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LICENSE

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+MIT License
+
+Copyright (c) 2024 Ashleigh Watson and Alex Nasa
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

app.py

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+import warnings
+warnings.filterwarnings("ignore")
+from diffusers import StableDiffusionPipeline, DDIMInverseScheduler, DDIMScheduler
+import torch
+from typing import Optional
+from tqdm import tqdm
+from diffusers.models.attention_processor import Attention, AttnProcessor2_0
+import torchvision
+import torch.nn as nn
+import torch.nn.functional as F
+import gc
+import gradio as gr
+import numpy as np
+import os
+import pickle
+from transformers import CLIPImageProcessor
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+import argparse
+
+weights = {
+    'down': {
+        4096: 0.0,
+        1024: 1.0,
+        256: 1.0,
+    },
+    'mid': {
+        64: 1.0,
+    },
+    'up': {
+        256: 1.0,
+        1024: 1.0,
+        4096: 0.0,
+    }
+}
+num_inference_steps = 10
+model_id = "stabilityai/stable-diffusion-2-1-base"
+
+pipe = StableDiffusionPipeline.from_pretrained(model_id).to("cuda")
+inverse_scheduler = DDIMInverseScheduler.from_pretrained(model_id, subfolder="scheduler")
+scheduler = DDIMScheduler.from_pretrained(model_id, subfolder="scheduler")
+
+safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker").to("cuda")
+feature_extractor = CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+should_stop = False
+
+def save_state_to_file(state):
+    filename = "state.pkl"
+    with open(filename, 'wb') as f:
+        pickle.dump(state, f) 
+    return filename
+
+def load_state_from_file(filename):
+    with open(filename, 'rb') as f:
+        state = pickle.load(f) 
+    return state 
+
+def stop_reconstruct():
+  global should_stop
+  should_stop = True
+
+def reconstruct(input_img, caption):
+
+  img = input_img
+
+  cond_prompt_embeds = pipe.encode_prompt(prompt=caption, device="cuda", num_images_per_prompt=1, do_classifier_free_guidance=False)[0]
+  uncond_prompt_embeds = pipe.encode_prompt(prompt="", device="cuda", num_images_per_prompt=1, do_classifier_free_guidance=False)[0]
+
+  prompt_embeds_combined = torch.cat([uncond_prompt_embeds, cond_prompt_embeds])
+
+
+  transform = torchvision.transforms.Compose([
+      torchvision.transforms.Resize((512, 512)),
+      torchvision.transforms.ToTensor()
+  ])
+
+  loaded_image = transform(img).to("cuda").unsqueeze(0)
+
+  if loaded_image.shape[1] == 4:
+      loaded_image = loaded_image[:,:3,:,:]
+
+  with torch.no_grad():
+      encoded_image = pipe.vae.encode(loaded_image*2 - 1)
+      real_image_latents = pipe.vae.config.scaling_factor * encoded_image.latent_dist.sample()
+
+  guidance_scale = 1
+  inverse_scheduler.set_timesteps(num_inference_steps, device="cuda")
+  timesteps = inverse_scheduler.timesteps
+
+  latents = real_image_latents
+
+  inversed_latents = []
+
+  with torch.no_grad():
+
+      replace_attention_processor(pipe.unet, True)
+
+      for i, t in tqdm(enumerate(timesteps), total=len(timesteps), desc="Inference steps"):
+
+          inversed_latents.append(latents)
+
+          latent_model_input = torch.cat([latents] * 2)
+
+          noise_pred = pipe.unet(
+              latent_model_input,
+              t,
+              encoder_hidden_states=prompt_embeds_combined,
+              cross_attention_kwargs=None,
+              return_dict=False,
+          )[0]
+
+
+          noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+          noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+          latents = inverse_scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+
+  # initial state
+  real_image_initial_latents = latents
+
+  W_values = uncond_prompt_embeds.repeat(num_inference_steps, 1, 1)
+  QT = nn.Parameter(W_values.clone())
+
+
+  guidance_scale = 7.5
+  scheduler.set_timesteps(num_inference_steps, device="cuda")
+  timesteps = scheduler.timesteps
+
+  optimizer = torch.optim.AdamW([QT], lr=0.008)
+
+  pipe.vae.eval()
+  pipe.vae.requires_grad_(False)
+  pipe.unet.eval()
+  pipe.unet.requires_grad_(False)
+
+  last_loss = 1
+
+  for epoch in range(50):
+      gc.collect()
+      torch.cuda.empty_cache()
+
+      if last_loss < 0.02:
+          break
+      elif last_loss < 0.03:
+          for param_group in optimizer.param_groups:
+              param_group['lr'] = 0.003
+      elif last_loss < 0.035:
