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app.py

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+import os
+import sys
+import cv2
+import numpy as np
+import torch
+import gradio as gr
+from PIL import Image, ImageFilter, ImageDraw
+from huggingface_hub import snapshot_download
+from diffusers import FluxFillPipeline, FluxPriorReduxPipeline
+import math
+from utils.utils import get_bbox_from_mask, expand_bbox, pad_to_square, box2squre, crop_back, expand_image_mask
+
+
+
+snapshot_download(repo_id="black-forest-labs/FLUX.1-Fill-dev", local_dir="./FLUX.1-Fill-dev")
+snapshot_download(repo_id="black-forest-labs/FLUX.1-Redux-dev", local_dir="./FLUX.1-Redux-dev")
+snapshot_download(repo_id="123123aa123/insertanything_model", local_dir="./insertanything_model")
+
+
+dtype = torch.bfloat16
+size = (768, 768)
+
+pipe = FluxFillPipeline.from_pretrained(
+    "./FLUX.1-Fill-dev",
+    torch_dtype=dtype
+).to("cuda")
+
+pipe.load_lora_weights(
+    "./insertanything_model/20250321-082022_steps5000_pytorch_lora_weights.safetensors"
+)
+
+
+redux = FluxPriorReduxPipeline.from_pretrained("./FLUX.1-Redux-dev").to(dtype=dtype).to("cuda")
+
+
+
+###   example  #####
+ref_dir='./examples/ref_image'
+ref_mask_dir='./examples/ref_mask'
+image_dir='./examples/source_image'
+image_mask_dir='./examples/source_mask'
+
+ref_list=[os.path.join(ref_dir,file) for file in os.listdir(ref_dir) if '.jpg' in file or '.png' in file or '.jpeg' in file ]
+ref_list.sort()
+
+ref_mask_list=[os.path.join(ref_mask_dir,file) for file in os.listdir(ref_mask_dir) if '.jpg' in file or '.png' in file or '.jpeg' in file]
+ref_mask_list.sort()
+
+image_list=[os.path.join(image_dir,file) for file in os.listdir(image_dir) if '.jpg' in file or '.png' in file or '.jpeg' in file ]
+image_list.sort()
+
+image_mask_list=[os.path.join(image_mask_dir,file) for file in os.listdir(image_mask_dir) if '.jpg' in file or '.png' in file or '.jpeg' in file]
+image_mask_list.sort()
+###   example  #####
+
+
+
+
+def run_local(base_image, base_mask, reference_image, ref_mask, seed, base_mask_option, ref_mask_option):
+
+    if base_mask_option == "Draw Mask":
+        tar_image = base_image["image"]
+        tar_mask = base_image["mask"]
+    else:
+        tar_image = base_image["image"]
+        tar_mask = base_mask
+
+    if ref_mask_option == "Draw Mask":
+        ref_image = reference_image["image"]
+        ref_mask = reference_image["mask"]
+    else:
+        ref_image = reference_image["image"]
+        ref_mask = ref_mask
+
+
+    tar_image = tar_image.convert("RGB")
+    tar_mask = tar_mask.convert("L")
+    ref_image = ref_image.convert("RGB")
+    ref_mask = ref_mask.convert("L")
+
+    tar_image = np.asarray(tar_image)
+    tar_mask = np.asarray(tar_mask)
+    tar_mask = np.where(tar_mask > 128, 1, 0).astype(np.uint8)
+
+    ref_image = np.asarray(ref_image)
+    ref_mask = np.asarray(ref_mask)
+    ref_mask = np.where(ref_mask > 128, 1, 0).astype(np.uint8)
+
+
+    ref_box_yyxx = get_bbox_from_mask(ref_mask)
+    ref_mask_3 = np.stack([ref_mask,ref_mask,ref_mask],-1)
+    masked_ref_image = ref_image * ref_mask_3 + np.ones_like(ref_image) * 255 * (1-ref_mask_3) 
+    y1,y2,x1,x2 = ref_box_yyxx
+    masked_ref_image = masked_ref_image[y1:y2,x1:x2,:] 
+    ref_mask = ref_mask[y1:y2,x1:x2] 
+    ratio = 1.3
+    masked_ref_image, ref_mask = expand_image_mask(masked_ref_image, ref_mask, ratio=ratio) 
+
+
+    masked_ref_image = pad_to_square(masked_ref_image, pad_value = 255, random = False) 
+
+    kernel = np.ones((7, 7), np.uint8)
+    iterations = 2
+    tar_mask = cv2.dilate(tar_mask, kernel, iterations=iterations)
+
+    # zome in
+    tar_box_yyxx = get_bbox_from_mask(tar_mask)
+    tar_box_yyxx = expand_bbox(tar_mask, tar_box_yyxx, ratio=1.2)
+
+    tar_box_yyxx_crop =  expand_bbox(tar_image, tar_box_yyxx, ratio=2)    #1.2 1.6
+    tar_box_yyxx_crop = box2squre(tar_image, tar_box_yyxx_crop) # crop box
+    y1,y2,x1,x2 = tar_box_yyxx_crop
+
+
+    old_tar_image = tar_image.copy()
+    tar_image = tar_image[y1:y2,x1:x2,:]
+    tar_mask = tar_mask[y1:y2,x1:x2]
+
+    H1, W1 = tar_image.shape[0], tar_image.shape[1]
+    # zome in
+
+
+    tar_mask = pad_to_square(tar_mask, pad_value=0)
+    tar_mask = cv2.resize(tar_mask, size)
+
+    masked_ref_image = cv2.resize(masked_ref_image.astype(np.uint8), size).astype(np.uint8)
+    pipe_prior_output = redux(Image.fromarray(masked_ref_image))
+
+
+    tar_image = pad_to_square(tar_image, pad_value=255)
+
+    H2, W2 = tar_image.shape[0], tar_image.shape[1]
+
+    tar_image = cv2.resize(tar_image, size)
+    diptych_ref_tar = np.concatenate([masked_ref_image, tar_image], axis=1)
+
+
+    tar_mask = np.stack([tar_mask,tar_mask,tar_mask],-1)
+    mask_black = np.ones_like(tar_image) * 0
+    mask_diptych = np.concatenate([mask_black, tar_mask], axis=1)
