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.gitattributes

@@ -33,3 +33,13 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+data/images/cabin.jpg filter=lfs diff=lfs merge=lfs -text
+data/images/fruit.jpg filter=lfs diff=lfs merge=lfs -text
+data/images/koala.jpg filter=lfs diff=lfs merge=lfs -text
+data/images/man_tree.jpg filter=lfs diff=lfs merge=lfs -text
+data/images/meditation.png filter=lfs diff=lfs merge=lfs -text
+data/images/old_couple.jpg filter=lfs diff=lfs merge=lfs -text
+data/images/statue.jpg filter=lfs diff=lfs merge=lfs -text
+data/images/steak.jpg filter=lfs diff=lfs merge=lfs -text
+data/images/woman_book.jpg filter=lfs diff=lfs merge=lfs -text
+images/main_figure.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,14 +1,68 @@
----
-title: ReFlex
-emoji: 📚
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.38.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: Text-Guided Editing of Real Images
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# ReFlex: Text-Guided Editing of Real Images in Rectified Flow via Mid-Step Feature Extraction and Attention Adaptation
+
+### [ICCV 2025] Official Pytorch implementation of the paper: "ReFlex: Text-Guided Editing of Real Images in Rectified Flow via Mid-Step Feature Extraction and Attention Adaptation" 
+by Jimyeon Kim, Jungwon Park, Yeji Song, Nojun Kwak, Wonjong Rhee†.
+
+Seoul National University
+
+[Arxiv](https://arxiv.org/abs/2507.01496)
+ 
+[Project Page](https://wlaud1001.github.io/ReFlex/)
+
+
+
+![main](./images/main_figure.png)
+
+## Setup
+```
+git clone https://github.com/wlaud1001/ReFlex.git
+cd ReFlex
+
+conda create -n reflex python=3.10
+conda activate reflex
+pip install -r requirements.txt
+```
+
+## Run
+
+### Run exmaple
+```
+python img_edit.py \
+    --gpu {gpu} \
+    --seed {seed} \
+    --img_path {source_img_path} \
+    --source_prompt {source_prompt} \
+    --target_prompt  {target_prompt} \
+    --results_dir {results_dir} \
+    --feature_steps {feature_steps} \
+    --attn_topk {attn_topk}
+```
+### Arguments
+- --gpu: Index of the GPU to use.
+- --seed: Random seed.
+- --img_path: Path to the input real image to be edited.
+- --mask_path (optional): Path to a ground-truth mask for local editing. 
+    - If provided, this mask is used directly. 
+    - If omitted, the editing mask is automatically generated from attention maps.
+- --source_prompt (optional): Text prompt describing the content of the input image.
+    - If provided, mask generation and latent blending will be applied.
+    - If omitted, editing proceeds without latent blending.
+- --target_prompt: Text prompt describing the desired edited image.
+- --blend_word (optional): Word in --source_prompt to guide mask generation via its I2T-CA map.
+    -  If omitted, the blend word is automatically inferred by comparing source_prompt and target_prompt.
+- --results_dir: Directory to save the output images
+### 
+
+### Scripts
+We also provide several example scripts in the (./scripts) directory for some use cases and reproducible experiments.
+#### Script Categories
+- scripts/wo_ca/: Cases where the source prompt is not given. I2T-CA adaptation and latent blending are not applied.
+- scripts/w_ca/: Cases where the source prompt is given, and the editing mask for latent blending is automatically generated from the attention map.
+- scripts/w_mask/: Cases where a ground-truth mask for local editing is provided and directly used for latent blending.
+
+You can run a script as follows:
+```
+./scripts/wo_ca/run_bear.sh
+./scripts/w_ca/run_bird.sh
+./scripts/w_mask/run_cat_hat.sh
+```

