app.py

import os
import sys
import time
import torch
import gradio as gr
import numpy as np
import imageio
import spaces
from PIL import Image

# Add project root to path
# current_file_path = os.path.abspath(__file__)
# project_root = os.path.dirname(os.path.dirname(current_file_path))
# if project_root not in sys.path:
#    sys.path.insert(0, project_root)

from videox_fun.ui.wan_ui import Wan_Controller, css
from videox_fun.ui.ui import (
    create_model_type, create_model_checkpoints, create_finetune_models_checkpoints,
    create_teacache_params, create_cfg_skip_params, create_cfg_riflex_k,
    create_prompts, create_samplers, create_height_width,
    create_generation_methods_and_video_length, create_generation_method,
    create_cfg_and_seedbox, create_ui_outputs
)
from videox_fun.data.dataset_image_video import derive_ground_object_from_instruction
from videox_fun.utils.lora_utils import merge_lora, unmerge_lora
from videox_fun.utils.utils import save_videos_grid, timer

# Redefine create_height_width to remove Chinese and specific defaults if needed, 
# although we will mostly ignore sliders if we use input resolution.
# We will create a custom version here to avoid modifying the library file if possible,
# or we just rely on `create_height_width` and update labels.
# But `create_height_width` is imported. Let's override it or create a new one.

def create_height_width_english(default_height, default_width, maximum_height, maximum_width):
    resize_method = gr.Radio(
        ["Generate by", "Resize according to Reference"],
        value="Generate by",
        show_label=False,
        visible=False # Hide since we force input resolution
    )
    # We keep sliders visible but maybe we can update them dynamically or just ignore them?
    # User requested "input is whatever resolution, inference is whatever resolution".
    # So we can hide these or just label them as "Default / Override if no video".
    # But better to hide them if we always use video resolution.
    # However, if no video is provided (which shouldn't happen for VideoCoF), we might need them.
    # Let's keep them but make them less prominent or explain.
    # Actually user said "no default 480x832", implying don't force it.
    
    width_slider     = gr.Slider(label="Width", value=default_width, minimum=128, maximum=maximum_width, step=16, visible=False)
    height_slider    = gr.Slider(label="Height", value=default_height, minimum=128, maximum=maximum_height, step=16, visible=False)
    base_resolution  = gr.Radio(label="Base Resolution", value=512, choices=[512, 640, 768, 896, 960, 1024], visible=False)

    return resize_method, width_slider, height_slider, base_resolution

def load_video_frames(video_path: str, source_frames: int):
    assert source_frames is not None, "source_frames is required"
    
    reader = imageio.get_reader(video_path)
    try:
        total_frames = reader.count_frames()
    except Exception:
        total_frames = sum(1 for _ in reader)
        reader = imageio.get_reader(video_path)

    stride = max(1, total_frames // source_frames)
    # Using random start frame as in inference.py
    start_frame = torch.randint(0, max(1, total_frames - stride * source_frames), (1,))[0].item()

    frames = []
    original_height, original_width = None, None

    for i in range(source_frames):
        idx = start_frame + i * stride
        if idx >= total_frames:
            break
        try:
            frame = reader.get_data(idx)
            pil_frame = Image.fromarray(frame)
            if original_height is None:
                original_width, original_height = pil_frame.size
            frames.append(pil_frame)
        except IndexError:
            break

    reader.close()

    while len(frames) < source_frames:
        if frames:
            frames.append(frames[-1].copy())
        else:
            w, h = (original_width, original_height) if original_width else (832, 480)
            frames.append(Image.new('RGB', (w, h), (0, 0, 0)))

    input_video = torch.from_numpy(np.array(frames))
    input_video = input_video.permute([3, 0, 1, 2]).unsqueeze(0).float()
    input_video = input_video * (2.0 / 255.0) - 1.0

    return input_video, original_height, original_width

class VideoCoF_Controller(Wan_Controller):
    @spaces.GPU(duration=300)
    @timer
    def generate(
        self,
        diffusion_transformer_dropdown,
        base_model_dropdown,
        lora_model_dropdown, 
        lora_alpha_slider,
        prompt_textbox, 
        negative_prompt_textbox, 
        sampler_dropdown, 
        sample_step_slider, 
        resize_method,
        width_slider, 
        height_slider, 
        base_resolution, 
        generation_method, 
        length_slider, 
        overlap_video_length, 
        partial_video_length, 
        cfg_scale_slider, 
        start_image, 
        end_image, 
        validation_video,
        validation_video_mask,
        control_video,
        denoise_strength,
        seed_textbox,
        ref_image=None,
        # Custom args
        source_frames_slider=33,
        reasoning_frames_slider=4,
        repeat_rope_checkbox=True,
        # New arg for acceleration
        enable_acceleration=False,
        fps=8,
        is_api=False,
    ):
        self.clear_cache()
        print(f"VideoCoF Generation started.")
        
