Upload 2 files

.gitattributes

@@ -33,3 +33,4 @@ 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
+demo.jpg filter=lfs diff=lfs merge=lfs -text

app-fp8.py

@@ -0,0 +1,859 @@
+# new add
+import time
+import psutil
+import platform  
+import atexit   
+
+pynvml_available = False
+if platform.system() == "Linux" or platform.system() == "Windows":
+    try:
+        from pynvml import *
+        nvmlInit()
+        pynvml_available = True
+        print("pynvml (NVIDIA GPU monitoring library) initialized successfully.")
+        
+        def shutdown_pynvml():
+            print("Shutting down pynvml...")
+            nvmlShutdown()
+        atexit.register(shutdown_pynvml) # register close pynvml when it quit
+        
+    except Exception as e:
+        print(f"Warning: pynvml could not be initialized. Detailed GPU stats via pynvml will not be available. Error: {e}")
+        if "NVML Shared Library Not Found" in str(e):
+            print("pynvml error hint: NVML shared library not found. If you have an NVIDIA GPU and drivers, ensure the library is accessible.")
+        elif "Driver Not Loaded" in str(e):
+            print("pynvml error hint: NVIDIA driver is not loaded. Please check your GPU driver installation.")
+
+import gradio as gr
+import numpy as np
+import os
+import torch
+import random
+
+from accelerate import infer_auto_device_map, load_checkpoint_and_dispatch, init_empty_weights
+from PIL import Image
+
+from data.data_utils import add_special_tokens, pil_img2rgb
+from data.transforms import ImageTransform
+from inferencer import InterleaveInferencer
+from modeling.autoencoder import load_ae
+from modeling.bagel.qwen2_navit import NaiveCache
+from modeling.bagel import (
+    BagelConfig, Bagel, Qwen2Config, Qwen2ForCausalLM,
+    SiglipVisionConfig, SiglipVisionModel
+)
+from modeling.qwen2 import Qwen2Tokenizer
+
+import argparse
+
+parser = argparse.ArgumentParser() 
+parser.add_argument("--server_name", type=str, default="127.0.0.1")
+parser.add_argument("--server_port", type=int, default=7860)
+parser.add_argument("--share", action="store_true")
+parser.add_argument("--model_path", type=str, default="/root/your_path/BAGEL-7B-MoT")
+args = parser.parse_args()
+
+# Model Initialization
+model_path = args.model_path #Download from https://huggingface.co/ByteDance-Seed/BAGEL-7B-MoT to models/BAGEL-7B-MoT
+
+llm_config = Qwen2Config.from_json_file(os.path.join(model_path, "llm_config.json"))
+llm_config.qk_norm = True
+llm_config.tie_word_embeddings = False
+llm_config.layer_module = "Qwen2MoTDecoderLayer"
+
+vit_config = SiglipVisionConfig.from_json_file(os.path.join(model_path, "vit_config.json"))
+vit_config.rope = False
+vit_config.num_hidden_layers -= 1
+
+vae_model, vae_config = load_ae(local_path=os.path.join(model_path, "ae.safetensors"))
+
+config = BagelConfig(
+    visual_gen=True,
+    visual_und=True,
+    llm_config=llm_config, 
+    vit_config=vit_config,
+    vae_config=vae_config,
+    vit_max_num_patch_per_side=70,
+    connector_act='gelu_pytorch_tanh',
+    latent_patch_size=2,
+    max_latent_size=64,
+)
+
+with init_empty_weights():
+    language_model = Qwen2ForCausalLM(llm_config)
+    vit_model      = SiglipVisionModel(vit_config)
+    model          = Bagel(language_model, vit_model, config)
+    model.vit_model.vision_model.embeddings.convert_conv2d_to_linear(vit_config, meta=True)
+
+tokenizer = Qwen2Tokenizer.from_pretrained(model_path)
+tokenizer, new_token_ids, _ = add_special_tokens(tokenizer)
+
+vae_transform = ImageTransform(1024, 512, 16)
+vit_transform = ImageTransform(980, 224, 14)
+
+# # Model Loading and Multi GPU Infernece Preparing
+# device_map = infer_auto_device_map(
+#     model,
+#     max_memory={i: "24GiB" for i in range(torch.cuda.device_count())},
+#     no_split_module_classes=["Bagel", "Qwen2MoTDecoderLayer"],
+# )
+
+# same_device_modules = [
+#     'language_model.model.embed_tokens',
+#     'time_embedder',
+#     'latent_pos_embed',
+#     'vae2llm',
+#     'llm2vae',
+#     'connector',
+#     'vit_pos_embed'
+# ]
+
+# if torch.cuda.device_count() == 1:
+#     first_device = device_map.get(same_device_modules[0], "cuda:0")
+#     for k in same_device_modules:
+#         if k in device_map:
+#             device_map[k] = first_device
+#         else:
+#             device_map[k] = "cuda:0"
+# else:
+#     first_device = device_map.get(same_device_modules[0])
+#     for k in same_device_modules:
+#         if k in device_map:
+#             device_map[k] = first_device
+
+# --- new changes ---
+print("Starting model loading and device map configuration...")