+          for param_group in optimizer.param_groups:
+              param_group['lr'] = 0.006
+
+      intermediate_values = real_image_initial_latents.clone()
+
+
+      for i in range(num_inference_steps):
+          latents = intermediate_values.detach().clone()
+
+          t = timesteps[i]
+
+          prompt_embeds = torch.cat([QT[i].unsqueeze(0), cond_prompt_embeds.detach()])
+
+          latent_model_input = torch.cat([latents] * 2)
+
+          noise_pred_model = pipe.unet(
+              latent_model_input,
+              t,
+              encoder_hidden_states=prompt_embeds,
+              cross_attention_kwargs=None,
+              return_dict=False,
+          )[0]
+
+          noise_pred_uncond, noise_pred_text = noise_pred_model.chunk(2)
+          noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+          intermediate_values = scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+
+          loss = F.mse_loss(inversed_latents[len(timesteps) - 1 - i].detach(), intermediate_values, reduction="mean")
+          last_loss = loss
+
+          optimizer.zero_grad()
+          loss.backward()
+          optimizer.step()
+
+      global should_stop
+      if should_stop:
+        should_stop = False
+        break
+
+      image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]
+      image = (image / 2.0 + 0.5).clamp(0.0, 1.0)
+      safety_checker_input = feature_extractor(image, return_tensors="pt", do_rescale=False).to("cuda")
+      image = safety_checker(images=[image], clip_input=safety_checker_input.pixel_values.to("cuda"))[0]
+      image_np = image[0].squeeze(0).float().permute(1,2,0).detach().cpu().numpy()
+      image_np = (image_np * 255).astype(np.uint8)
+
+      yield image_np, caption, [caption, real_image_initial_latents, QT]
+
+  image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]
+  image = (image / 2.0 + 0.5).clamp(0.0, 1.0)
+  safety_checker_input = feature_extractor(image, return_tensors="pt", do_rescale=False).to("cuda")
+  image = safety_checker(images=[image], clip_input=safety_checker_input.pixel_values.to("cuda"))[0]
+  image_np = image[0].squeeze(0).float().permute(1,2,0).detach().cpu().numpy()
+  image_np = (image_np * 255).astype(np.uint8)
+  
+  yield image_np, caption, [caption, real_image_initial_latents, QT]
+
+
+class AttnReplaceProcessor(AttnProcessor2_0):
+
+    def __init__(self, replace_all, weight):
+        super().__init__()
+        self.replace_all = replace_all
+        self.weight = weight
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        temb: Optional[torch.FloatTensor] = None,
+        *args,
+        **kwargs,
+    ) -> torch.FloatTensor:
+
+        residual = hidden_states
+
+        is_cross = not encoder_hidden_states is None
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, _, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        attention_scores = attn.scale * torch.bmm(query, key.transpose(-1, -2))
+
+        dimension_squared = hidden_states.shape[1]
+
+        if not is_cross and (self.replace_all):
+            ucond_attn_scores_src, ucond_attn_scores_dst, attn_scores_src, attn_scores_dst = attention_scores.chunk(4)
+            attn_scores_dst.copy_(self.weight[dimension_squared] * attn_scores_src + (1.0 - self.weight[dimension_squared]) * attn_scores_dst)
+            ucond_attn_scores_dst.copy_(self.weight[dimension_squared] * ucond_attn_scores_src + (1.0 - self.weight[dimension_squared]) * ucond_attn_scores_dst)
+
+        attention_probs = attention_scores.softmax(dim=-1)
+        del attention_scores
+
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+        del attention_probs
+
+        hidden_states = attn.to_out[0](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+def replace_attention_processor(unet, clear = False):
+
+  for name, module in unet.named_modules():
+    if 'attn1' in name and 'to' not in name:
+        layer_type = name.split('.')[0].split('_')[0]
+
+        if not clear:
+          if layer_type == 'down':
+              module.processor = AttnReplaceProcessor(True, weights['down'])
+          elif layer_type == 'mid':
+              module.processor = AttnReplaceProcessor(True, weights['mid'])
+          elif layer_type == 'up':
+              module.processor = AttnReplaceProcessor(True, weights['up'])
+        else:
+          module.processor = AttnReplaceProcessor(False, 0.0)
+
+def apply_prompt(meta_data, new_prompt):
+
+  caption, real_image_initial_latents, QT = meta_data
