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

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

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=None, seed=None, progress=gr.Progress(track_tqdm=True)):
    generator = None
    if seed is not None:
        generator = torch.Generator("cuda").manual_seed(seed)

    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 ")
    # 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
    
    if has_guidance_scale and has_callback_on_step_end:
        print("has callback_on_step_end and has guidance_scale")
        image = pipe(
            prompt,
            num_inference_steps=num_steps,
            generator=generator,
            guidance_scale=guidance_scale,
            callback_on_step_end=callback,
        ).images[0]
    elif not has_callback_on_step_end and has_guidance_scale:
        print("NO callback_on_step_end and has guidance_scale")
        image = pipe(
            prompt,
            num_inference_steps=num_steps,
            guidance_scale=guidance_scale,
            generator=generator,
            callback=callback,
            callback_steps=1,
        ).images[0]
    elif has_callback_on_step_end and not has_guidance_scale:
        print("has callback_on_step_end and NO guidance_scale")
        image = pipe(
            prompt,
            num_inference_steps=num_steps,
            generator=generator,
            callback_on_step_end=callback,
        ).images[0]
    elif not has_callback_on_step_end and not has_guidance_scale:
        print("NO callback_on_step_end and NO guidance_scale")
        image = pipe(
            prompt,
            num_inference_steps=num_steps,
            generator=generator,
            callback=callback,
            callback_steps=1,
        ).images[0]

    return image

@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}")
    progress = gr.Progress(track_tqdm=True)
    num_steps = 30
    guidance_scale = 7.5  # Example guidance scale, can be adjusted per model
    seed = 42
    config = MODEL_CONFIGS[model_name]
    pipe_class = config["pipeline_class"]
    pipe = None
    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 = ProgressPipeline(b_pipe)
        print("ProgressPipeline specal")
    else:
        pipe = b_pipe
        
    image = generate_image_with_progress(
        model_name,pipe, prompt_text, num_steps=num_steps, guidance_scale=guidance_scale, seed=seed, progress=progress
    )
    return f"Seed: {seed}", image

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():
            with gr.Tab(model_name):
                button = gr.Button(f"Run {model_name}")
                output = gr.Textbox(label="Status")
                img = gr.Image(label=model_name, height=300)

                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])

    app.launch()


if __name__ == "__main__":
    main()