        # Ensure model is on CUDA inside the zero-gpu decorated function
        if torch.cuda.is_available():
            self.device = torch.device("cuda")
            # If pipeline is not on cuda, move it (if possible, but usually accelerate handles this or it's handled by parts)
            # However, Wan_Controller logic might rely on `self.device`.
            # We explicitly set `self.device` to cuda here.
        
        if self.diffusion_transformer_dropdown != diffusion_transformer_dropdown:
            self.update_diffusion_transformer(diffusion_transformer_dropdown)

        if self.base_model_path != base_model_dropdown:
            self.update_base_model(base_model_dropdown)

        if self.lora_model_path != lora_model_dropdown:
            self.update_lora_model(lora_model_dropdown)

        # Scheduler setup
        scheduler_config = self.pipeline.scheduler.config
        if sampler_dropdown in ["Flow_Unipc", "Flow_DPM++"]:
            scheduler_config['shift'] = 1
        self.pipeline.scheduler = self.scheduler_dict[sampler_dropdown].from_config(scheduler_config)

        # LoRA merging
        # 1. Merge VideoCoF LoRA
        if self.lora_model_path != "none":
            print(f"Merge VideoCoF Lora: {self.lora_model_path}")
            self.pipeline = merge_lora(self.pipeline, self.lora_model_path, multiplier=lora_alpha_slider)

        # 2. Merge Acceleration LoRA (FusionX) if enabled
        acc_lora_path = os.path.join(self.personalized_model_dir, "Wan2.1_Text_to_Video_14B_FusionX_LoRA.safetensors")
        if enable_acceleration:
            if os.path.exists(acc_lora_path):
                print(f"Merge Acceleration LoRA: {acc_lora_path}")
                # FusionX LoRA generally uses multiplier 1.0
                self.pipeline = merge_lora(self.pipeline, acc_lora_path, multiplier=1.0)
            else:
                print(f"Warning: Acceleration LoRA not found at {acc_lora_path}")

        # Seed
        if int(seed_textbox) != -1 and seed_textbox != "": 
            torch.manual_seed(int(seed_textbox))
        else: 
            seed_textbox = np.random.randint(0, 1e10)
        generator = torch.Generator(device=self.device).manual_seed(int(seed_textbox))

        try:
            # VideoCoF logic
            # Use validation_video as source if provided (UI standard for Video-to-Video)
            input_video_path = validation_video
            
            if input_video_path is None:
                # Fallback to control_video if set, but standard UI uses validation_video
                input_video_path = control_video

            if input_video_path is None:
                raise ValueError("Please upload a video for VideoCoF generation.")

            # CoT Prompt Construction
            edit_text = prompt_textbox
            ground_instr = derive_ground_object_from_instruction(edit_text)
            prompt = (
                "A video sequence showing three parts: first the original scene, "
                f"then grounded {ground_instr}, and finally the same scene but {edit_text}"
            )
            print(f"Constructed prompt: {prompt}")

            # Load video frames
            input_video_tensor, video_height, video_width = load_video_frames(
                input_video_path,
                source_frames=source_frames_slider
            )

            # Using loaded video dimensions
            h, w = video_height, video_width
            print(f"Input video dimensions: {w}x{h}")

            print(f"Running pipeline with frames={length_slider}, source={source_frames_slider}, reasoning={reasoning_frames_slider}")
            
            sample = self.pipeline(
                video=input_video_tensor,
                prompt=prompt,
                num_frames=length_slider,
                source_frames=source_frames_slider,
                reasoning_frames=reasoning_frames_slider,
                negative_prompt=negative_prompt_textbox,
                height=h,
                width=w,
                generator=generator,
                guidance_scale=cfg_scale_slider,
                num_inference_steps=sample_step_slider,
                repeat_rope=repeat_rope_checkbox,
                cot=True,
            ).videos

            final_video = sample

        except Exception as e:
            print(f"Error: {e}")
            # Unmerge in case of error (LIFO order)
            if enable_acceleration and os.path.exists(acc_lora_path):
                 print("Unmerging Acceleration LoRA (due to error)")
                 self.pipeline = unmerge_lora(self.pipeline, acc_lora_path, multiplier=1.0)
            
            if self.lora_model_path != "none":
                 print("Unmerging VideoCoF LoRA (due to error)")
                 self.pipeline = unmerge_lora(self.pipeline, self.lora_model_path, multiplier=lora_alpha_slider)
            return gr.update(), gr.update(), f"Error: {str(e)}"

        # Unmerge LoRAs (LIFO order)
        if enable_acceleration and os.path.exists(acc_lora_path):
            print("Unmerging Acceleration LoRA")
            self.pipeline = unmerge_lora(self.pipeline, acc_lora_path, multiplier=1.0)

        if self.lora_model_path != "none":
            print("Unmerging VideoCoF LoRA")
            self.pipeline = unmerge_lora(self.pipeline, self.lora_model_path, multiplier=lora_alpha_slider)

        # Save output
        save_sample_path = self.save_outputs(
            False, length_slider, final_video, fps=fps
        )
        
