app.py

# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# Copyright 2024 Black Forest Labs and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# STEP 1: Very first thing in the file: force spawn
import multiprocessing as mp
mp.set_start_method("spawn", force=True)

import spaces

import tempfile
from PIL import Image
import gradio as gr
import string
import random, time, math   
import os


os.environ["NCCL_P2P_DISABLE"]="1"
os.environ["NCCL_IB_DISABLE"]="1"

import src.flux.generate
from src.flux.generate import generate_from_test_sample, seed_everything
from src.flux.pipeline_tools import CustomFluxPipeline, load_modulation_adapter, load_dit_lora
from src.utils.data_utils import get_train_config, image_grid, pil2tensor, json_dump, pad_to_square, cv2pil, merge_bboxes
from eval.tools.face_id import FaceID
from eval.tools.florence_sam import ObjectDetector
import shutil
import yaml
import numpy as np
from huggingface_hub import snapshot_download, hf_hub_download
import torch

# FLUX.1-dev
snapshot_download(
    repo_id="black-forest-labs/FLUX.1-dev",
    local_dir="./checkpoints/FLUX.1-dev",
    local_dir_use_symlinks=False
)

# Florence-2-large
snapshot_download(
    repo_id="microsoft/Florence-2-large",
    local_dir="./checkpoints/Florence-2-large",
    local_dir_use_symlinks=False
)

# CLIP ViT Large
snapshot_download(
    repo_id="openai/clip-vit-large-patch14",
    local_dir="./checkpoints/clip-vit-large-patch14",
    local_dir_use_symlinks=False
)

# DINO ViT-s16
snapshot_download(
    repo_id="facebook/dino-vits16",
    local_dir="./checkpoints/dino-vits16",
    local_dir_use_symlinks=False
)

# mPLUG Visual Question Answering
snapshot_download(
    repo_id="xingjianleng/mplug_visual-question-answering_coco_large_en",
    local_dir="./checkpoints/mplug_visual-question-answering_coco_large_en",
    local_dir_use_symlinks=False
)

# XVerse
snapshot_download(
    repo_id="ByteDance/XVerse",
    local_dir="./checkpoints/XVerse",
    local_dir_use_symlinks=False
)

hf_hub_download(
    repo_id="facebook/sam2.1-hiera-large",
    local_dir="./checkpoints/",
    filename="sam2.1_hiera_large.pt",
)



os.environ["FLORENCE2_MODEL_PATH"]    = "./checkpoints/Florence-2-large"
os.environ["SAM2_MODEL_PATH"]         = "./checkpoints/sam2.1_hiera_large.pt"
os.environ["FACE_ID_MODEL_PATH"]      = "./checkpoints/model_ir_se50.pth"
os.environ["CLIP_MODEL_PATH"]         = "./checkpoints/clip-vit-large-patch14"
os.environ["FLUX_MODEL_PATH"]         = "./checkpoints/FLUX.1-dev"
os.environ["DPG_VQA_MODEL_PATH"]      = "./checkpoints/mplug_visual-question-answering_coco_large_en"
os.environ["DINO_MODEL_PATH"]         = "./checkpoints/dino-vits16"

dtype = torch.bfloat16
device = "cuda"

config_path = "train/config/XVerse_config_demo.yaml"

config = config_train = get_train_config(config_path)
# config["model"]["dit_quant"] = "int8-quanto"
config["model"]["use_dit_lora"] = False
model = CustomFluxPipeline(
    config, device, torch_dtype=dtype,
)
model.pipe.set_progress_bar_config(leave=False)

face_model = FaceID(device)
detector = ObjectDetector(device)

config = get_train_config(config_path)
model.config = config

run_mode = "mod_only" # orig_only, mod_only, both
store_attn_map = False
run_name = time.strftime("%m%d-%H%M")

num_inputs = 6

ckpt_root = "./checkpoints/XVerse"
model.clear_modulation_adapters()
model.pipe.unload_lora_weights()
if not os.path.exists(ckpt_root):
    print("Checkpoint root does not exist.")