+
+# --- ram & vram helps functions ---
+def get_gpu_memory_stats_pynvml(device_id=0):
+    if not pynvml_available:
+        return f"GPU-{device_id} (pynvml): Not available."
+    try:
+        handle = nvmlDeviceGetHandleByIndex(device_id)
+        info = nvmlDeviceGetMemoryInfo(handle)
+        total_gb = info.total / (1024**3)
+        used_gb = info.used / (1024**3)
+        # free_gb = info.free / (1024**3) # It can be calculated by the sum already used
+        return f"GPU-{device_id} (pynvml): Total: {total_gb:.2f} GB, Used (Overall): {used_gb:.2f} GB"
+    except NVMLError as e:
+        return f"GPU-{device_id} (pynvml) Error: {e}"
+
+def get_gpu_memory_stats_pytorch(device_id=0):
+    if not torch.cuda.is_available():
+        return "PyTorch: CUDA not available."
+    if device_id < 0 or device_id >= torch.cuda.device_count():
+        return f"PyTorch GPU-{device_id}: Invalid device ID."
+    
+    allocated_gb = torch.cuda.memory_allocated(device_id) / (1024**3)
+    reserved_gb = torch.cuda.memory_reserved(device_id) / (1024**3) # PyTorch Reserved Total vram
+    
+    # try gets pynvml info 
+    total_capacity_str_pt = ""
+    if pynvml_available:
+        try:
+            handle = nvmlDeviceGetHandleByIndex(device_id)
+            info = nvmlDeviceGetMemoryInfo(handle)
+            total_gb_pt = info.total / (1024**3)
+            total_capacity_str_pt = f"Total Capacity: {total_gb_pt:.2f} GB, "
+        except NVMLError:
+            pass # If the acquisition fails, the total capacity will not be displayed
+
+    return (f"PyTorch GPU-{device_id}: {total_capacity_str_pt}"
+            f"Allocated: {allocated_gb:.2f} GB, Reserved: {reserved_gb:.2f} GB")
+
+def get_system_ram_stats():
+    mem = psutil.virtual_memory()
+    total_gb = mem.total / (1024**3)
+    available_gb = mem.available / (1024**3)
+    used_gb = mem.used / (1024**3)
+    percent_used = mem.percent
+    return (f"System RAM: Total: {total_gb:.2f} GB, Available: {available_gb:.2f} GB, "
+            f"Used (Overall): {used_gb:.2f} GB ({percent_used}%)")
+
+def get_process_ram_stats():
+    process = psutil.Process(os.getpid()) # get the current Python process
+    mem_info = process.memory_info()
+    rss_gb = mem_info.rss / (1024**3)  # Resident Set Size (Actual physical memory usage)
+    return f"App Process RAM (RSS): {rss_gb:.2f} GB"
+
+def get_all_memory_stats_for_gradio_display():
+    """Prepare the string of memory/video memory statistics for Gradio display"""
+    stats_lines = []
+    if torch.cuda.is_available() and torch.cuda.device_count() > 0:
+        stats_lines.append("**GPU VRAM Usage:**")
+        for i in range(torch.cuda.device_count()):
+            stats_lines.append(get_gpu_memory_stats_pynvml(i))
+            stats_lines.append(get_gpu_memory_stats_pytorch(i))
+            if i < torch.cuda.device_count() - 1: # If there are multiple GPUs, add a separator
+                 stats_lines.append("---")
+    else:
+        stats_lines.append("**GPU VRAM Usage:** CUDA not available or no GPUs found.")
