check_app.py

import spaces
import torch
import random
import numpy as np
from inspect import signature
from diffusers import (
    FluxPipeline,
    StableDiffusion3Pipeline,
    PixArtSigmaPipeline,
    SanaPipeline,
    AuraFlowPipeline,
    Kandinsky3Pipeline,
    HunyuanDiTPipeline,
    LuminaText2ImgPipeline,AutoPipelineForText2Image
)
import gradio as gr
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from pathlib import Path
import time
import os
from datetime import datetime
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
    
pipe = {}
    
class ProgressPipeline(DiffusionPipeline):
    def __init__(self, original_pipeline):
        super().__init__()
        self.original_pipeline = original_pipeline
        # Register all components from the original pipeline
        for attr_name, attr_value in vars(original_pipeline).items():
            setattr(self, attr_name, attr_value)
    
    @torch.no_grad()
    def __call__(
        self,
        prompt,
        num_inference_steps=30,
        generator=None,
        guidance_scale=7.5,
        callback=None,
        callback_steps=1,
        **kwargs
    ):
        # Initialize the progress tracking
        self._num_inference_steps = num_inference_steps
        self._step = 0
        
        def progress_callback(step_index, timestep, callback_kwargs):
            if callback and step_index % callback_steps == 0:
                # Pass self (the pipeline) to the callback
                callback(self, step_index, timestep, callback_kwargs)
            return callback_kwargs
        
        # Monkey patch the original pipeline's progress tracking
        original_step = self.original_pipeline.scheduler.step
        def wrapped_step(*args, **kwargs):
            self._step += 1
            progress_callback(self._step, None, {})
            return original_step(*args, **kwargs)
        
        self.original_pipeline.scheduler.step = wrapped_step
        
        try:
            # Call the original pipeline
            result = self.original_pipeline(
                prompt=prompt,
                num_inference_steps=num_inference_steps,
                generator=generator,
                guidance_scale=guidance_scale,
                **kwargs
            )
            
            return result
        finally:
            # Restore the original step function
            self.original_pipeline.scheduler.step = original_step

cache_dir = '/workspace/hf_cache'

MODEL_CONFIGS = {
        "FLUX": {
        "repo_id": "black-forest-labs/FLUX.1-dev",
        "pipeline_class": FluxPipeline,
    },
    "Stable Diffusion 3.5": {
        "repo_id": "stabilityai/stable-diffusion-3.5-large",
        "pipeline_class": StableDiffusion3Pipeline,
         
    },
    "PixArt": {
        "repo_id": "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
        "pipeline_class": PixArtSigmaPipeline,
        
    },
    "SANA": {
        "repo_id": "Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers",
        "pipeline_class": SanaPipeline,
         
    },
    "AuraFlow": {
        "repo_id": "fal/AuraFlow",
        "pipeline_class": AuraFlowPipeline,
         
    },
    "Kandinsky": {
        "repo_id": "kandinsky-community/kandinsky-3",
        "pipeline_class": Kandinsky3Pipeline,
        
    },
    "Hunyuan": {
        "repo_id": "Tencent-Hunyuan/HunyuanDiT-Diffusers",
        "pipeline_class": HunyuanDiTPipeline,
         
    },
    "Lumina": {
        "repo_id": "Alpha-VLLM/Lumina-Next-SFT-diffusers",
        "pipeline_class": LuminaText2ImgPipeline,
         
    }
}

def generate_image_with_progress(model_name,pipe, prompt, num_steps, guidance_scale=3.5, seed=None,negative_prompt=None,  randomize_seed=None, width=1024, height=1024, num_inference_steps=40,  progress=gr.Progress(track_tqdm=False)):
    generator = None
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    if seed is not None:
        generator = torch.Generator("cuda").manual_seed(seed)
    else:
        generator = torch.Generator("cuda")

    def callback(pipe, step_index, timestep, callback_kwargs):
        print(f" callback => {step_index}, {timestep}")
        if step_index is None:
            step_index = 0
        cur_prg = step_index / num_steps
        progress(cur_prg, desc=f"Step {step_index}/{num_steps}")
        return callback_kwargs
    print(f"START GENR {model_name} {prompt} ")
    # Get the signature of the pipe
    pipe_signature = signature(pipe)
    
    # Check for the presence of "guidance_scale" and "callback_on_step_end" in the signature
    has_guidance_scale = "guidance_scale" in pipe_signature.parameters
    has_callback_on_step_end = "callback_on_step_end" in pipe_signature.parameters
    
    # Define common arguments
    common_args = {
        "prompt": prompt,
        "num_inference_steps": num_steps,
        "negative_prompt": negative_prompt,
        "width": width,
        "height": height,
        "generator": generator,
    }
    
    if has_guidance_scale:
        common_args["guidance_scale"] = guidance_scale
    
    if has_callback_on_step_end:
        print("has callback_on_step_end and", "has guidance_scale" if has_guidance_scale else "NO guidance_scale")
        common_args["callback_on_step_end"] = callback
    else:
        print("NO callback_on_step_end and", "has guidance_scale" if has_guidance_scale else "NO guidance_scale")
        common_args["callback"] = callback
        common_args["callback_steps"] = 1
    
