app.py

import gradio as gr
import numpy as np
import os
import torch
import torch.quantization
import random
import subprocess
# Install flash-attn without CUDA extensions, which is suitable for CPU-only environments.
subprocess.run(
    "pip install flash-attn --no-build-isolation",
    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
    shell=True,
)

from accelerate import 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 import (
    BagelConfig, Bagel, Qwen2Config, Qwen2ForCausalLM,
    SiglipVisionConfig, SiglipVisionModel
)
from modeling.qwen2 import Qwen2Tokenizer

from huggingface_hub import snapshot_download

# --- Model Download ---
save_dir = "./model_weights"
repo_id = "Wayne-King/SRUM_BAGEL_7B_MoT"
cache_dir = save_dir + "/cache"

snapshot_download(
    cache_dir=cache_dir,
    local_dir=save_dir,
    repo_id=repo_id,
    local_dir_use_symlinks=False,
    resume_download=True,
    allow_patterns=["*.json", "*.safetensors", "*.bin", "*.py", "*.md", "*.txt"],
)

# --- Model Initialization ---
model_path = save_dir
device = "cpu" # Set device to CPU

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"))
# Move VAE model to CPU
vae_model.to(device)


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 for CPU ---
# Removed multi-GPU device mapping logic.
# We will load the entire model onto the CPU.
# Using float32 for better CPU compatibility instead of bfloat16.
model = load_checkpoint_and_dispatch(
    model,
    checkpoint=os.path.join(model_path, "model.safetensors"),
    device_map={"":"cpu"}, # Map all model parts to CPU
    offload_buffers=True,
    offload_folder="offload",
    dtype=torch.float32, # Use float32 for CPU
    force_hooks=True,
).eval()

# --- INT8 Quantization ---
# Apply dynamic quantization to the language model component for CPU optimization.
# This converts the linear layer weights to int8, reducing memory and speeding up inference.
print("Applying INT8 dynamic quantization to the language model...")
model.language_model = torch.quantization.quantize_dynamic(
    model.language_model, {torch.nn.Linear}, dtype=torch.qint8
)
print("Quantization complete.")


# --- 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)
        # Conditional check for CUDA availability (will be false in CPU-only)
        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

# --- Inference Functions (GPU Decorators Removed) ---

# 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,
    )

    result = {"text": "", "image": None}
    # Call inferencer with or without think parameter based on user choice
    for i in inferencer(text=prompt, think=show_thinking, understanding_output=False, **inference_hyper):
        if type(i) == str:
            result["text"] += i
        else:
            result["image"] = i

        yield result["image"], result.get("text", None)


# 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):
    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
    )
    
    result = {"text": "", "image": None}
    # Use show_thinking parameter to control thinking process
    for i in inferencer(image=image, text=prompt, think=show_thinking, 
                        understanding_output=True, **inference_hyper):
        if type(i) == str:
            result["text"] += i
        else:
            result["image"] = i
        yield result["text"]


# 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)
    
    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 = {"text": "", "image": None}
    for i in inferencer(image=image, text=prompt, think=show_thinking, understanding_output=False, **inference_hyper):
        if type(i) == str:
            result["text"] += i
        else:
            result["image"] = i

        yield result["image"], result.get("text", "")

# 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 (Unchanged) ---
with gr.Blocks() as demo:
    gr.Markdown("# 🥯 [BAGEL](https://bagel-ai.org/) - CPU Version")

    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)
        
        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_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")
        
        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]
        )
        
        gr.on(
            triggers=[gen_btn.click, txt_input.submit],
            fn=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
            ],
            outputs=[img_output, thinking_output]
        )

    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)
        
        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")
                
                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")
        
        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]
        )
        
        gr.on(
            triggers=[edit_btn.click, edit_prompt.submit],
            fn=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]
        )

    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)
        
        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")
        
        gr.on(
            triggers=[img_understand_btn.click, understand_prompt.submit],
            fn=image_understanding,
            inputs=[
                img_input, understand_prompt, understand_show_thinking,
                understand_do_sample, understand_text_temperature, understand_max_new_tokens
            ],
            outputs=txt_output
        )


demo.launch(share=True)