img_edit.py

@@ -0,0 +1,492 @@
+import argparse
+import gc
+import os
+import random
+import re
+import time
+from distutils.util import strtobool
+
+import pandas as pd
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--img_path",
+    type=str,
+)
+parser.add_argument(
+    "--target_prompt",
+    type=str,
+)
+parser.add_argument(
+    "--source_prompt",
+    type=str,
+    default=''
+)
+parser.add_argument(
+    "--blend_word",
+    type=str,
+    default=''
+)
+parser.add_argument(
+    "--mask_path",
+    type=str,
+    default=None
+)
+
+
+parser.add_argument(
+    "--gpu",
+    type=str,
+    default="0",
+)
+parser.add_argument(
+    "--seed",
+    type=int,
+    default=0
+)
+parser.add_argument(
+    "--results_dir",
+    type=str,
+    default='results'
+)
+
+
+parser.add_argument(
+    "--model",
+    type=str,
+    default='flux',
+    choices=['flux']
+)
+
+parser.add_argument(
+    "--ca_steps",
+    type=int,
+    default=10,
+    help="Number of steps to apply I2T-CA adaptation and injection.",
+)
+
+parser.add_argument(
+    "--sa_steps",
+    type=int,
+    default=7
+    help="Number of steps to apply I2I-SA adaptation and injection.",
+)
+
+parser.add_argument(
+    "--feature_steps",
+    type=int,
+    default=5
+    help="Number of steps to inject residual features.",
+)
+
+
+parser.add_argument(
+    "--ca_attn_layer_from",
+    type=int,
+    default=13,
+    help="Layers to apply I2T-CA adaptation and injection.",
+)
+parser.add_argument(
+    "--ca_attn_layer_to",
+    type=int,
+    default=45,
+    help="Layers to apply I2T-CA adaptation and injection.",
+)
+
+parser.add_argument(
+    "--sa_attn_layer_from",
+    type=int,
+    default=20,
+    help="Layers to apply I2I-SA adaptation and injection.",
+)
+parser.add_argument(
+    "--sa_attn_layer_to",
+    type=int,
+    default=45,
+    help="Layers to apply I2I-SA adaptation and injection.",
+)
+
+parser.add_argument(
+    "--feature_layer_from",
+    type=int,
+    default=13,
+    help="Layers to inject residual features.",
+)
+parser.add_argument(
+    "--feature_layer_to",
+    type=int,
+    default=20,
+    help="Layers to inject residual features.",
+)
+
+parser.add_argument(
+    "--flow_steps",
+    type=int,
+    default=7,
+    help="Steps to apply forward step before inversion",
+)
+parser.add_argument(
+    "--step_start",
+    type=int,
+    default=0
+)
+
+
+parser.add_argument(
+    "--num_inference_steps",
+    type=int,
+    default=28
+)
+parser.add_argument(
+    "--guidance_scale",
+    type=float,
+    default=3.5,
+)
+
+parser.add_argument(
+    "--attn_topk",
+    type=int,
+    default=20,
+    help="Hyperparameter for I2I-SA adaptaion."
+)
+
+parser.add_argument(
+    "--text_scale",
+    type=float,
+    default=4,
+    help="Hyperparameter for I2T-CA adaptaion."
+)
+
+parser.add_argument(
+    "--mid_step_index",
+    type=int,
+    default=14,
+    help="Hyperparameter for mid-step feature extraction."
+)
+
+
+parser.add_argument(
+    "--use_mask",
+    type=strtobool,
+    default=True
+)
+
+parser.add_argument(
+    "--use_ca_mask",
+    type=strtobool,
+    default=True
+)
+
+parser.add_argument(
+    "--mask_steps",
+    type=int,
+    default=18,
+    help="Steps to apply latent blending"
+)
+
+parser.add_argument(
+    "--mask_dilation",
+    type=int,
+    default=3
+)
+parser.add_argument(
+    "--mask_nbins",
+    type=int,
+    default=128
+)
+
+args = parser.parse_args()
+
+os.environ["CUDA_VISIBLE_DEVICES"] = f"{args.gpu}"
+
+import gc
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import yaml
+from diffusers import FlowMatchEulerDiscreteScheduler
+from diffusers.utils.torch_utils import randn_tensor
+from PIL import Image
+
+from src.attn_utils.attn_utils import AttentionAdapter, AttnCollector
+from src.attn_utils.flux_attn_processor import NewFluxAttnProcessor2_0
+from src.attn_utils.seq_aligner import get_refinement_mapper
+from src.callback.callback_fn import CallbackAll
+from src.inversion.inverse import get_inversed_latent_list
+from src.inversion.scheduling_flow_inverse import \
+    FlowMatchEulerDiscreteForwardScheduler
+from src.pipeline.flux_pipeline import NewFluxPipeline
+from src.transformer_utils.transformer_utils import (FeatureCollector,
+                                                     FeatureReplace)
+from src.utils import (find_token_id_differences, find_word_token_indices,
+                       get_flux_pipeline, mask_decode, mask_interpolate)