+    
+    stats_lines.append("\n**CPU RAM Usage:**")
+    stats_lines.append(get_system_ram_stats())
+    stats_lines.append(get_process_ram_stats())
+    
+    return "\n".join(s for s in stats_lines if s)
+# --- ram & vram helps functions end ---
+
+
+# ram & vram setting
+# ram & vram setting
+# ram & vram setting  edit by your spec
+# If you have 60GB CPU vram，there are 55GiB (Leave some vram)
+
+cpu_mem_for_offload = "16GiB"
+gpu_mem_per_device = "24GiB" # Your GPU Vram
+
+max_memory_config = {i: gpu_mem_per_device for i in range(torch.cuda.device_count())}
+if torch.cuda.device_count() == 0: # If there is no GPU, a basic configuration is also required
+    max_memory_config["cpu"] = cpu_mem_for_offload
+else:
+    max_memory_config["cpu"] = cpu_mem_for_offload # Add a budget for the CPU
+
+print(f"Using max_memory_config: {max_memory_config}")
+
+device_map = infer_auto_device_map(
+    model,
+    max_memory=max_memory_config, # Use the configuration that includes the CPU memory budget
+    no_split_module_classes=["Bagel", "Qwen2MoTDecoderLayer"],
+)
+print("Device map after infer_auto_device_map (with CPU budget):")
+for k, v_map in device_map.items(): # Check info
+    print(f"  {k}: {v_map}")
+
+same_device_modules = [
+    'language_model.model.embed_tokens',
+    'time_embedder',
+    'latent_pos_embed',
+    'vae2llm',
+    'llm2vae',
+    'connector',
+    'vit_pos_embed'
+]
+
+# already have same_device_modules 
+if torch.cuda.device_count() > 0:
+    first_device_key = same_device_modules[0]
+    default_target_device = "cuda:0" # The default target is the first GPU
+    first_module_target_device = device_map.get(first_device_key, default_target_device)
+    
+    print(f"Target device for same_device_modules (based on {first_device_key}): {first_module_target_device}")
+
+    for k_module in same_device_modules:
+        if k_module in device_map:
+            if device_map[k_module] != first_module_target_device:
+                print(f"  Moving {k_module} from {device_map[k_module]} to {first_module_target_device} (same_device_modules)")
+                device_map[k_module] = first_module_target_device
+        else: # If the module is not in the automatically generated map but you want it to be on a specific device
+            print(f"  Assigning {k_module} (from same_device_modules) to {first_module_target_device} as it was not in initial map.")
+            device_map[k_module] = first_module_target_device 
+elif torch.cuda.device_count() == 0 and "cpu" in max_memory_config: # without GPU
+    print("No CUDA devices found. Assigning same_device_modules to CPU.")
+    for k_module in same_device_modules:
+        device_map[k_module] = "cpu"
+
+
+print("Device map after same_device_modules logic:")
+for k, v_map in device_map.items():
+    print(f"  {k}: {v_map}")
+
+# key point 2：make sure no 'disk'  (backup)
+keys_to_change_to_cpu = []
+for module_name, device_assignment in device_map.items():
+    if device_assignment == "disk":
+        keys_to_change_to_cpu.append(module_name)
+
+if keys_to_change_to_cpu:
+    print(f"Manually changing the following layers from 'disk' to 'cpu': {keys_to_change_to_cpu}")
+    for module_name in keys_to_change_to_cpu:
+        device_map[module_name] = "cpu"
+    print("Final device_map before loading checkpoint (after disk override):")
+    for k, v_map in device_map.items():
+        print(f"  {k}: {v_map}")
+else:
+    print("No layers assigned to 'disk' by infer_auto_device_map, or they were already handled. Final device_map is as above.")
+# --- fix model loadding end ---
+
+# adjust layers more clearly&detail to GPU
+# make sure，The device_map only contains GPU indexes (such as 0) or 'cpu'.
+print("\nStarting custom device_map modifications to maximize GPU utilization...")
+print("Device map state BEFORE custom modifications:")
+for k_map_item, v_map_item in device_map.items():
+    print(f"  {k_map_item}: {v_map_item}")
+
+
+# -- Key tuning parameters Start --
+
+
+
+# 1. Try to move more LLM Transformer layers (layers 11 to 27) to GPU 0
+# These layers are currently on the CPU. There are a total of 17 such layers (ranging from 11 to 27).
+# You can set the number of LLM layers that you wish to move from the CPU to GPU 0.
+# Please start the experiment with a smaller value, such as 5 or 8, and then gradually increase it.
+# If set to 17, all layers 11-27 will be attempted to be moved.
+
+NUM_ADDITIONAL_LLM_LAYERS_TO_GPU = 5  # <--- 5 fit for 24GB Vram for TEST layers(like: 5, 8, 10, 12, 15, 17)
+
+# 2. Whether to attempt to move the 'norm' and 'lm_head' layers of LLM to GPU 0 (if they are on the CPU)
+# It is usually recommended to place them on the same device as the last layer of the LLM.
+
+TRY_MOVE_LLM_NORM_HEAD_TO_GPU = True # <--- Default True, Turn to False,If you don't want to remove
+
+# 3. (Optional) Whether to attempt to move 'vit_model' to GPU 0 (if it is on the CPU)
+# This is usually considered only after the LLM layer has been successfully moved to the GPU 
+# And there is still a considerable amount of video memory left.
+    
+TRY_MOVE_VIT_MODEL_TO_GPU = False   # <--- Default False , can be test
+
+# --- Adjust end ---
+
+
+# run LLM layers move
+moved_llm_layers_count = 0
+if NUM_ADDITIONAL_LLM_LAYERS_TO_GPU > 0:
+    print(f"\nAttempting to move up to {NUM_ADDITIONAL_LLM_LAYERS_TO_GPU} LLM layers (11 to {10 + NUM_ADDITIONAL_LLM_LAYERS_TO_GPU}) to GPU 0...")
+    for i in range(NUM_ADDITIONAL_LLM_LAYERS_TO_GPU):
+        layer_idx = 11 + i  # From layer 11 to start
+        if layer_idx > 27:  # language_model.model.layers Max to 27
+            print(f"  Reached max layer index (27). Stopped LLM layer promotion.")