+
+  inference_steps = len(QT)
+
+  cond_prompt_embeds = pipe.encode_prompt(prompt=caption, device="cuda", num_images_per_prompt=1, do_classifier_free_guidance=False)[0]
+#   uncond_prompt_embeds = pipe.encode_prompt(prompt=caption, device="cuda", num_images_per_prompt=1, do_classifier_free_guidance=False)[0]
+  new_prompt_embeds = pipe.encode_prompt(prompt=new_prompt, device="cuda", num_images_per_prompt=1, do_classifier_free_guidance=False)[0]
+
+  guidance_scale = 7.5
+  scheduler.set_timesteps(inference_steps, device="cuda")
+  timesteps = scheduler.timesteps
+
+  latents = torch.cat([real_image_initial_latents] * 2)
+
+  with torch.no_grad():
+    replace_attention_processor(pipe.unet)
+
+    for i, t in tqdm(enumerate(timesteps), total=len(timesteps), desc="Inference steps"):
+
+        modified_prompt_embeds = torch.cat([QT[i].unsqueeze(0), QT[i].unsqueeze(0), cond_prompt_embeds, new_prompt_embeds])
+        latent_model_input = torch.cat([latents] * 2)
+
+        noise_pred = pipe.unet(
+            latent_model_input,
+            t,
+            encoder_hidden_states=modified_prompt_embeds,
+            cross_attention_kwargs=None,
+            return_dict=False,
+        )[0]
+
+
+        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+        latents = scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+    replace_attention_processor(pipe.unet, True)
+
+    image = pipe.vae.decode(latents[1].unsqueeze(0) / pipe.vae.config.scaling_factor, return_dict=False)[0]
+    image = (image / 2.0 + 0.5).clamp(0.0, 1.0)
+    safety_checker_input = feature_extractor(image, return_tensors="pt", do_rescale=False).to("cuda")
+    image = safety_checker(images=[image], clip_input=safety_checker_input.pixel_values.to("cuda"))[0]
+    image_np = image[0].squeeze(0).float().permute(1,2,0).detach().cpu().numpy()
+    image_np = (image_np * 255).astype(np.uint8)
+    
+  return image_np
+
+
+
+def on_image_change(filepath):
+    # Extract the filename without extension
+    filename = os.path.splitext(os.path.basename(filepath))[0]
+    
+    # Check if the filename is "example1" or "example2"
+    if filename in ["example1", "example2", "example3", "example4"]:
+        meta_data_raw = load_state_from_file(f"assets/{filename}.pkl")
+        _, _, QT_raw = meta_data_raw
+
+        global num_inference_steps
+        num_inference_steps = len(QT_raw)
+        scale_value = 7
+        new_prompt = ""
+
+        if filename == "example1":
+            scale_value = 7
+            new_prompt = "a photo of a tree, summer, colourful"
+            
+        elif filename == "example2":
+            scale_value = 8
+            new_prompt = "a photo of a panda, two ears, white background"
+
+        elif filename == "example3":
+            scale_value = 7
+            new_prompt = "a realistic photo of a female warrior, flowing dark purple or black hair, bronze shoulder armour, leather chest piece, sky background with clouds"
+            
+        elif filename == "example4":
+            scale_value = 7 
+            new_prompt = "a photo of plastic bottle on some sand, beach background, sky background"
+
+        update_scale(scale_value)
+        img = apply_prompt(meta_data_raw, new_prompt)
+            
+    return filepath, img, meta_data_raw, num_inference_steps, scale_value, scale_value
+
+def update_value(value, key, res):
+    global weights
+    weights[key][res] = value
+
+def update_step(value):
+    global num_inference_steps
+    num_inference_steps = value
+
+def update_scale(scale):
+    values = [1.0] * 7
+
+    if scale == 9:
+        return values
+    
+    reduction_steps = (9 - scale) * 0.5
+    
+    for i in range(4):  # There are 4 positions to reduce symmetrically
+        if reduction_steps >= 1:
+            values[i] = 0.0
+            values[-(i + 1)] = 0.0
+            reduction_steps -= 1
+        elif reduction_steps > 0:
+            values[i] = 0.5
+            values[-(i + 1)] = 0.5
+            break
+
+    global weights
+    index = 0
+
+    for outer_key, inner_dict in weights.items():
+        for inner_key in inner_dict:
+            inner_dict[inner_key] = values[index]
+            index += 1
+    
+    return weights['down'][4096], weights['down'][1024], weights['down'][256], weights['mid'][64], weights['up'][256], weights['up'][1024], weights['up'][4096]
+            
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+            '''
+            <div style="text-align: center;">