+
+
+def fix_seed(random_seed):
+    """
+    fix seed to control any randomness from a code 
+    (enable stability of the experiments' results.)
+    """
+    torch.manual_seed(random_seed)
+    torch.cuda.manual_seed(random_seed)
+    torch.cuda.manual_seed_all(random_seed)  # if use multi-GPU
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    np.random.seed(random_seed)
+    random.seed(random_seed)
+
+def main(args):
+    fix_seed(args.seed)
+    device = torch.device('cuda')
+
+    pipe = get_flux_pipeline(pipeline_class=NewFluxPipeline)
+    attn_proc = NewFluxAttnProcessor2_0
+    pipe = pipe.to(device)
+
+    layer_order = range(57)
+
+    ca_layer_list = layer_order[args.ca_attn_layer_from:args.ca_attn_layer_to]
+    sa_layer_list = layer_order[args.feature_layer_to:args.sa_attn_layer_to]
+    feature_layer_list = layer_order[args.feature_layer_from:args.feature_layer_to]
+
+    
+    img_path = args.img_path
+    source_img = Image.open(img_path).resize((1024, 1024)).convert("RGB")    
+    img_base_name = os.path.splitext(img_path)[0].split('/')[-1]
+    result_img_dir = f"{args.results_dir}/seed_{args.seed}/{args.target_prompt}"
+        
+    source_prompt = args.source_prompt
+    target_prompt = args.target_prompt
+    prompts = [source_prompt, target_prompt]
+
+    print(prompts)
+    mask = None
+
+    if args.use_mask:
+        use_mask = True
+
+        if args.mask_path is not None:
+            mask = Image.open(args.mask_path)
+            mask = torch.tensor(np.array(mask)).bool()
+            mask = mask.to(device)
+
+            # Increase the latent blending steps if the ground truth mask is used.
+            args.mask_steps = int(args.num_inference_steps * 0.9)
+            
+            source_ca_index = None
+            target_ca_index = None
+            use_ca_mask = False
+
+        elif args.use_ca_mask and source_prompt:
+            mask = None
+            if args.blend_word and args.blend_word in source_prompt:
+                editing_source_token_index = find_word_token_indices(source_prompt, args.blend_word, pipe.tokenizer_2)
+                editing_target_token_index = None
+            else:
+                editing_tokens_info = find_token_id_differences(*prompts, pipe.tokenizer_2)
+                editing_source_token_index = editing_tokens_info['prompt_1']['index']
+                editing_target_token_index = editing_tokens_info['prompt_2']['index']
+
+            use_ca_mask = True
+            if editing_source_token_index:
+                source_ca_index = editing_source_token_index
+                target_ca_index = None
+            elif editing_target_token_index:
+                source_ca_index = None
+                target_ca_index = editing_target_token_index
+            else:
+                source_ca_index = None
+                target_ca_index = None
+                use_ca_mask = False
+
+        else:
+            source_ca_index = None
+            target_ca_index = None
+            use_ca_mask = False
+
+    else:
+        use_mask = False
+        use_ca_mask = False
+        source_ca_index = None
+        target_ca_index = None
+
+    if source_prompt:
+        # Use I2T-CA injection
+        mappers, alphas = get_refinement_mapper(prompts, pipe.tokenizer_2, max_len=512)
+        mappers = mappers.to(device=device)
+        alphas = alphas.to(device=device, dtype=pipe.dtype)
+        alphas = alphas[:, None, None, :]
+
+        ca_steps = args.ca_steps
+        attn_adj_from = 1
+
+    else:
+        # Not use I2T-CA injection
+        mappers = None
+        alphas = None
+
+        ca_steps = 0
+        attn_adj_from=3
+
+    sa_steps = args.sa_steps
+    feature_steps = args.feature_steps
+
+    attn_controller = AttentionAdapter(
+        ca_layer_list=ca_layer_list,
+        sa_layer_list=sa_layer_list,
+        ca_steps=ca_steps,
+        sa_steps=sa_steps,
+        method='replace_topk',
+        topk=args.attn_topk,
+        text_scale=args.text_scale,
+        mappers=mappers,
+        alphas=alphas,
+        attn_adj_from=attn_adj_from,
+        save_source_ca=source_ca_index is not None,
+        save_target_ca=target_ca_index is not None,
+    )
+
+    attn_collector = AttnCollector(
+        transformer=pipe.transformer, 
+        controller=attn_controller, 
+        attn_processor_class=NewFluxAttnProcessor2_0, 
+    )
+
+    feature_controller = FeatureReplace(
+        layer_list=feature_layer_list,
