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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
hf_token = os.getenv("HF_TOKEN")
snapshot_download(repo_id="black-forest-labs/FLUX.1-Fill-dev", local_dir="./FLUX.1-Fill-dev", token=hf_token)
snapshot_download(repo_id="black-forest-labs/FLUX.1-Redux-dev", local_dir="./FLUX.1-Redux-dev", token=hf_token)
snapshot_download(repo_id="WensongSong/Insert-Anything", local_dir="./insertanything_model", token=hf_token)
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() |