+            break
+        layer_name = f"language_model.model.layers.{layer_idx}"
+        
+        if device_map.get(layer_name) == 'cpu':
+            print(f"  Promoting LLM layer '{layer_name}' from 'cpu' to GPU 0.")
+            device_map[layer_name] = 0  # move to GPU 0
+            moved_llm_layers_count += 1
+        elif layer_name in device_map:
+            print(f"  LLM Layer '{layer_name}' is already on device '{device_map[layer_name]}'. Skipping promotion.")
+        else:
+            print(f"  Warning: LLM Layer '{layer_name}' not found in device_map. Cannot promote.")
+    print(f"Successfully promoted {moved_llm_layers_count} LLM layers to GPU 0.")
+else:
+    print("\nSkipping promotion of additional LLM layers based on NUM_ADDITIONAL_LLM_LAYERS_TO_GPU setting.")
+
+# run LLM norm  & lm_head move
+if TRY_MOVE_LLM_NORM_HEAD_TO_GPU:
+    print("\nAttempting to move LLM 'norm' and 'lm_head' to GPU 0 (if on CPU)...")
+    llm_aux_modules = ["language_model.model.norm", "language_model.model.lm_head"]
+    # you can choose other modules with LLM，eg: rotary_emb, norm_moe_gen，if there are on the CPU
+    # llm_aux_modules.append("language_model.model.rotary_emb")
+    # llm_aux_modules.append("language_model.model.norm_moe_gen")
+
+    for module_name in llm_aux_modules:
+        if device_map.get(module_name) == 'cpu':
+            print(f"  Promoting '{module_name}' from 'cpu' to GPU 0.")
+            device_map[module_name] = 0
+        elif module_name in device_map:
+            print(f"  Module '{module_name}' is already on device '{device_map[module_name]}'. Skipping promotion.")
+        else:
+            print(f"  Warning: Module '{module_name}' not found in device_map. Cannot promote.")
+else:
+    print("\nSkipping promotion of LLM 'norm' and 'lm_head' based on TRY_MOVE_LLM_NORM_HEAD_TO_GPU setting.")
+
+# （option）run vit_model move
+if TRY_MOVE_VIT_MODEL_TO_GPU:
+    print("\nAttempting to move 'vit_model' to GPU 0 (if on CPU)...")
+    vit_module_name = "vit_model"
+    if device_map.get(vit_module_name) == 'cpu':
+        print(f"  Promoting '{vit_module_name}' from 'cpu' to GPU 0.")
+        device_map[vit_module_name] = 0
+    elif vit_module_name in device_map:
+        print(f"  Module '{vit_module_name}' is already on device '{device_map[vit_module_name]}'. Skipping promotion.")
+    else:
+        print(f"  Warning: Module '{vit_module_name}' not found in device_map. Cannot promote.")
+else:
+    print("\nSkipping promotion of 'vit_model' based on TRY_MOVE_VIT_MODEL_TO_GPU setting.")
+
+
+print("\nFinal device_map after all custom modifications:")
+for k_map_item, v_map_item in device_map.items():
+    print(f"  {k_map_item}: {v_map_item}")
+print("--- End of custom device_map modifications ---")
+
+# adjust gpu vram end
+
+
+
+
+model = load_checkpoint_and_dispatch(
+    model,
+    checkpoint=os.path.join(model_path, "ema-FP8.safetensors"),
+    device_map=device_map,
+    offload_buffers=True,
+    offload_folder="offload",
+    dtype=torch.bfloat16,
+    force_hooks=True,
+).eval()
+
+
+# Inferencer Preparing 
+inferencer = InterleaveInferencer(
+    model=model,
+    vae_model=vae_model,
+    tokenizer=tokenizer,
+    vae_transform=vae_transform,
+    vit_transform=vit_transform,
+    new_token_ids=new_token_ids,
+)
+
+def set_seed(seed):
+    """Set random seeds for reproducibility"""
+    if seed > 0:
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed(seed)
+            torch.cuda.manual_seed_all(seed)
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.benchmark = False
+    return seed
+
+# Text to Image function with thinking option and hyperparameters
+def text_to_image(prompt, show_thinking=False, cfg_text_scale=4.0, cfg_interval=0.4, 
+                 timestep_shift=3.0, num_timesteps=50, 
+                 cfg_renorm_min=1.0, cfg_renorm_type="global", 
+                 max_think_token_n=1024, do_sample=False, text_temperature=0.3,
+                 seed=0, image_ratio="1:1"):
+    # Set seed for reproducibility
+    set_seed(seed)
+
+    if image_ratio == "1:1":
+        image_shapes = (1024, 1024)
+    elif image_ratio == "4:3":
+        image_shapes = (768, 1024)
+    elif image_ratio == "3:4":
+        image_shapes = (1024, 768) 
+    elif image_ratio == "16:9":
+        image_shapes = (576, 1024)
+    elif image_ratio == "9:16":
+        image_shapes = (1024, 576) 
+    
+    # Set hyperparameters
+    inference_hyper = dict(
+        max_think_token_n=max_think_token_n if show_thinking else 1024,
+        do_sample=do_sample if show_thinking else False,
+        text_temperature=text_temperature if show_thinking else 0.3,
