app.py

import os
import spaces
import torch
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
from safetensors.torch import load_file
import gradio as gr
import random
import tqdm
from huggingface_hub import hf_hub_download

# Enable TQDM progress tracking
tqdm.monitor_interval = 0

# Load the model from safetensors file
def load_model():
    model_path = hf_hub_download(
        repo_id="kayfahaarukku/AkashicPulse-v1.0",
        filename="AkashicPulse-v1.0-ft-ft.safetensors"
    )
    
    # Initialize pipeline with base SDXL configuration
    pipe = StableDiffusionXLPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0",
        torch_dtype=torch.float16,
        variant="fp16",
        use_safetensors=True
    )
    
    # Load the custom model weights
    state_dict = load_file(model_path)
    pipe.unet.load_state_dict(state_dict)
    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
    return pipe

# Load the pipeline
pipe = load_model()

# Function to generate an image
@spaces.GPU
def generate_image(prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress=gr.Progress()):
    pipe.to('cuda')
    
    if randomize_seed:
        seed = random.randint(0, 99999999)
    if use_defaults:
        prompt = f"{prompt}, masterpiece, best quality"
        negative_prompt = f"lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, signature, watermark, username, blurry, {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

# 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)
    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=7), gr.update(value=28), gr.update(value=0), gr.update(value=True), gr.update(value='')

with gr.Blocks(title="AkashicPulse v1.0 Demo", theme="NoCrypt/[email protected]") as demo:
    gr.HTML(
        "<h1>AkashicPulse v1.0 Demo</h1>"
        "This demo showcases the AkashicPulse v1.0 model capabilities. For best results, it's recommended to run the model in Stable Diffusion WebUI or ComfyUI with MaHiRo CFG enabled."
    )
    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=4, maximum=10, step=0.5, label="Guidance Scale (CFG)", value=7)
            num_inference_steps_input = gr.Slider(minimum=20, maximum=30, step=1, label="Number of 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, series, by artist name, the rest of the prompt, masterpiece, best quality`

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

                ### Current settings (recommended):
                - Sampler: Euler a (fixed)
                - Steps: 20-30 (sweet spot: 28)
                - CFG: 4-10 (sweet spot: 7)
                - Optional: Enable MaHiRo CFG in reForge or ComfyUI
                """
            )

    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)