommended settings:
                - Steps: 25-30
                - CFG: 3.5-5
                - Sweet spot: 28 steps, 4 CFG
                """
            )

    generate_button.click(
        interface_fn,
        inputs=[
            prompt_input, negative_prompt_input, use_defaults_input, resolution_input, 
            guidance_scale_input, num_inference_steps_input, seed_input, randomize_seed_input
        ],
        outputs=[output_image, seed_input, metadata_textbox]
    )
    
    reset_button.click(
        reset_inputs,
        inputs=[],
        outputs=[
            prompt_input, negative_prompt_input, use_defaults_input, resolution_input,
            guidance_scale_input, num_inference_steps_input, seed_input, randomize_seed_input, metadata_textbox
        ]
    )

demo.queue(max_size=20).launch(share=False)