+        cfg_text_scale=cfg_text_scale,
+        cfg_interval=[cfg_interval, 1.0],  # End fixed at 1.0
+        timestep_shift=timestep_shift,
+        num_timesteps=num_timesteps,
+        cfg_renorm_min=cfg_renorm_min,
+        cfg_renorm_type=cfg_renorm_type,
+        image_shapes=image_shapes,
+    )
+    # --- add：record start_time ---
+    start_time = time.time()
+    
+    # Call inferencer with or without think parameter based on user choice
+    result = inferencer(text=prompt, think=show_thinking, **inference_hyper)
+    
+    # --- add：record end_time ---
+    end_time = time.time()
+    duration = end_time - start_time
+    duration_str = f"{duration:.2f} seconds"
+    print(f"The image takes time: {duration_str}") # conslog
+    
+    return result["image"], result.get("text", None), duration_str
+
+
+# Image Understanding function with thinking option and hyperparameters
+def image_understanding(image: Image.Image, prompt: str, show_thinking=False, 
+                        do_sample=False, text_temperature=0.3, max_new_tokens=512):
+    # --- add：record start_time ---
+    start_time = time.time()
+    
+    if image is None:
+        return "Please upload an image."
+
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+
+    image = pil_img2rgb(image)
+    
+    # Set hyperparameters
+    inference_hyper = dict(
+        do_sample=do_sample,
+        text_temperature=text_temperature,
+        max_think_token_n=max_new_tokens, # Set max_length
+    )
+    
+    # Use show_thinking parameter to control thinking process
+    result = inferencer(image=image, text=prompt, think=show_thinking, 
+                        understanding_output=True, **inference_hyper)
+
+    # --- add：record end_time ---
+    end_time = time.time()
+    duration = end_time - start_time
+    duration_str = f"{duration:.2f} seconds"
+    print(f"The image takes time: {duration_str}") # conslog
+    
+    return result["text"], duration_str
+
+
+# Image Editing function with thinking option and hyperparameters
+def edit_image(image: Image.Image, prompt: str, show_thinking=False, cfg_text_scale=4.0, 
+              cfg_img_scale=2.0, cfg_interval=0.0, 
+              timestep_shift=3.0, num_timesteps=50, cfg_renorm_min=1.0, 
+              cfg_renorm_type="text_channel", max_think_token_n=1024, 
+              do_sample=False, text_temperature=0.3, seed=0):
+    # Set seed for reproducibility
+    set_seed(seed)
+
+    # --- add：record start_time ---
+    start_time = time.time()
+    
+    if image is None:
+        return "Please upload an image.", ""
+
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+
+    image = pil_img2rgb(image)
+    
+    # Set hyperparameters
+    inference_hyper = dict(
+        max_think_token_n=max_think_token_n if show_thinking else 1024,
+        do_sample=do_sample if show_thinking else False,
+        text_temperature=text_temperature if show_thinking else 0.3,
+        cfg_text_scale=cfg_text_scale,
+        cfg_img_scale=cfg_img_scale,
+        cfg_interval=[cfg_interval, 1.0],  # End fixed at 1.0
+        timestep_shift=timestep_shift,
+        num_timesteps=num_timesteps,
+        cfg_renorm_min=cfg_renorm_min,
+        cfg_renorm_type=cfg_renorm_type,
+    )
+    
+    # Include thinking parameter based on user choice
+    result = inferencer(image=image, text=prompt, think=show_thinking, **inference_hyper)
+
+    # --- add：record end_time ---
+    end_time = time.time()
+    duration = end_time - start_time
+    duration_str = f"{duration:.2f} seconds"
+    print(f"The image takes time: {duration_str}") # conslog
+    
+    return result["image"], result.get("text", ""), duration_str
+
+
+# Helper function to load example images
+def load_example_image(image_path):
+    try:
+        return Image.open(image_path)
+    except Exception as e:
+        print(f"Error loading example image: {e}")
+        return None
+
+
+# Gradio UI 
+with gr.Blocks() as demo:
+    gr.Markdown("""
+<div>
+  <img src="https://lf3-static.bytednsdoc.com/obj/eden-cn/nuhojubrps/banner.png" alt="BAGEL" width="380"/>
+</div>
+""")
+
+    with gr.Tab("📝 Text to Image"):
+        txt_input = gr.Textbox(
+            label="Prompt", 
+            value="A female cosplayer portraying an ethereal fairy or elf, wearing a flowing dress made of delicate fabrics in soft, mystical colors like emerald green and silver. She has pointed ears, a gentle, enchanting expression, and her outfit is adorned with sparkling jewels and intricate patterns. The background is a magical forest with glowing plants, mystical creatures, and a serene atmosphere."
