Update app.py

app.py

@@ -1,531 +1,166 @@
-import spaces
 import gradio as gr
+import numpy as np
+import spaces
 import torch
-from PIL import Image
-from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
 import random
-import uuid
-from typing import Tuple, Union, List, Optional, Any, Dict
-import numpy as np
-import time
-import zipfile
-from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
-
-# Description for the app
-DESCRIPTION = """## flux comparator hpc/."""
+from PIL import Image
 
-# Helper functions
-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
+from diffusers import FluxKontextPipeline
+from diffusers.utils import load_image
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 2048
-
-# Load pipelines for both models
-# Flux.1-dev-realism
-base_model_dev = "prithivMLmods/Flux.1-Merged" # Merge of (black-forest-labs/FLUX.1-dev + black-forest-labs/FLUX.1-schnell)
-pipe_dev = DiffusionPipeline.from_pretrained(base_model_dev, torch_dtype=torch.bfloat16)
-lora_repo = "strangerzonehf/Flux-Super-Realism-LoRA"
-trigger_word = "Super Realism"
-pipe_dev.load_lora_weights(lora_repo)
-pipe_dev.to("cuda")
-
-# Flux.1-krea
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
-taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
-# Merge of (black-forest-labs/FLUX.1-dev + https://huggingface.co/black-forest-labs/FLUX.1-Krea-dev)
-good_vae = AutoencoderKL.from_pretrained("prithivMLmods/Flux.1-Krea-Merged-Dev", subfolder="vae", torch_dtype=dtype).to(device)
-pipe_krea = DiffusionPipeline.from_pretrained("prithivMLmods/Flux.1-Krea-Merged-Dev", torch_dtype=dtype, vae=taef1).to(device)
-
-# Define the flux_pipe_call_that_returns_an_iterable_of_images for flux.1-krea
-@torch.inference_mode()
-def flux_pipe_call_that_returns_an_iterable_of_images(
-    self,
-    prompt: Union[str, List[str]] = None,
-    prompt_2: Optional[Union[str, List[str]]] = None,
-    height: Optional[int] = None,
-    width: Optional[int] = None,
-    num_inference_steps: int = 28,
-    timesteps: List[int] = None,
-    guidance_scale: float = 3.5,
-    num_images_per_prompt: Optional[int] = 1,
-    generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-    latents: Optional[torch.FloatTensor] = None,
-    prompt_embeds: Optional[torch.FloatTensor] = None,
-    pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-    output_type: Optional[str] = "pil",
-    return_dict: bool = True,
-    joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-    max_sequence_length: int = 512,
-    good_vae: Optional[Any] = None,
-):
-    height = height or self.default_sample_size * self.vae_scale_factor
-    width = width or self.default_sample_size * self.vae_scale_factor
-
-    self.check_inputs(
-        prompt,
-        prompt_2,
-        height,
-        width,
-        prompt_embeds=prompt_embeds,
-        pooled_prompt_embeds=pooled_prompt_embeds,
-        max_sequence_length=max_sequence_length,
-    )
-
-    self._guidance_scale = guidance_scale
-    self._joint_attention_kwargs = joint_attention_kwargs
-    self._interrupt = False
-
-    batch_size = 1 if isinstance(prompt, str) else len(prompt)
-    device = self._execution_device
-
-    lora_scale = joint_attention_kwargs.get("scale", None) if joint_attention_kwargs is not None else None
-    prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt(
-        prompt=prompt,
-        prompt_2=prompt_2,
-        prompt_embeds=prompt_embeds,
-        pooled_prompt_embeds=pooled_prompt_embeds,
-        device=device,
-        num_images_per_prompt=num_images_per_prompt,
-        max_sequence_length=max_sequence_length,
-        lora_scale=lora_scale,
-    )
-
-    num_channels_latents = self.transformer.config.in_channels // 4
-    latents, latent_image_ids = self.prepare_latents(
-        batch_size * num_images_per_prompt,
-        num_channels_latents,
-        height,
-        width,
-        prompt_embeds.dtype,
-        device,
-        generator,
-        latents,
-    )
 