+
+
+    diptych_ref_tar = Image.fromarray(diptych_ref_tar)
+    mask_diptych[mask_diptych == 1] = 255
+    mask_diptych = Image.fromarray(mask_diptych)
+
+
+
+    generator = torch.Generator("cuda").manual_seed(seed)
+    edited_image = pipe(
+        image=diptych_ref_tar,
+        mask_image=mask_diptych,
+        height=mask_diptych.size[1],
+        width=mask_diptych.size[0],
+        max_sequence_length=512,
+        generator=generator,
+        **pipe_prior_output, 
+    ).images[0]
+
+
+
+    width, height = edited_image.size
+    left = width // 2
+    right = width
+    top = 0
+    bottom = height
+    edited_image = edited_image.crop((left, top, right, bottom))
+
+
+    edited_image = np.array(edited_image)
+    edited_image = crop_back(edited_image, old_tar_image, np.array([H1, W1, H2, W2]), np.array(tar_box_yyxx_crop)) 
+    edited_image = Image.fromarray(edited_image)
+
+
+    return [edited_image]
+
+def update_ui(option):
+    if option == "Draw Mask":
+        return gr.update(visible=False), gr.update(visible=True)
+    else:
+        return gr.update(visible=True), gr.update(visible=False)
+
+
+with gr.Blocks() as demo:
+
+
+    gr.Markdown("#  Play with InsertAnything to Insert your Target Objects! ")
+    gr.Markdown("# Upload / Draw Images for the Background (up) and Reference Object (down)")
+    gr.Markdown("### Draw mask on the background or just upload the mask.")
+    gr.Markdown("### Only select one of these two methods. Don't forget to click the corresponding button!!")
+
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                base_image = gr.Image(label="Background Image", source="upload", tool="sketch", type="pil",
+                                        brush_color='#FFFFFF', mask_opacity=0.5)
+
+                base_mask = gr.Image(label="Background Mask", source="upload", type="pil")
+
+            with gr.Row():
+                base_mask_option = gr.Radio(["Draw Mask", "Upload with Mask"], label="Background Mask Input Option", value="Upload with Mask")
+
+            with gr.Row():
+                ref_image = gr.Image(label="Reference Image", source="upload", tool="sketch", type="pil", 
+                                    brush_color='#FFFFFF', mask_opacity=0.5)
+                                    
+                ref_mask = gr.Image(label="Reference Mask", source="upload", type="pil")
+
+            with gr.Row():
+                ref_mask_option = gr.Radio(["Draw Mask", "Upload with Mask"], label="Reference Mask Input Option", value="Upload with Mask")
+
+            baseline_gallery = gr.Gallery(label='Output', show_label=True, elem_id="gallery", height=512, columns=1)
+            with gr.Accordion("Advanced Option", open=True):
+                seed = gr.Slider(label="Seed", minimum=-1, maximum=999999999, step=1, value=666)
+                gr.Markdown("### Guidelines")
+                gr.Markdown(" Users can try using different seeds. For example, seeds like 42 and 123456 may produce different effects.")
+
+    run_local_button = gr.Button(value="Run")
+
+
+    # #### example #####
+    num_examples = len(image_list)
+    for i in range(num_examples):
+        with gr.Row():
+            if i == 0:
+                gr.Examples([image_list[i]], inputs=[base_image], label="Examples - Background Image", examples_per_page=1)
+                gr.Examples([image_mask_list[i]], inputs=[base_mask], label="Examples - Background Mask", examples_per_page=1)
+                gr.Examples([ref_list[i]], inputs=[ref_image], label="Examples - Reference Object", examples_per_page=1)
+                gr.Examples([ref_mask_list[i]], inputs=[ref_mask], label="Examples - Reference Mask", examples_per_page=1)
+            else:
+                gr.Examples([image_list[i]], inputs=[base_image], examples_per_page=1, label="")
+                gr.Examples([image_mask_list[i]], inputs=[base_mask], examples_per_page=1, label="")
+                gr.Examples([ref_list[i]], inputs=[ref_image], examples_per_page=1, label="")
+                gr.Examples([ref_mask_list[i]], inputs=[ref_mask], examples_per_page=1, label="")
+        if i < num_examples - 1:
+            with gr.Row():
+                gr.HTML("<hr>")
+    # #### example #####
+        
+    run_local_button.click(fn=run_local, 
+                           inputs=[base_image, base_mask, ref_image, ref_mask, seed, base_mask_option, ref_mask_option], 
+                           outputs=[baseline_gallery]
+                        )
+demo.launch()

requirements.txt

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+torch==2.5.1
+torchvision==0.20.1
+diffusers==0.32.2
+transformers==4.50.3
+peft==0.15.1
+opencv-python
+protobuf
+sentencepiece
+gradio==3.39.0
+bezier
+lightning==2.5.1
+datasets
+prodigyopt
+einops
+scipy