    # Generate image
    image = pipe(**common_args).images[0]
    filepath = save_generated_image(image, model_name, prompt)
    # Then, reload the gallery
    images, load_message = load_images_from_directory(model_name)
    print(f"Saved image to: {filepath}")
    return seed, image, images

@spaces.GPU(duration=170)
def create_pipeline_logic(prompt_text, model_name, negative_prompt="",  seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=4.5, num_inference_steps=40,):
    print(f"starting {model_name} {prompt_text}")
    progress = gr.Progress(track_tqdm=False)
    config = MODEL_CONFIGS[model_name]
    pipe_class = config["pipeline_class"]
    global pipe
    if "pipeline_class" not in pipe or pipe["pipeline_class"] != pipe_class:
        print(f"NO PIPE. LOADING NEW {model_name}")
        pipe["pipeline_class"] = pipe_class
        b_pipe = AutoPipelineForText2Image.from_pretrained(
            config["repo_id"],
            #variant="fp16",
            #cache_dir=config["cache_dir"],
            torch_dtype=torch.bfloat16
        ).to("cuda")
        pipe_signature = signature(b_pipe)
        # Check for the presence of "callback_on_step_end" in the signature
        has_callback_on_step_end = "callback_on_step_end" in pipe_signature.parameters
        if not has_callback_on_step_end:
            pipe["pipeline"] = ProgressPipeline(b_pipe)
            print("ProgressPipeline specal")
        else:
            pipe["pipeline"] = b_pipe
    else:
        print(f"HAS PIPE. LOADING EXISTIN  {model_name}")
        
    gen_seed,image, images = generate_image_with_progress(
        model_name,pipe["pipeline"], prompt_text, num_steps=num_inference_steps, guidance_scale=guidance_scale, seed=seed,negative_prompt = negative_prompt,  randomize_seed = randomize_seed, width = width, height = height, progress=progress
    )
    return f"Seed: {gen_seed}", image, images
def main():

    with gr.Blocks() as app:
        gr.Markdown("# Dynamic Multiple Model Image Generation")

        prompt_text = gr.Textbox(label="Enter prompt")

        with gr.Accordion("Advanced Settings", 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=100,
                value=0,
            )

            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=512,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
                height = gr.Slider(
                    label="Height",
                    minimum=512,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=7.5,
                    step=0.1,
                    value=4.5,
                )
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=40,
                )
        
        for model_name, config in MODEL_CONFIGS.items():
            #global gallery
            with gr.Tab(model_name) as tab_model:
                run_button = gr.Button(f"Run {model_name}")
                output = gr.Textbox(label="Status")
                img = gr.Image(label=model_name, height=300)

                

                gallery = gr.Gallery(
                    label="Image Gallery",
                    show_label=True,
                    columns=4,
                    rows=3,
                    height=600,
                    object_fit="contain"
                    )
                
                tab_model.select(
                    fn=load_images_from_directory,
                    inputs=[gr.Text(value= model_name,visible=False)],
                    outputs=[gallery],
                )
                run_button.click(fn=create_pipeline_logic, inputs=[prompt_text, gr.Text(value= model_name,visible=False), negative_prompt,
                    seed,
                    randomize_seed,
                    width,
                    height,
                    guidance_scale,
                    num_inference_steps], outputs=[output, img, gallery])
                
        

    app.launch()

def save_generated_image(image, model_name, prompt):
    """Save generated image with timestamp and model name"""
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    # Create sanitized filename from prompt (first 30 chars)
    prompt_part = "".join(c for c in prompt[:30] if c.isalnum() or c in (' ', '-', '_')).strip()
    filename = f"{timestamp}_{model_name}_{prompt_part}.png"
    path = Path(model_name)
    path.mkdir(parents=True, exist_ok=True)
    filepath = os.path.join(model_name, filename)
    image.save(filepath)
    return filepath

def load_images_from_directory(directory_path):
    """
    Load all images from the specified directory.
    Returns a list of image file paths.
    """
    print(f"Loading images {directory_path}")
    image_extensions = {'.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp'}
    directory = Path(directory_path)
    
    if not directory.exists():
        print(f"NO Direc {directory_path} ")
        return [], f"Error: Directory '{directory_path}' does not exist"
    
    image_files = [
        str(f) for f in directory.iterdir()
        if f.suffix.lower() in image_extensions and f.is_file()
    ]
    
    if not image_files:
        print(f"NO images {directory_path} ")
        return [], f"No images found in directory '{directory_path}'"
    print(f"has images {directory_path} {len(image_files)}")
    return image_files, f"Found {len(image_files)} images"

if __name__ == "__main__":
    main()