modulation_adapter = load_modulation_adapter(model, config, dtype, device, f"{ckpt_root}/modulation_adapter", is_training=False)
model.add_modulation_adapter(modulation_adapter)
if config["model"]["use_dit_lora"]:
    load_dit_lora(model, model.pipe, config, dtype, device, f"{ckpt_root}", is_training=False)

vae_skip_iter = None
attn_skip_iter = 0


def clear_images():
    return [None, ]*num_inputs

@spaces.GPU()
def det_seg_img(image, label):
    if isinstance(image, str):
        image = Image.open(image).convert("RGB")
    instance_result_dict = detector.get_multiple_instances(image, label, min_size=image.size[0]//20)
    indices = list(range(len(instance_result_dict["instance_images"])))
    ins, bbox = merge_instances(image, indices, instance_result_dict["instance_bboxes"], instance_result_dict["instance_images"])
    return ins

@spaces.GPU()
def crop_face_img(image):
    if isinstance(image, str):
        image = Image.open(image).convert("RGB")

    # image = resize_keep_aspect_ratio(image, 1024)
    image = pad_to_square(image).resize((2048, 2048))
    
    face_bbox = face_model.detect(
        (pil2tensor(image).unsqueeze(0) * 255).to(torch.uint8).to(device), 1.4
    )[0]
    face = image.crop(face_bbox)
    return face

@spaces.GPU()
def vlm_img_caption(image):
    if isinstance(image, str):
        image = Image.open(image).convert("RGB")
    
    try:
        caption = detector.detector.caption(image, "<CAPTION>").strip()
        if caption.endswith("."):
            caption = caption[:-1]

    except Exception as e:
        print(e)
        caption = ""
    
    caption = caption.lower()
    return caption


def generate_random_string(length=4):
    letters = string.ascii_letters  # 包含大小写字母的字符串
    result_str = ''.join(random.choice(letters) for i in range(length))
    return result_str

def resize_keep_aspect_ratio(pil_image, target_size=1024):
    H, W = pil_image.height, pil_image.width
    target_area = target_size * target_size
    current_area = H * W
    scaling_factor = (target_area / current_area) ** 0.5  # sqrt(target_area / current_area)
    new_H = int(round(H * scaling_factor))
    new_W = int(round(W * scaling_factor))
    return pil_image.resize((new_W, new_H))

@spaces.GPU()
def generate_image(
    prompt, 
    cond_size, target_height, target_width, 
    seed, 
    vae_skip_iter, control_weight_lambda,
    double_attention,  # 新增参数
    single_attention,  # 新增参数
    ip_scale,
    latent_sblora_scale_str, vae_lora_scale,
    indexs,  # 新增参数
    # *images_captions_faces,  # Combine all unpacked arguments into one tuple
):
    torch.cuda.empty_cache()
    num_images = 1

    # Determine the number of images, captions, and faces based on the indexs length
    images = list(images_captions_faces[:num_inputs])
    captions = list(images_captions_faces[num_inputs:2 * num_inputs])
    idips_checkboxes = list(images_captions_faces[2 * num_inputs:3 * num_inputs])
    images = [images[i] for i in indexs]
    captions = [captions[i] for i in indexs]
    idips_checkboxes = [idips_checkboxes[i] for i in indexs]

    print(f"Length of images: {len(images)}")
    print(f"Length of captions: {len(captions)}")
    print(f"Indexs: {indexs}")
    
    print(f"Control weight lambda: {control_weight_lambda}")
    if control_weight_lambda != "no":
        parts = control_weight_lambda.split(',')
        new_parts = []
        for part in parts:
            if ':' in part:
                left, right = part.split(':')
                values = right.split('/')
                # 保存整体值
                global_value = values[0]
                id_value = values[1]
                ip_value = values[2]
                new_values = [global_value]
                for is_id in idips_checkboxes:
                    if is_id:
                        new_values.append(id_value)
                    else:
                        new_values.append(ip_value)
                new_part = f"{left}:{('/'.join(new_values))}"
                new_parts.append(new_part)
            else:
                new_parts.append(part)
        control_weight_lambda = ','.join(new_parts)
    