-    sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
-    image_seq_len = latents.shape[1]
-    mu = calculate_shift(
-        image_seq_len,
-        self.scheduler.config.base_image_seq_len,
-        self.scheduler.config.max_image_seq_len,
-        self.scheduler.config.base_shift,
-        self.scheduler.config.max_shift,
-    )
-    timesteps, num_inference_steps = retrieve_timesteps(
-        self.scheduler,
-        num_inference_steps,
-        device,
-        timesteps,
-        sigmas,
-        mu=mu,
-    )
-    self._num_timesteps = len(timesteps)
-
-    guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None
-
-    for i, t in enumerate(timesteps):
-        if self.interrupt:
-            continue
-
-        timestep = t.expand(latents.shape[0]).to(latents.dtype)
-
-        noise_pred = self.transformer(
-            hidden_states=latents,
-            timestep=timestep / 1000,
-            guidance=guidance,
-            pooled_projections=pooled_prompt_embeds,
-            encoder_hidden_states=prompt_embeds,
-            txt_ids=text_ids,
-            img_ids=latent_image_ids,
-            joint_attention_kwargs=self.joint_attention_kwargs,
-            return_dict=False,
-        )[0]
-
-        latents_for_image = self._unpack_latents(latents, height, width, self.vae_scale_factor)
-        latents_for_image = (latents_for_image / self.vae.config.scaling_factor) + self.vae.config.shift_factor
-        image = self.vae.decode(latents_for_image, return_dict=False)[0]
-        yield self.image_processor.postprocess(image, output_type=output_type)[0]
-        
-        latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-        torch.cuda.empty_cache()
-
-    latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
-    latents = (latents / good_vae.config.scaling_factor) + good_vae.config.shift_factor
-    image = good_vae.decode(latents, return_dict=False)[0]
-    self.maybe_free_model_hooks()
-    torch.cuda.empty_cache()
-    yield self.image_processor.postprocess(image, output_type=output_type)[0]
-
-pipe_krea.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe_krea)
-
-# Helper functions for flux.1-krea
-def calculate_shift(
-    image_seq_len,
-    base_seq_len: int = 256,
-    max_seq_len: int = 4096,
-    base_shift: float = 0.5,
-    max_shift: float = 1.16,
-):
-    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
-    b = base_shift - m * base_seq_len
-    mu = image_seq_len * m + b
-    return mu
+pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to("cuda")
 