+                <div style="display: flex; justify-content: center;">
+                    <img src="https://github.com/user-attachments/assets/55a38e74-ab93-4d80-91c8-0fa6130af45a" alt="Logo">
+                </div>
+                <h1>Out of Focus 1.0</h1>
+                <p style="font-size:16px;">Out of AI presents a flexible tool to manipulate your images. This is our first version of Image modification tool through prompt manipulation by reconstruction through diffusion inversion process</p>
+            </div>
+            <br>
+            <div style="display: flex; justify-content: center; align-items: center; text-align: center;">
+                <a href="https://www.buymeacoffee.com/outofai" target="_blank"><img src="https://img.shields.io/badge/-buy_me_a%C2%A0coffee-red?logo=buy-me-a-coffee" alt="Buy Me A Coffee"></a> &ensp;
+                <a href="https://twitter.com/OutofAi" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/cloudposse.svg?style=social&label=Ashleigh%20Watson"></a> &ensp;
+                <a href="https://twitter.com/banterless_ai" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/cloudposse.svg?style=social&label=Alex%20Nasa"></a>
+            </div>
+            '''
+        )
+    with gr.Row():
+      with gr.Column():
+
+          with gr.Row():
+            example_input = gr.Image(height=512, width=512, type="filepath", visible=False)
+            image_input = gr.Image(height=512, width=512, type="pil", label="Upload Source Image")
+          steps_slider = gr.Slider(minimum=5, maximum=25, step=5, value=num_inference_steps, label="Steps", info="Number of inference steps required to reconstruct and modify the image")
+          prompt_input = gr.Textbox(label="Prompt", info="Give an initial prompt in details, describing the image")
+          reconstruct_button = gr.Button("Reconstruct")
+          stop_button = gr.Button("Stop", variant="stop", interactive=False)
+      with gr.Column():
+        reconstructed_image = gr.Image(type="pil", label="Reconstructed")
+
+        with gr.Row():
+            invisible_slider = gr.Slider(minimum=0, maximum=9, step=1, value=7, visible=False)
+            interpolate_slider = gr.Slider(minimum=0, maximum=9, step=1, value=7, label="Cross-Attention Influence", info="Scales the related influence the source image has on the target image")
+        with gr.Row():  
+            new_prompt_input = gr.Textbox(label="New Prompt", interactive=False, info="Manipulate the image by changing the prompt or word addition at the end, achieve the best results by swapping words instead of adding or removing in between")
+        with gr.Row():
+            apply_button = gr.Button("Generate Vision", variant="primary", interactive=False)
+        with gr.Row():
+            with gr.Accordion(label="Advanced Options", open=False):
+                    gr.Markdown(
+                        '''
+                        <div style="text-align: center;">
+                            <h1>Weight Adjustment</h1>
+                            <p style="font-size:16px;">Specific Cross-Attention Influence weights can be manually modified for given resolutions (1.0 = Fully Source Attn 0.0 = Fully Target Attn)</p>
+                        </div>
+                        '''
+                    )
+                    down_slider_4096 = gr.Number(minimum=0.0, maximum=1.0, step=0.1, value=weights['down'][4096], label="Self-Attn Down 64x64")
+                    down_slider_1024 = gr.Number(minimum=0.0, maximum=1.0, step=0.1, value=weights['down'][1024], label="Self-Attn Down 32x32")
+                    down_slider_256 = gr.Number(minimum=0.0, maximum=1.0, step=0.1, value=weights['down'][256], label="Self-Attn Down 16x16")
+                    mid_slider_64 = gr.Number(minimum=0.0, maximum=1.0, step=0.1, value=weights['mid'][64], label="Self-Attn Mid 8x8")
+                    up_slider_256 = gr.Number(minimum=0.0, maximum=1.0, step=0.1, value=weights['up'][256], label="Self-Attn Up 16x16")
+                    up_slider_1024 = gr.Number(minimum=0.0, maximum=1.0, step=0.1, value=weights['up'][1024], label="Self-Attn Up 32x32")
+                    up_slider_4096 = gr.Number(minimum=0.0, maximum=1.0, step=0.1, value=weights['up'][4096], label="Self-Attn Up 64x64")
+
+        with gr.Row():
+            show_case = gr.Examples(
+                examples=[
+                    ["assets/example4.png", "a photo of plastic bottle on a rock, mountain background, sky background", "a photo of plastic bottle on some sand, beach background, sky background"],