+        )
+        
+        with gr.Row():
+            show_thinking = gr.Checkbox(label="Thinking", value=False)
+        
+        # Add hyperparameter controls in an accordion
+        with gr.Accordion("Inference Hyperparameters", open=False):
+            with gr.Group():
+                with gr.Row():
+                    seed = gr.Slider(minimum=0, maximum=1000000, value=0, step=1, 
+                                   label="Seed", info="0 for random seed, positive for reproducible results")
+                    image_ratio = gr.Dropdown(choices=["1:1", "4:3", "3:4", "16:9", "9:16"], 
+                                                value="1:1", label="Image Ratio", 
+                                                info="The longer size is fixed to 1024")
+                    
+                with gr.Row():
+                    cfg_text_scale = gr.Slider(minimum=1.0, maximum=8.0, value=4.0, step=0.1, interactive=True,
+                                             label="CFG Text Scale", info="Controls how strongly the model follows the text prompt (4.0-8.0)")
+                    cfg_interval = gr.Slider(minimum=0.0, maximum=1.0, value=0.4, step=0.1, 
+                                           label="CFG Interval", info="Start of CFG application interval (end is fixed at 1.0)")
+                
+                with gr.Row():
+                    cfg_renorm_type = gr.Dropdown(choices=["global", "local", "text_channel"], 
+                                                value="global", label="CFG Renorm Type", 
+                                                info="If the genrated image is blurry, use 'global'")
+                    cfg_renorm_min = gr.Slider(minimum=0.0, maximum=1.0, value=0.0, step=0.1, interactive=True,
+                                             label="CFG Renorm Min", info="1.0 disables CFG-Renorm")
+                
+                with gr.Row():
+                    num_timesteps = gr.Slider(minimum=10, maximum=100, value=50, step=5, interactive=True,
+                                            label="Timesteps", info="Total denoising steps")
+                    timestep_shift = gr.Slider(minimum=1.0, maximum=5.0, value=3.0, step=0.5, interactive=True,
+                                             label="Timestep Shift", info="Higher values for layout, lower for details")
+                
+                # Thinking parameters in a single row
+                thinking_params = gr.Group(visible=False)
+                with thinking_params:
+                    with gr.Row():
+                        do_sample = gr.Checkbox(label="Sampling", value=False, info="Enable sampling for text generation")
+                        max_think_token_n = gr.Slider(minimum=64, maximum=4006, value=1024, step=64, interactive=True,
+                                                    label="Max Think Tokens", info="Maximum number of tokens for thinking")
+                        text_temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.3, step=0.1, interactive=True,
+                                                  label="Temperature", info="Controls randomness in text generation")
+        
+        thinking_output = gr.Textbox(label="Thinking Process", visible=False)
+        img_output = gr.Image(label="Generated Image")
+        gen_btn = gr.Button("Generate", variant="primary")
+        
+        # --- add：A text box used to display the generation time ---
+        generation_time_output = gr.Textbox(label="Processing Time", interactive=False)
+        # --- add end ---
+        
+        # Dynamically show/hide thinking process box and parameters
+        def update_thinking_visibility(show):
+            return gr.update(visible=show), gr.update(visible=show)
+        
+        show_thinking.change(
+            fn=update_thinking_visibility,
+            inputs=[show_thinking],
+            outputs=[thinking_output, thinking_params]
+        )
+        
+        # Process function based on thinking option and hyperparameters
+        def process_text_to_image(prompt, show_thinking, cfg_text_scale, 
+                                 cfg_interval, timestep_shift, 
+                                 num_timesteps, cfg_renorm_min, cfg_renorm_type, 
+                                 max_think_token_n, do_sample, text_temperature, seed, image_ratio):
+            image, thinking, duration_str = text_to_image(
+                prompt, show_thinking, cfg_text_scale, cfg_interval,
+                timestep_shift, num_timesteps, 
+                cfg_renorm_min, cfg_renorm_type,
+                max_think_token_n, do_sample, text_temperature, seed, image_ratio
+            )
+            return image, thinking if thinking else "", duration_str 
+        
+        gr.on(
+            triggers=[gen_btn.click, txt_input.submit],
+            fn=process_text_to_image,
+            inputs=[
+                txt_input, show_thinking, cfg_text_scale, 
+                cfg_interval, timestep_shift, 
+                num_timesteps, cfg_renorm_min, cfg_renorm_type,
+                max_think_token_n, do_sample, text_temperature, seed, image_ratio
+            ],
+            # --- 这里是关键修改：确保 outputs 列表包含三个组件 ---
+            outputs=[img_output, thinking_output, generation_time_output]
+            # --- 修改结束 ---
+        )
+
+    with gr.Tab("🖌️ Image Edit"):
+        with gr.Row():
+            with gr.Column(scale=1):
+                edit_image_input = gr.Image(label="Input Image", value=load_example_image('test_images/women.jpg'))
+                edit_prompt = gr.Textbox(
+                    label="Prompt",
+                    value="She boards a modern subway, quietly reading a folded newspaper, wearing the same clothes."