-def retrieve_timesteps(
-    scheduler,
-    num_inference_steps: Optional[int] = None,
-    device: Optional[Union[str, torch.device]] = None,
-    timesteps: Optional[List[int]] = None,
-    sigmas: Optional[List[float]] = None,
-    **kwargs,
-):
-    if timesteps is not None and sigmas is not None:
-        raise ValueError("Only one of `timesteps` or `sigmas` can be passed.")
-    if timesteps is not None:
-        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-        num_inference_steps = len(timesteps)
-    elif sigmas is not None:
-        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-        num_inference_steps = len(timesteps)
-    else:
-        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-    return timesteps, num_inference_steps
-
-# Styles for flux.1-dev-realism
-style_list = [
-    {"name": "3840 x 2160", "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": ""},
-    {"name": "2560 x 1440", "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": ""},
-    {"name": "HD+", "prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": ""},
-    {"name": "Style Zero", "prompt": "{prompt}", "negative_prompt": ""},
-]
-
-styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
-DEFAULT_STYLE_NAME = "Style Zero"
-STYLE_NAMES = list(styles.keys())
-
-def apply_style(style_name: str, positive: str) -> Tuple[str, str]:
-    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
-    return p.replace("{prompt}", positive), n
-
-# Generation function for flux.1-dev-realism
 @spaces.GPU
-def generate_dev(
-    prompt: str,
-    negative_prompt: str = "",
-    use_negative_prompt: bool = False,
-    seed: int = 0,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale: float = 3,
-    randomize_seed: bool = False,
-    style_name: str = DEFAULT_STYLE_NAME,
-    num_inference_steps: int = 30,
-    num_images: int = 1,
-    zip_images: bool = False,
-    progress=gr.Progress(track_tqdm=True),
-):
-    positive_prompt, style_negative_prompt = apply_style(style_name, prompt)
-    
-    if use_negative_prompt:
-        final_negative_prompt = style_negative_prompt + " " + negative_prompt
-    else:
-        final_negative_prompt = style_negative_prompt
-    
-    final_negative_prompt = final_negative_prompt.strip()
-    
-    if trigger_word:
-        positive_prompt = f"{trigger_word} {positive_prompt}"
-    
-    seed = int(randomize_seed_fn(seed, randomize_seed))
-    generator = torch.Generator(device="cuda").manual_seed(seed)
-    
-    start_time = time.time()
-    
-    images = pipe_dev(
-        prompt=positive_prompt,
-        negative_prompt=final_negative_prompt if final_negative_prompt else None,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        num_images_per_prompt=num_images,
-        generator=generator,
-        output_type="pil",
-    ).images
-    
-    end_time = time.time()
-    duration = end_time - start_time
-    
-    image_paths = [save_image(img) for img in images]
-    
-    zip_path = None
-    if zip_images:
-        zip_name = str(uuid.uuid4()) + ".zip"
-        with zipfile.ZipFile(zip_name, 'w') as zipf:
-            for i, img_path in enumerate(image_paths):
-                zipf.write(img_path, arcname=f"Img_{i}.png")
-        zip_path = zip_name
-    
-    return image_paths, seed, f"{duration:.2f}", zip_path
-
-# Generation function for flux.1-krea
-@spaces.GPU
-def generate_krea(
-    prompt: str,
-    seed: int = 0,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale: float = 4.5,
-    randomize_seed: bool = False,
-    num_inference_steps: int = 28,
-    num_images: int = 1,
-    zip_images: bool = False,
-    progress=gr.Progress(track_tqdm=True),
-):
+def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, steps=28, progress=gr.Progress(track_tqdm=True)):
+    """
+    Perform image editing using the FLUX.1 Kontext pipeline.
+    
+    This function takes an input image and a text prompt to generate a modified version
+    of the image based on the provided instructions. It uses the FLUX.1 Kontext model
+    for contextual image editing tasks.
+    
+    Args:
+        input_image (PIL.Image.Image): The input image to be edited. Will be converted
+            to RGB format if not already in that format.
+        prompt (str): Text description of the desired edit to apply to the image.
+            Examples: "Remove glasses", "Add a hat", "Change background to beach".
+        seed (int, optional): Random seed for reproducible generation. Defaults to 42.
+            Must be between 0 and MAX_SEED (2^31 - 1).
+        randomize_seed (bool, optional): If True, generates a random seed instead of
+            using the provided seed value. Defaults to False.
+        guidance_scale (float, optional): Controls how closely the model follows the
+            prompt. Higher values mean stronger adherence to the prompt but may reduce
+            image quality. Range: 1.0-10.0. Defaults to 2.5.
+        steps (int, optional): Controls how many steps to run the diffusion model for.
+            Range: 1-30. Defaults to 28.
+        progress (gr.Progress, optional): Gradio progress tracker for monitoring
+            generation progress. Defaults to gr.Progress(track_tqdm=True).
+    
+    Returns:
+        tuple: A 3-tuple containing:
+            - PIL.Image.Image: The generated/edited image
+            - int: The seed value used for generation (useful when randomize_seed=True)
+            - gr.update: Gradio update object to make the reuse button visible
+    
+    Example:
+        >>> edited_image, used_seed, button_update = infer(
+        ...     input_image=my_image,
+        ...     prompt="Add sunglasses",
+        ...     seed=123,
+        ...     randomize_seed=False,
+        ...     guidance_scale=2.5
+        ... )
+    """
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator().manual_seed(seed)
-    
-    start_time = time.time()
     