    print(f"Control weight lambda: {control_weight_lambda}")

    src_inputs = []
    use_words = []
    cur_run_time = time.strftime("%m%d-%H%M%S")
    tmp_dir_root = f"tmp/gradio_demo/{run_name}"
    temp_dir = f"{tmp_dir_root}/{cur_run_time}_{generate_random_string(4)}"
    os.makedirs(temp_dir, exist_ok=True)
    print(f"Temporary directory created: {temp_dir}")
    for i, (image_path, caption) in enumerate(zip(images, captions)):
        if image_path:
            if caption.startswith("a ") or caption.startswith("A "):
                word = caption[2:]
            else:
                word = caption
            
            if f"ENT{i+1}" in prompt:
                prompt = prompt.replace(f"ENT{i+1}", caption)
            
            image = resize_keep_aspect_ratio(Image.open(image_path), 768)
            save_path = f"{temp_dir}/tmp_resized_input_{i}.png"
            image.save(save_path)
            
            input_image_path = save_path

            src_inputs.append(
                {
                    "image_path": input_image_path,
                    "caption": caption
                }
            )
            use_words.append((i, word, word))


    test_sample = dict(
        input_images=[], position_delta=[0, -32], 
        prompt=prompt,
        target_height=target_height,
        target_width=target_width,
        seed=seed,
        cond_size=cond_size,
        vae_skip_iter=vae_skip_iter,
        lora_scale=ip_scale,
        control_weight_lambda=control_weight_lambda,
        latent_sblora_scale=latent_sblora_scale_str,
        condition_sblora_scale=vae_lora_scale,
        double_attention=double_attention,
        single_attention=single_attention,
    )
    if len(src_inputs) > 0:
        test_sample["modulation"] = [
            dict(
                type="adapter",
                src_inputs=src_inputs,
                use_words=use_words,
            ),
        ]
    
    json_dump(test_sample, f"{temp_dir}/test_sample.json", 'utf-8')
    assert single_attention == True
    target_size = int(round((target_width * target_height) ** 0.5) // 16 * 16)
    print(test_sample)

    model.config["train"]["dataset"]["val_condition_size"] = cond_size
    model.config["train"]["dataset"]["val_target_size"] = target_size
    
    if control_weight_lambda == "no":
        control_weight_lambda = None
    if vae_skip_iter == "no":
        vae_skip_iter = None
    use_condition_sblora_control = True
    use_latent_sblora_control = True
    image = generate_from_test_sample(
        test_sample, model.pipe, model.config, 
        num_images=num_images, 
        target_height=target_height,
        target_width=target_width,
        seed=seed,
        store_attn_map=store_attn_map, 
        vae_skip_iter=vae_skip_iter,  # 使用新的参数
        control_weight_lambda=control_weight_lambda,  # 传递新的参数
        double_attention=double_attention,  # 新增参数
        single_attention=single_attention,  # 新增参数
        ip_scale=ip_scale,
        use_latent_sblora_control=use_latent_sblora_control,
        latent_sblora_scale=latent_sblora_scale_str,
        use_condition_sblora_control=use_condition_sblora_control,
        condition_sblora_scale=vae_lora_scale,
    )
    if isinstance(image, list):
        num_cols = 2
        num_rows = int(math.ceil(num_images / num_cols))
        image = image_grid(image, num_rows, num_cols)

    save_path = f"{temp_dir}/tmp_result.png"
    image.save(save_path)