+                )
+            
+            with gr.Column(scale=1):
+                edit_image_output = gr.Image(label="Result")
+                edit_thinking_output = gr.Textbox(label="Thinking Process", visible=False)
+        
+        with gr.Row():
+            edit_show_thinking = gr.Checkbox(label="Thinking", value=False)
+        
+        # Add hyperparameter controls in an accordion
+        with gr.Accordion("Inference Hyperparameters", open=False):
+            with gr.Group():
+                with gr.Row():
+                    edit_seed = gr.Slider(minimum=0, maximum=1000000, value=0, step=1, interactive=True,
+                                        label="Seed", info="0 for random seed, positive for reproducible results")
+                    edit_cfg_text_scale = gr.Slider(minimum=1.0, maximum=8.0, value=4.0, step=0.1, interactive=True,
+                                                  label="CFG Text Scale", info="Controls how strongly the model follows the text prompt")
+                
+                with gr.Row():
+                    edit_cfg_img_scale = gr.Slider(minimum=1.0, maximum=4.0, value=2.0, step=0.1, interactive=True,
+                                                 label="CFG Image Scale", info="Controls how much the model preserves input image details")
+                    edit_cfg_interval = gr.Slider(minimum=0.0, maximum=1.0, value=0.0, step=0.1, interactive=True,
+                                                label="CFG Interval", info="Start of CFG application interval (end is fixed at 1.0)")
+                    
+                with gr.Row():
+                    edit_cfg_renorm_type = gr.Dropdown(choices=["global", "local", "text_channel"], 
+                                                     value="text_channel", label="CFG Renorm Type", 
+                                                     info="If the genrated image is blurry, use 'global")
+                    edit_cfg_renorm_min = gr.Slider(minimum=0.0, maximum=1.0, value=0.0, step=0.1, interactive=True,
+                                                  label="CFG Renorm Min", info="1.0 disables CFG-Renorm")
+                
+                with gr.Row():
+                    edit_num_timesteps = gr.Slider(minimum=10, maximum=100, value=50, step=5, interactive=True,
+                                                 label="Timesteps", info="Total denoising steps")
+                    edit_timestep_shift = gr.Slider(minimum=1.0, maximum=10.0, value=3.0, step=0.5, interactive=True,
+                                                  label="Timestep Shift", info="Higher values for layout, lower for details")
+                
+                
+                # Thinking parameters in a single row
+                edit_thinking_params = gr.Group(visible=False)
+                with edit_thinking_params:
+                    with gr.Row():
+                        edit_do_sample = gr.Checkbox(label="Sampling", value=False, info="Enable sampling for text generation")
+                        edit_max_think_token_n = gr.Slider(minimum=64, maximum=4006, value=1024, step=64, interactive=True,
+                                                         label="Max Think Tokens", info="Maximum number of tokens for thinking")
+                        edit_text_temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.3, step=0.1, interactive=True,
+                                                        label="Temperature", info="Controls randomness in text generation")
+        
+        edit_btn = gr.Button("Submit", variant="primary")
+        # --- add：A text box used to display the generation time ---
+        edit_generation_time_output = gr.Textbox(label="Processing Time", interactive=False)
+        # --- add end ---
+        
+        # Dynamically show/hide thinking process box for editing
+        def update_edit_thinking_visibility(show):
+            return gr.update(visible=show), gr.update(visible=show)
+        
+        edit_show_thinking.change(
+            fn=update_edit_thinking_visibility,
+            inputs=[edit_show_thinking],
+            outputs=[edit_thinking_output, edit_thinking_params]
+        )
+        
+        # Process editing with thinking option and hyperparameters
+        def process_edit_image(image, prompt, show_thinking, cfg_text_scale, 
+                              cfg_img_scale, cfg_interval, 
+                              timestep_shift, num_timesteps, cfg_renorm_min, 
+                              cfg_renorm_type, max_think_token_n, do_sample, 
+                              text_temperature, seed):
+            edited_image, thinking, duration_str = edit_image(
+                image, prompt, show_thinking, cfg_text_scale, cfg_img_scale, 
+                cfg_interval, timestep_shift, 
+                num_timesteps, cfg_renorm_min, cfg_renorm_type,
+                max_think_token_n, do_sample, text_temperature, seed
+            )
+            
+            return edited_image, thinking if thinking else "", duration_str
+        
+        gr.on(
+            triggers=[edit_btn.click, edit_prompt.submit],
+            fn=process_edit_image,
+            inputs=[
+                edit_image_input, edit_prompt, edit_show_thinking, 
+                edit_cfg_text_scale, edit_cfg_img_scale, edit_cfg_interval,
+                edit_timestep_shift, edit_num_timesteps, 
+                edit_cfg_renorm_min, edit_cfg_renorm_type,
+                edit_max_think_token_n, edit_do_sample, edit_text_temperature, edit_seed
+            ],
+            outputs=[edit_image_output, edit_thinking_output, edit_generation_time_output]
+        )
+
+    with gr.Tab("🖼️ Image Understanding"):
+        with gr.Row():
+            with gr.Column(scale=1):
+                img_input = gr.Image(label="Input Image", value=load_example_image('test_images/meme.jpg'))
+                understand_prompt = gr.Textbox(
+                    label="Prompt", 
+                    value="Can someone explain what's funny about this meme??"