-    images = []
-    for _ in range(num_images):
-        final_img = list(pipe_krea.flux_pipe_call_that_returns_an_iterable_of_images(
+    if input_image:
+        input_image = input_image.convert("RGB")
+        image = pipe(
+            image=input_image, 
             prompt=prompt,
             guidance_scale=guidance_scale,
-            num_inference_steps=num_inference_steps,
-            width=width,
-            height=height,
-            generator=generator,
-            output_type="pil",
-            good_vae=good_vae,
-        ))[-1]  # Take the final image only
-        images.append(final_img)
-    
-    end_time = time.time()
-    duration = end_time - start_time
-    
-    image_paths = [save_image(img) for img in images]
-    
-    zip_path = None
-    if zip_images:
-        zip_name = str(uuid.uuid4()) + ".zip"
-        with zipfile.ZipFile(zip_name, 'w') as zipf:
-            for i, img_path in enumerate(image_paths):
-                zipf.write(img_path, arcname=f"Img_{i}.png")
-        zip_path = zip_name
-    
-    return image_paths, seed, f"{duration:.2f}", zip_path
-
-# Main generation function to handle model choice
-@spaces.GPU
-def generate(
-    model_choice: str,
-    prompt: str,
-    negative_prompt: str = "",
-    use_negative_prompt: bool = False,
-    seed: int = 0,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale: float = 3,
-    randomize_seed: bool = False,
-    style_name: str = DEFAULT_STYLE_NAME,
-    num_inference_steps: int = 30,
-    num_images: int = 1,
-    zip_images: bool = False,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if model_choice == "flux.1-dev-merged":
-        return generate_dev(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            use_negative_prompt=use_negative_prompt,
-            seed=seed,
-            width=width,
-            height=height,
-            guidance_scale=guidance_scale,
-            randomize_seed=randomize_seed,
-            style_name=style_name,
-            num_inference_steps=num_inference_steps,
-            num_images=num_images,
-            zip_images=zip_images,
-            progress=progress,
-        )
-    elif model_choice == "flux.1-krea-merged-dev":
-        return generate_krea(
+            width = input_image.size[0],
+            height = input_image.size[1],
+            num_inference_steps=steps,
+            generator=torch.Generator().manual_seed(seed),
+        ).images[0]
+    else:
+        image = pipe(
             prompt=prompt,
-            seed=seed,
-            width=width,
-            height=height,
             guidance_scale=guidance_scale,
-            randomize_seed=randomize_seed,
-            num_inference_steps=num_inference_steps,
-            num_images=num_images,
-            zip_images=zip_images,
-            progress=progress,
-        )
-    else:
-        raise ValueError("Invalid model choice")
+            num_inference_steps=steps,
+            generator=torch.Generator().manual_seed(seed),
+        ).images[0]
+    return image, seed, gr.Button(visible=True)
 
-# Examples 
-examples = [
-    "An attractive young woman with blue eyes lying face down on the bed, in the style of animated gifs, light white and light amber, jagged edges, the snapshot aesthetic, timeless beauty, goosepunk, sunrays shine upon it --no freckles --chaos 65 --ar 1:2 --profile yruxpc2 --stylize 750 --v 6.1",
-    "Headshot of handsome young man, wearing dark gray sweater with buttons and big shawl collar, brown hair and short beard, serious look on his face, black background, soft studio lighting, portrait photography --ar 85:128 --v 6.0 --style",
-    "Purple Dreamy, a medium-angle shot of a young woman with long brown hair, wearing a pair of eye-level glasses, stands in front of a backdrop of purple and white lights.",
-    "High-resolution photograph, woman, UHD, photorealistic, shot on a Sony A7III --chaos 20 --ar 1:2 --style raw --stylize 250"
-]
-
-css = '''
-.gradio-container {
-    max-width: 590px !important;
-    margin: 0 auto !important;
-}
-h1 {
-    text-align: center;
-}
-footer {
-    visibility: hidden;
+@spaces.GPU
+def infer_example(input_image, prompt):
+    image, seed, _ = infer(input_image, prompt)
+    return image, seed
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 960px;
 }
-'''
+"""
 
-# Gradio interface
-with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
-    gr.Markdown(DESCRIPTION)
-    with gr.Row():
-        prompt = gr.Text(
-            label="Prompt",
-            show_label=False,
-            max_lines=1,
-            placeholder="Enter your prompt",
-            container=False,
-        )
-        run_button = gr.Button("Run", scale=0, variant="primary")
-    result = gr.Gallery(label="Result", columns=1, show_label=False, preview=True)
+with gr.Blocks(css=css) as demo:
     