    return image



def merge_instances(orig_img, indices, ins_bboxes, ins_images):
    orig_image_width, orig_image_height = orig_img.width, orig_img.height
    final_img = Image.new("RGB", (orig_image_width, orig_image_height), color=(255, 255, 255))
    bboxes = []
    for i in indices:
        bbox = np.array(ins_bboxes[i], dtype=int).tolist()
        bboxes.append(bbox)
        
        img = cv2pil(ins_images[i])
        mask = (np.array(img)[..., :3] != 255).any(axis=-1)
        mask = Image.fromarray(mask.astype(np.uint8) * 255, mode='L')
        final_img.paste(img, (bbox[0], bbox[1]), mask)
    
    bbox = merge_bboxes(bboxes)
    img = final_img.crop(bbox)
    return img, bbox


def change_accordion(at: bool, index: int, state: list):
    print(at, state)
    indexs = state
    if at:
        if index not in indexs:
            indexs.append(index)
    else:
        if index in indexs:
            indexs.remove(index)
    
    # 确保 indexs 是有序的
    indexs.sort()
    print(indexs)
    return gr.Accordion(open=at), indexs

def update_inputs(is_open, index, state: list):
    indexs = state
    if is_open:
        if index not in indexs:
            indexs.append(index)
    else:
        if index in indexs:
            indexs.remove(index)
    
    # 确保 indexs 是有序的
    indexs.sort()
    print(indexs)
    return indexs, is_open

if __name__ == "__main__":

    with gr.Blocks() as demo:
    
        indexs_state = gr.State([0, 1])  # 添加状态来存储 indexs
        
        gr.Markdown("### XVerse Demo")
        with gr.Row():
            with gr.Column():
                prompt = gr.Textbox(label="Prompt", value="")
                with gr.Accordion("Open for More!", open=False):
                    
                    with gr.Row():
                        target_height = gr.Slider(512, 1024, step=128, value=768, label="Generated Height", info="")
                        target_width = gr.Slider(512, 1024, step=128, value=768, label="Generated Width", info="")
                        cond_size = gr.Slider(256, 384, step=128, value=256, label="Condition Size", info="")
                    with gr.Row():
                        weight_id = gr.Slider(0.1, 5, step=0.1, value=3, label="weight_id")
                        weight_ip = gr.Slider(0.1, 5, step=0.1, value=5, label="weight_ip")
                    with gr.Row():
                        ip_scale_str = gr.Slider(0.5, 1.5, step=0.01, value=0.85, label="latent_lora_scale")
                        vae_lora_scale = gr.Slider(0.5, 1.5, step=0.01, value=1.3, label="vae_lora_scale")
                    with gr.Row():
                        vae_skip_iter_s1 = gr.Slider(0, 1, step=0.01, value=0.05, label="vae_skip_iter_before")
                        vae_skip_iter_s2 = gr.Slider(0, 1, step=0.01, value=0.8, label="vae_skip_iter_after")
                    
            
                    with gr.Row():
                        weight_id_ip_str = gr.Textbox(
                            value="0-1:1/3/5",
                            label="weight_id_ip_str",
                            interactive=False, visible=False
                        )
                        weight_id.change(
                            lambda s1, s2: f"0-1:1/{s1}/{s2}",
                            inputs=[weight_id, weight_ip],
                            outputs=weight_id_ip_str
                        )
                        weight_ip.change(
                            lambda s1, s2: f"0-1:1/{s1}/{s2}",
                            inputs=[weight_id, weight_ip],
                            outputs=weight_id_ip_str
                        )
                        vae_skip_iter = gr.Textbox(
                            value="0-0.05:1,0.8-1:1",
                            label="vae_skip_iter",
                            interactive=False, visible=False
                        )
                        vae_skip_iter_s1.change(
                            lambda s1, s2: f"0-{s1}:1,{s2}-1:1",
                            inputs=[vae_skip_iter_s1, vae_skip_iter_s2],
                            outputs=vae_skip_iter
                        )
                        vae_skip_iter_s2.change(
                            lambda s1, s2: f"0-{s1}:1,{s2}-1:1",
                            inputs=[vae_skip_iter_s1, vae_skip_iter_s2],
                            outputs=vae_skip_iter
                        )
                        