+                )
+            
+            with gr.Column(scale=1):
+                txt_output = gr.Textbox(label="Result", lines=20)
+                
+        
+        with gr.Row():
+            understand_show_thinking = gr.Checkbox(label="Thinking", value=False)
+        
+        # Add hyperparameter controls in an accordion
+        with gr.Accordion("Inference Hyperparameters", open=False):
+            with gr.Row():
+                understand_do_sample = gr.Checkbox(label="Sampling", value=False, info="Enable sampling for text generation")
+                understand_text_temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.3, step=0.05, interactive=True,
+                                                     label="Temperature", info="Controls randomness in text generation (0=deterministic, 1=creative)")
+                understand_max_new_tokens = gr.Slider(minimum=64, maximum=4096, value=512, step=64, interactive=True,
+                                                   label="Max New Tokens", info="Maximum length of generated text, including potential thinking")
+        
+        img_understand_btn = gr.Button("Submit", variant="primary")
+        # --- add：A text box used to display the generation time ---
+        understand_generation_time_output = gr.Textbox(label="Processing Time", interactive=False)
+        # --- add end ---
+        
+        # Process understanding with thinking option and hyperparameters
+        def process_understanding(image, prompt, show_thinking, do_sample, 
+                                 text_temperature, max_new_tokens):
+            result, duration_str = image_understanding(
+                image, prompt, show_thinking, do_sample, 
+                text_temperature, max_new_tokens
+            )
+            return result, duration_str
+        
+        gr.on(
+            triggers=[img_understand_btn.click, understand_prompt.submit],
+            fn=process_understanding,
+            inputs=[
+                img_input, understand_prompt, understand_show_thinking,
+                understand_do_sample, understand_text_temperature, understand_max_new_tokens
+            ],
+            outputs=[txt_output, understand_generation_time_output]
+        )
+
+    gr.Markdown("""
+<div style="display: flex; justify-content: flex-start; flex-wrap: wrap; gap: 10px;">
+  <a href="https://bagel-ai.org/">
+    <img
+      src="https://img.shields.io/badge/BAGEL-Website-0A66C2?logo=safari&logoColor=white"
+      alt="BAGEL Website"
+    />
+  </a>
+  <a href="https://arxiv.org/abs/2505.14683">
+    <img
+      src="https://img.shields.io/badge/BAGEL-Paper-red?logo=arxiv&logoColor=red"
+      alt="BAGEL Paper on arXiv"
+    />
+  </a>
+  <a href="https://huggingface.co/ByteDance-Seed/BAGEL-7B-MoT">
+    <img 
+        src="https://img.shields.io/badge/BAGEL-Hugging%20Face-orange?logo=huggingface&logoColor=yellow" 
+        alt="BAGEL on Hugging Face"
+    />
+  </a>
+  <a href="https://demo.bagel-ai.org/">
+    <img
+      src="https://img.shields.io/badge/BAGEL-Demo-blue?logo=googleplay&logoColor=blue"
+      alt="BAGEL Demo"
+    />
+  </a>
+  <a href="https://discord.gg/Z836xxzy">
+    <img
+      src="https://img.shields.io/badge/BAGEL-Discord-5865F2?logo=discord&logoColor=purple"
+      alt="BAGEL Discord"
+    />
+  </a>
+  <a href="mailto:bagel@bytedance.com">
+    <img
+      src="https://img.shields.io/badge/BAGEL-Email-D14836?logo=gmail&logoColor=red"
+      alt="BAGEL Email"
+    />
+  </a>
+</div>
+""")
+    # --- add ram/vram Stats tab ---
+    with gr.Tab("📊 System Monitor"):
+        with gr.Column():
+            memory_stats_display = gr.Markdown("Check RAM/VRAM Stats")
+            refresh_button = gr.Button("🔄 Check RAM/VRAM Stats")
+            
+           # When the button is clicked, the function get_all_memory_stats_for_gradio_display is called.
+           # And update its return value to the memory_stats_display component
+            refresh_button.click(
+                fn=get_all_memory_stats_for_gradio_display,
+                inputs=None, 
+                outputs=[memory_stats_display]
+            )
+    # --- ram/vram Stats end ---
+
+if __name__ == "__main__": 
+    demo.launch(
+        server_name=args.server_name, 
+        server_port=args.server_port,
+        share=args.share, 
+        inbrowser=True,
+    )