-    with gr.Row():
-    # Model choice radio button above additional options
-        model_choice = gr.Radio(
-            choices=["flux.1-krea-merged-dev", "flux.1-dev-merged"],
-            label="Select Model",
-            value="flux.1-krea-merged-dev"
-        )
-    
-    with gr.Accordion("Additional Options", open=False):
-        style_selection = gr.Dropdown(
-            label="Quality Style (for flux.1-dev-realism only)",
-            choices=STYLE_NAMES,
-            value=DEFAULT_STYLE_NAME,
-            interactive=True,
-        )
-        use_negative_prompt = gr.Checkbox(label="Use negative prompt (for flux.1-dev-realism only)", value=False)
-        negative_prompt = gr.Text(
-            label="Negative prompt",
-            max_lines=1,
-            placeholder="Enter a negative prompt",
-            visible=False,
-        )
-        seed = gr.Slider(
-            label="Seed",
-            minimum=0,
-            maximum=MAX_SEED,
-            step=1,
-            value=0,
-        )
-        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""# FLUX.1 Kontext [dev]
+Image editing and manipulation model guidance-distilled from FLUX.1 Kontext [pro], [[blog]](https://bfl.ai/announcements/flux-1-kontext-dev) [[model]](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev)
+        """)
         with gr.Row():
-            width = gr.Slider(
-                label="Width",
-                minimum=512,
-                maximum=2048,
-                step=64,
-                value=1024,
-            )
-            height = gr.Slider(
-                label="Height",
-                minimum=512,
-                maximum=2048,
-                step=64,
-                value=1024,
-            )
-        guidance_scale = gr.Slider(
-            label="Guidance Scale",
-            minimum=0.1,
-            maximum=20.0,
-            step=0.1,
-            value=3.5,
-        )
-        num_inference_steps = gr.Slider(
-            label="Number of inference steps",
-            minimum=1,
-            maximum=40,
-            step=1,
-            value=28,
-        )
-        num_images = gr.Slider(
-            label="Number of images",
-            minimum=1,
-            maximum=5,
-            step=1,
-            value=1,
-        )
-        zip_images = gr.Checkbox(label="Zip generated images", value=False)
+            with gr.Column():
+                input_image = gr.Image(label="Upload the image for editing", type="pil")
+                with gr.Row():
+                    prompt = gr.Text(
+                        label="Prompt",
+                        show_label=False,
+                        max_lines=1,
+                        placeholder="Enter your prompt for editing (e.g., 'Remove glasses', 'Add a hat')",
+                        container=False,
+                    )
+                    run_button = gr.Button("Run", scale=0)
+                with gr.Accordion("Advanced Settings", open=False):
+                    
+                    seed = gr.Slider(
+                        label="Seed",
+                        minimum=0,
+                        maximum=MAX_SEED,
+                        step=1,
+                        value=0,
+                    )
+                    
+                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+                    
+                    guidance_scale = gr.Slider(
+                        label="Guidance Scale",
+                        minimum=1,
+                        maximum=10,
+                        step=0.1,
+                        value=2.5,
+                    )       
+                    
+                    steps = gr.Slider(
+                        label="Steps",
+                        minimum=1,
+                        maximum=30,
+                        value=28,
+                        step=1
+                    )
+                    
+            with gr.Column():
+                result = gr.Image(label="Result", show_label=False, interactive=False)
+                reuse_button = gr.Button("Reuse this image", visible=False)
         
-        gr.Markdown("### Output Information")
-        seed_display = gr.Textbox(label="Seed used", interactive=False)
-        generation_time = gr.Textbox(label="Generation time (seconds)", interactive=False)
-        zip_file = gr.File(label="Download ZIP")
-
-    gr.Examples(
-        examples=examples,
-        inputs=prompt,
-        outputs=[result, seed_display, generation_time, zip_file],
-        fn=generate,
-        cache_examples=False,
-    )
-
-    use_negative_prompt.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_negative_prompt,
-        outputs=negative_prompt,
-        api_name=False,
-    )
-
+            
+        examples = gr.Examples(
+            examples=[
+                ["images/14.png", "Change the cat’s eyes to blue."],
+                ["images/15.png", "Change the weather to rainy."],
+                ["images/16.png", "Change the hair color to gray."]
+            ],
+            inputs=[input_image, prompt],
+            outputs=[result, seed],
+            fn=infer_example,
+            cache_examples="lazy"
+        )
+            
     gr.on(
-        triggers=[
-            prompt.submit,
-            run_button.click,
-        ],
-        fn=generate,
-        inputs=[
-            model_choice,
-            prompt,
-            negative_prompt,
-            use_negative_prompt,
-            seed,
-            width,
-            height,
-            guidance_scale,
-            randomize_seed,
-            style_selection,
-            num_inference_steps,
-            num_images,
-            zip_images,
-        ],
-        outputs=[result, seed_display, generation_time, zip_file],
-        api_name="run",
+        triggers=[run_button.click, prompt.submit],
+        fn = infer,
+        inputs = [input_image, prompt, seed, randomize_seed, guidance_scale, steps],
+        outputs = [result, seed, reuse_button]
+    )
+    reuse_button.click(
+        fn = lambda image: image,
+        inputs = [result],
+        outputs = [input_image]
     )
 
-if __name__ == "__main__":
-    demo.queue(max_size=30).launch(mcp_server=True, ssr_mode=False, show_error=True)
+demo.launch(mcp_server=True)