                    
                    with gr.Row():
                        db_latent_lora_scale_str = gr.Textbox(
                            value="0-1:0.85",
                            label="db_latent_lora_scale_str",
                            interactive=False, visible=False
                        )
                        sb_latent_lora_scale_str = gr.Textbox(
                            value="0-1:0.85",
                            label="sb_latent_lora_scale_str",
                            interactive=False, visible=False
                        )
                        vae_lora_scale_str = gr.Textbox(
                            value="0-1:1.3",
                            label="vae_lora_scale_str",
                            interactive=False, visible=False
                        )
                        vae_lora_scale.change(
                                lambda s: f"0-1:{s}",
                                inputs=vae_lora_scale,
                                outputs=vae_lora_scale_str
                            )
                        ip_scale_str.change(
                                lambda s: [f"0-1:{s}", f"0-1:{s}"],
                                inputs=ip_scale_str,
                                outputs=[db_latent_lora_scale_str, sb_latent_lora_scale_str]
                            )
        
                    with gr.Row():
                        double_attention = gr.Checkbox(value=False, label="Double Attention", visible=False)
                        single_attention = gr.Checkbox(value=True, label="Single Attention", visible=False)            
    
                    clear_btn = gr.Button("清空输入图像")
                with gr.Row():
                    with gr.Column():
                        image_1 = gr.Image(type="filepath", label=f"Image 1")
                        caption_1 = gr.Textbox(label=f"Caption 1", value="")
                        id_ip_checkbox_1 = gr.Checkbox(value=False, label=f"ID or not 1", visible=True)
                        with gr.Row():
                            vlm_btn_1 = gr.Button("Auto Caption")
                            det_btn_1 = gr.Button("Det & Seg")
                            face_btn_1 = gr.Button("Crop Face")
    
                    with gr.Column():
                        image_2 = gr.Image(type="filepath", label=f"Image 2")
                        caption_2 = gr.Textbox(label=f"Caption 2", value="")
                        id_ip_checkbox_2 = gr.Checkbox(value=False, label=f"ID or not 2", visible=True)
                        with gr.Row():
                            vlm_btn_2 = gr.Button("Auto Caption")
                            det_btn_2 = gr.Button("Det & Seg")
                            face_btn_2 = gr.Button("Crop Face")
        
            with gr.Column():
                output = gr.Image(label="生成的图像")
                seed = gr.Number(value=42, label="Seed", info="")
                gen_btn = gr.Button("生成图像")
    
        gr.Markdown("### Examples")
        gen_btn.click(
            generate_image, 
            inputs=[
                prompt, cond_size, target_height, target_width, seed,
                vae_skip_iter, weight_id_ip_str,
                double_attention, single_attention,
                db_latent_lora_scale_str, sb_latent_lora_scale_str, vae_lora_scale_str,
                indexs_state,  # 传递 indexs 状态
                # *images,  
                # *captions, 
                # *idip_checkboxes,
            ], 
            outputs=output
        )
    
        # # 修改清空函数的输出参数
        clear_btn.click(clear_images, outputs=images)
    
        face_btn_1.click(crop_face_img, inputs=[image_1], outputs=[image_1])
        det_btn_1.click(det_seg_img, inputs=[image_1, caption_1], outputs=[image_1])
        vlm_btn_1.click(vlm_img_caption, inputs=[image_1], outputs=[caption_1])
    
        face_btn_2.click(crop_face_img, inputs=[image_2], outputs=[image_2])
        det_btn_2.click(det_seg_img, inputs=[image_2, caption_2], outputs=[image_2])
        vlm_btn_2.click(vlm_img_caption, inputs=[image_2], outputs=[caption_2])
    
    
    demo.queue()
    demo.launch()