+        feature_steps=feature_steps,
+    )
+
+    feature_collector = FeatureCollector(
+        transformer=pipe.transformer, 
+        controller=feature_controller,
+    )
+
+    num_prompts=len(prompts)
+    
+    shape = (1, 16, 128, 128)
+    generator = torch.Generator(device=device).manual_seed(args.seed)        
+    latents = randn_tensor(shape, device=device, generator=generator)
+    latents = pipe._pack_latents(latents, *latents.shape)
+    
+    attn_collector.restore_orig_attention()
+    feature_collector.restore_orig_transformer()
+
+    t0 = time.perf_counter()
+
+    inv_latents = get_inversed_latent_list(
+        pipe,
+        source_img,
+        random_noise=latents,
+        num_inference_steps=args.num_inference_steps,
+        backward_method="ode",
+        use_prompt_for_inversion=False,
+        guidance_scale_for_inversion=0,
+        prompt_for_inversion='',
+        flow_steps=args.flow_steps,
+    )
+
+    source_latents = inv_latents[::-1]
+    target_latents = inv_latents[::-1]
+
+    attn_collector.register_attention_control()
+    feature_collector.register_transformer_control()
+
+    callback_fn = CallbackAll(
+        latents=source_latents,
+        attn_collector=attn_collector, 
+        feature_collector=feature_collector, 
+        feature_inject_steps=feature_steps,
+        mid_step_index=args.mid_step_index,
+        step_start=args.step_start,
+        use_mask=use_mask,
+        use_ca_mask=use_ca_mask,
+        source_ca_index=source_ca_index,
+        target_ca_index=target_ca_index,
+        mask_kwargs={'dilation': args.mask_dilation},
+        mask_steps=args.mask_steps,
+        mask=mask,
+    )
+
+    init_latent = target_latents[args.step_start]
+    init_latent = init_latent.repeat(num_prompts, 1, 1)
+    init_latent[0] = source_latents[args.mid_step_index]
+    
+    os.makedirs(result_img_dir, exist_ok=True)
+    pipe.scheduler = FlowMatchEulerDiscreteForwardScheduler.from_config(
+        pipe.scheduler.config, 
+        step_start=args.step_start,
+        margin_index_from_image=0
+    )
+
+    attn_controller.reset()
+    feature_controller.reset()
+    attn_controller.text_scale = args.text_scale
+    attn_controller.cur_step = args.step_start
+    feature_controller.cur_step = args.step_start
+
+    with torch.no_grad():
+        images = pipe(
+            prompts,               
+            latents=init_latent,
+            num_images_per_prompt=1,
+            guidance_scale=args.guidance_scale,
+            num_inference_steps=args.num_inference_steps,
+            generator=generator,
+            callback_on_step_end=callback_fn,
+            mid_step_index=args.mid_step_index,
+            step_start=args.step_start,
+            callback_on_step_end_tensor_inputs=['latents'],
+        ).images
+    
+    t1 = time.perf_counter()
+    print(f"Done in {t1 - t0:.1f}s.")
+
+    source_img_path = os.path.join(result_img_dir, f"source.png")
+    source_img.save(source_img_path)
+    
+    for i, img in enumerate(images[1:]):
+        target_img_path = os.path.join(result_img_dir, f"target_{i}.png")
+        img.save(target_img_path)
+
+    target_text_path = os.path.join(result_img_dir, f"target_prompts.txt")
+    with open(target_text_path, 'w') as file:
+        file.write(target_prompt + '\n')
+    
+    source_text_path = os.path.join(result_img_dir, f"source_prompt.txt")
+    with open(source_text_path, 'w') as file:
+        file.write(source_prompt + '\n')
+
+    images = [source_img] + images
+
+    fs=3
+    n = len(images)
+    fig, ax = plt.subplots(1, n, figsize=(n*fs, 1*fs))
+
+    for i, img in enumerate(images):
+        ax[i].imshow(img)
+
+    ax[0].set_title('source')
+    ax[1].set_title(source_prompt, fontsize=7)
+    ax[2].set_title(target_prompt, fontsize=7)
+
+    overall_img_path = os.path.join(result_img_dir, f"overall.png")
+    plt.savefig(overall_img_path, bbox_inches='tight')
+    plt.close()
+    
+    mask_save_dir = os.path.join(result_img_dir, f"mask")
+    os.makedirs(mask_save_dir, exist_ok=True)
+
+    if use_ca_mask:
+        ca_mask_path = os.path.join(mask_save_dir, f"mask_ca.png")
+        mask_img = Image.fromarray((callback_fn.mask.cpu().float().numpy() * 255).astype(np.uint8)).convert('L')
+        mask_img.save(ca_mask_path)
+
+    del inv_latents
+    del init_latent
+    gc.collect()
+    torch.cuda.empty_cache()
+
+if __name__ == '__main__':
+    main(args)