+                    ["assets/example1.png", "a photo of a tree, spring, foggy", "a photo of a tree, summer, colourful"], 
+                    ["assets/example2.png", "a photo of a cat, two ears, white background", "a photo of a panda, two ears, white background"], 
+                    ["assets/example3.png", "a digital illustration of a female warrior, flowing dark purple or black hair, bronze shoulder armour, leather chest piece, sky background with clouds", "a realistic photo of a female warrior, flowing dark purple or black hair, bronze shoulder armour, leather chest piece, sky background with clouds"],
+                     
+                ],
+                inputs=[example_input, prompt_input, new_prompt_input],
+                label=None
+            )
+
+    meta_data = gr.State()
+
+    example_input.change(
+        fn=on_image_change,
+        inputs=example_input,
+        outputs=[image_input, reconstructed_image, meta_data, steps_slider, invisible_slider, interpolate_slider]
+    ).then(
+        lambda: gr.update(interactive=True),
+        outputs=apply_button
+    ).then(
+        lambda: gr.update(interactive=True),
+        outputs=new_prompt_input
+    )
+    steps_slider.release(update_step, inputs=steps_slider)
+    interpolate_slider.release(update_scale, inputs=interpolate_slider, outputs=[down_slider_4096, down_slider_1024, down_slider_256, mid_slider_64, up_slider_256, up_slider_1024, up_slider_4096 ])
+    invisible_slider.change(update_scale, inputs=invisible_slider, outputs=[down_slider_4096, down_slider_1024, down_slider_256, mid_slider_64, up_slider_256, up_slider_1024, up_slider_4096 ])
+
+    up_slider_4096.change(update_value, inputs=[up_slider_4096, gr.State('up'), gr.State(4096)])
+    up_slider_1024.change(update_value, inputs=[up_slider_1024, gr.State('up'), gr.State(1024)])
+    up_slider_256.change(update_value, inputs=[up_slider_256, gr.State('up'), gr.State(256)])
+
+    down_slider_4096.change(update_value, inputs=[down_slider_4096, gr.State('down'), gr.State(4096)])
+    down_slider_1024.change(update_value, inputs=[down_slider_1024, gr.State('down'), gr.State(1024)])
+    down_slider_256.change(update_value, inputs=[down_slider_256, gr.State('down'), gr.State(256)])
+
+    mid_slider_64.change(update_value, inputs=[mid_slider_64, gr.State('mid'), gr.State(64)])
+
+    reconstruct_button.click(reconstruct, inputs=[image_input, prompt_input], outputs=[reconstructed_image, new_prompt_input, meta_data]).then(
+        lambda: gr.update(interactive=True),
+        outputs=reconstruct_button
+    ).then(
+        lambda: gr.update(interactive=True),
+        outputs=new_prompt_input
+    ).then(
+        lambda: gr.update(interactive=True),
+        outputs=apply_button
+    ).then(
+        lambda: gr.update(interactive=False),
+        outputs=stop_button
+    )
+
+    reconstruct_button.click(
+        lambda: gr.update(interactive=False),
+        outputs=reconstruct_button
+    )
+
+    reconstruct_button.click(
+        lambda: gr.update(interactive=True),
+        outputs=stop_button
+    )
+
+    reconstruct_button.click(
+        lambda: gr.update(interactive=False),
+        outputs=apply_button
+    )
+
+    stop_button.click(
+        lambda: gr.update(interactive=False),
+        outputs=stop_button
+    )
+
+    apply_button.click(apply_prompt, inputs=[meta_data, new_prompt_input], outputs=reconstructed_image)
+    stop_button.click(stop_reconstruct)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--share", action="store_true")
+    args = parser.parse_args()
+    demo.queue()
+    demo.launch(share=args.share)

assets/example1.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cd481563fee5830919786d31895653b35b44a486beb11881fd13cf98e213c184
+size 3220274

assets/example2.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c2c26bd70e19685eb33b6514a5f26da4c2d3d69e306f60fba021beb390e86f36
+size 3220286

assets/example3.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e539a7ce84d036519fdef1dc6610c1de32cf70540ef96375915e457a74d8f25d
+size 3220392

assets/example4.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d91c3c54c5d987ea15365e3dc79e2df203751d80f1548020881de0e024d8ad9d
+size 3220316

requirements.txt

@@ -0,0 +1,9 @@
+diffusers
+transformers
+gradio
+accelerate
+
+--extra-index-url https://download.pytorch.org/whl/cu121
+torch
+torchvision
+torchaudio