requirements.txt

@@ -0,0 +1,12 @@
+diffusers==0.31.0
+torch==2.4.1
+pandas
+matplotlib
+transformers==4.44.2
+torchao
+torchvision
+opencv-python
+scikit-image
+accelerate
+sentencepiece
+protobuf

scripts/w_ca/run_bird.sh

@@ -0,0 +1,20 @@
+source_prompt='a blue and white bird sits on a branch'
+target_prompt='a blue and white butterfly sits on a branch'
+
+ca_steps=10
+sa_steps=7
+feature_steps=5
+
+attn_topk=20
+
+python img_edit.py \
+    --gpu 3 \
+    --seed 0 \
+    --img_path 'data/images/bird.jpg' \
+    --source_prompt "$source_prompt" \
+    --target_prompt  "$target_prompt" \
+    --results_dir 'results/bird' \
+    --ca_steps $ca_steps \
+    --sa_steps $sa_steps \
+    --feature_steps $feature_steps \
+    --attn_topk $attn_topk

scripts/w_ca/run_cabin.sh

@@ -0,0 +1,20 @@
+source_prompt='a painting of a cabin in the snow with mountains in the background'
+target_prompt='a painting of a car in the snow with mountains in the background'
+
+ca_steps=10
+sa_steps=7
+feature_steps=5
+
+attn_topk=40
+
+python img_edit.py \
+    --gpu 3 \
+    --seed 0 \
+    --img_path 'data/images/cabin.jpg' \
+    --source_prompt "$source_prompt" \
+    --target_prompt  "$target_prompt" \
+    --results_dir 'results/cabin' \
+    --ca_steps $ca_steps \
+    --sa_steps $sa_steps \
+    --feature_steps $feature_steps \
+    --attn_topk $attn_topk 

scripts/w_ca/run_car.sh

@@ -0,0 +1,21 @@
+source_prompt='a sports car driving down the street'
+target_prompt='stained glass window of a sports car driving down the street'
+
+ca_steps=10
+sa_steps=7
+feature_steps=5
+
+attn_topk=10
+
+python img_edit.py \
+    --gpu 1 \
+    --seed 0 \
+    --img_path 'data/images/car.jpg' \
+    --source_prompt "$source_prompt" \
+    --target_prompt  "$target_prompt" \
+    --results_dir 'results/car' \
+    --ca_steps $ca_steps \
+    --sa_steps $sa_steps \
+    --feature_steps $feature_steps \
+    --use_mask 0 \
+    --attn_topk $attn_topk

scripts/w_ca/run_cat_poly.sh

@@ -0,0 +1,21 @@
+source_prompt='a cat is shown in a low polygonal style'
+target_prompt='a fox is shown in a low polygonal style'
+
+ca_steps=10
+sa_steps=7
+feature_steps=5
+
+attn_topk=20
+
+python img_edit.py \
+    --gpu 1 \
+    --seed 0 \
+    --img_path 'data/images/cat_poly.jpg' \
+    --source_prompt "$source_prompt" \
+    --target_prompt  "$target_prompt" \
+    --results_dir 'results/cat_poly' \
+    --ca_steps $ca_steps \
+    --sa_steps $sa_steps \
+    --feature_steps $feature_steps \
+    --attn_topk $attn_topk
+    

scripts/w_ca/run_flower.sh

@@ -0,0 +1,21 @@
+source_prompt='a pink flower with yellow center in the middle'
+target_prompt='a blue flower with red center in the middle'
+
+ca_steps=10
+sa_steps=7
+feature_steps=5
+
+attn_topk=20
+
+python img_edit.py \
+    --gpu 1 \
+    --seed 0 \
+    --img_path 'data/images/flower.jpg' \
+    --source_prompt "$source_prompt" \
+    --target_prompt  "$target_prompt" \
+    --results_dir 'results/flower' \
+    --ca_steps $ca_steps \
+    --sa_steps $sa_steps \
+    --feature_steps $feature_steps \
+    --attn_topk $attn_topk \
+    --blend_word 'flower'

scripts/w_ca/run_fruit.sh

@@ -0,0 +1,20 @@
+source_prompt='white plate with fruits on it'
+target_prompt='white plate with pizza on it'
+
+ca_steps=10
+sa_steps=7
+feature_steps=5
+
+attn_topk=40
+
+python img_edit.py \
+    --gpu 0 \
+    --seed 0 \
+    --img_path 'data/images/fruit.jpg' \
+    --source_prompt "$source_prompt" \
+    --target_prompt  "$target_prompt" \
+    --results_dir 'results/fruit' \
+    --ca_steps $ca_steps \
+    --sa_steps $sa_steps \
+    --feature_steps $feature_steps \
+    --attn_topk $attn_topk 

scripts/w_ca/run_koala.sh

@@ -0,0 +1,20 @@
+source_prompt='a koala is sitting on a tree'
+target_prompt='a koala and a bird is sitting on a tree'
+
+ca_steps=10
+sa_steps=7
+feature_steps=5
+
+attn_topk=40
+
+python img_edit.py \
+    --gpu 3 \
+    --seed 0 \
+    --img_path 'data/images/koala.jpg' \
+    --source_prompt "$source_prompt" \
+    --target_prompt  "$target_prompt" \
+    --results_dir 'results/koala' \
+    --ca_steps $ca_steps \
+    --sa_steps $sa_steps \
+    --feature_steps $feature_steps \
+    --attn_topk $attn_topk

scripts/w_ca/run_owl_heart.sh

@@ -0,0 +1,20 @@
+source_prompt='a cartoon painting of a cute owl with a heart on its body'
+target_prompt='a cartoon painting of a cute owl with a circle on its body'
+
+ca_steps=10
+sa_steps=7
+feature_steps=5
+
+attn_topk=20
+
+python img_edit.py \
+    --gpu 1 \
+    --seed 0 \
+    --img_path 'data/images/owl_heart.jpg' \
+    --source_prompt "$source_prompt" \
+    --target_prompt  "$target_prompt" \
+    --results_dir 'results/owl_heart' \
+    --ca_steps $ca_steps \
+    --sa_steps $sa_steps \
+    --feature_steps $feature_steps \
+    --attn_topk $attn_topk

scripts/w_ca/run_statue.sh

@@ -0,0 +1,21 @@
+source_prompt='photo of a statue in front view'
+target_prompt='photo of a statue in side view'
+
+ca_steps=10
+sa_steps=7
+feature_steps=5
+
+attn_topk=60
+
+python img_edit.py \
+    --gpu 0 \
+    --seed 0 \
+    --img_path 'data/images/statue.jpg' \
+    --source_prompt "$source_prompt" \
+    --target_prompt  "$target_prompt" \
+    --results_dir 'results/statue' \
+    --ca_steps $ca_steps \
+    --sa_steps $sa_steps \
+    --feature_steps $feature_steps \
+    --attn_topk $attn_topk \
+    --blend_word 'statue'

scripts/w_ca/run_steak.sh

@@ -0,0 +1,20 @@
+source_prompt='a plate with steak on it'
+target_prompt='a plate with salmon on it'
+
+ca_steps=10
+sa_steps=7
+feature_steps=5
+
+attn_topk=40
+
+python img_edit.py \
+    --gpu 0 \
+    --seed 0 \
+    --img_path 'data/images/steak.jpg' \
+    --source_prompt "$source_prompt" \
+    --target_prompt  "$target_prompt" \
+    --results_dir 'results/steak' \
+    --ca_steps $ca_steps \
+    --sa_steps $sa_steps \
+    --feature_steps $feature_steps \
+    --attn_topk $attn_topk

scripts/w_ca/run_tennis.sh

@@ -0,0 +1,21 @@
+source_prompt='a woman in a black tank top and pink shorts is about to hit a tennis ball'
+target_prompt='a iron woman robot in a black tank top and pink shorts is about to hit a tennis ball'
+
+ca_steps=10
+sa_steps=7
+feature_steps=5
+
+attn_topk=20
+
+python img_edit.py \
+    --gpu 0 \
+    --seed 0 \
+    --img_path 'data/images/tennis.jpg' \
+    --source_prompt "$source_prompt" \
+    --target_prompt  "$target_prompt" \
+    --results_dir 'results/tennis' \
+    --ca_steps $ca_steps \
+    --sa_steps $sa_steps \
+    --feature_steps $feature_steps \
+    --attn_topk $attn_topk \
+    --blend_word 'woman'

scripts/w_ca/run_woman_book.sh

@@ -0,0 +1,20 @@
+source_prompt='a woman sitting in the grass with a book'
+target_prompt='a woman sitting in the grass with a laptop'
+
+ca_steps=10
+sa_steps=7
+feature_steps=5
+
+attn_topk=20
+
+python img_edit.py \
+    --gpu 1 \
+    --seed 0 \
+    --img_path 'data/images/woman_book.jpg' \
+    --source_prompt "$source_prompt" \
+    --target_prompt  "$target_prompt" \
+    --results_dir 'results/woman_book' \
+    --ca_steps $ca_steps \
+    --sa_steps $sa_steps \
+    --feature_steps $feature_steps \
+    --attn_topk $attn_topk

scripts/w_mask/run_cat_hat.sh

@@ -0,0 +1,21 @@
+source_prompt='a cat wearing a pink hat'
+target_prompt='a tiger wearing a pink hat'
+
+ca_steps=10
+sa_steps=7
+feature_steps=5
+
+attn_topk=20
+
+python img_edit.py \
+    --gpu 3 \
+    --seed 0 \
+    --img_path 'data/images/cat_hat.jpg' \
+    --mask_path 'data/masks/cat_hat.jpg' \
+    --source_prompt "$source_prompt" \
+    --target_prompt  "$target_prompt" \
+    --results_dir 'results/cat_hat' \
+    --ca_steps $ca_steps \
+    --sa_steps $sa_steps \
+    --feature_steps $feature_steps \
+    --attn_topk $attn_topk