        # Return input video to display it alongside output if needed? 
        # But generate returns [result_image, result_video, infer_progress].
        # The user said "load original video didn't display".
        # That usually refers to the input component not showing the video after upload or example selection.
        # Grado handles that automatically if `value` is set or user uploads.
        # Maybe they mean the `validation_video` component didn't show the example?
        # Or do they mean they want to see the processed input frames?
        # "load 原视频没有display 出来" -> "Loaded original video didn't display".
        # Likely referring to the input UI component.
        # If they mean they want to see it in the output area, we can't easily change the return signature without changing UI structure.
        # But let's ensure the input component works.
        
        return gr.Image(visible=False, value=None), gr.Video(value=save_sample_path, visible=True), "Success"

def ui(GPU_memory_mode, scheduler_dict, config_path, compile_dit, weight_dtype):
    controller = VideoCoF_Controller(
        GPU_memory_mode, scheduler_dict, model_name=None, model_type="Inpaint", 
        config_path=config_path, compile_dit=compile_dit,
        weight_dtype=weight_dtype
    )

    with gr.Blocks() as demo:
        gr.Markdown("# VideoCoF Demo")
        
        with gr.Column(variant="panel"):
            # Hide model selection
            diffusion_transformer_dropdown, _ = create_model_checkpoints(controller, visible=False, default_model="Wan-AI/Wan2.1-T2V-14B")
            
            # Use snapshot download for the VideoCoF repo to get all weights (including safetensors)
            try:
                from huggingface_hub import snapshot_download, hf_hub_download
                print("Downloading Wan2.1-T2V-14B weights...")
                snapshot_download(repo_id="Wan-AI/Wan2.1-T2V-14B", local_dir="Wan-AI/Wan2.1-T2V-14B")
                
                os.makedirs("models/Personalized_Model", exist_ok=True)
                
                print("Downloading VideoCoF weights...")
                hf_hub_download(repo_id="XiangpengYang/VideoCoF", filename="videocof.safetensors", local_dir="models/Personalized_Model")
                
                print("Downloading FusionX Acceleration LoRA...")
                hf_hub_download(repo_id="MonsterMMORPG/Wan_GGUF", filename="Wan2.1_Text_to_Video_14B_FusionX_LoRA.safetensors", local_dir="models/Personalized_Model")
                
            except Exception as e:
                print(f"Warning: Failed to pre-download weights: {e}")

            base_model_dropdown, lora_model_dropdown, lora_alpha_slider, _ = create_finetune_models_checkpoints(controller, visible=False, default_lora="videocof.safetensors")
            
            # Set default LoRA alpha to 1.0 (matching inference.py)
            lora_alpha_slider.value = 1.0

        with gr.Column(variant="panel"):
            prompt_textbox, negative_prompt_textbox = create_prompts(prompt="Remove the young man with short black hair wearing black shirt on the left.")
            
            with gr.Row():
                with gr.Column():
                    sampler_dropdown, sample_step_slider = create_samplers(controller)
                    
                    # Default steps lowered to 4 for acceleration
                    sample_step_slider.value = 4
                    
                    # Custom VideoCoF Params
                    with gr.Group():
                        gr.Markdown("### VideoCoF Parameters")
                        source_frames_slider = gr.Slider(label="Source Frames", minimum=1, maximum=100, value=33, step=1)
                        reasoning_frames_slider = gr.Slider(label="Reasoning Frames", minimum=1, maximum=20, value=4, step=1)
                        repeat_rope_checkbox = gr.Checkbox(label="Repeat RoPE", value=True)
                        # Add Acceleration Checkbox
                        enable_acceleration = gr.Checkbox(label="Enable 4-step Acceleration (FusionX LoRA)", value=False)
                        
                    # Use custom height/width creation to hide/customize
                    resize_method, width_slider, height_slider, base_resolution = create_height_width_english(
                        default_height=480, default_width=832, maximum_height=1344, maximum_width=1344
                    )
                    
                    # Default video length 65
                    generation_method, length_slider, overlap_video_length, partial_video_length = \
                        create_generation_methods_and_video_length(
                            ["Video Generation"], 
                            default_video_length=65, 
                            maximum_video_length=161
                        )
                    
                    # Simplified input for VideoCoF - mainly Video to Video.
                    image_to_video_col, video_to_video_col, control_video_col, source_method, start_image, template_gallery, end_image, validation_video, validation_video_mask, denoise_strength, control_video, ref_image = create_generation_method(
                        ["Video to Video"], prompt_textbox, support_end_image=False, default_video="assets/two_man.mp4",
                        video_examples=[
                            ["assets/two_man.mp4", "Remove the young man with short black hair wearing black shirt on the left."],
                            ["assets/sign.mp4", "Replace the yellow \"SCHOOL\" sign with a red hospital sign, featuring a white hospital emblem on the top and the word \"HOSPITAL\" below."]
                        ]
                    )
                    
                    # Ensure validation_video is visible and interactive
                    validation_video.visible = True
                    validation_video.interactive = True

                    # Set default seed to 0
                    cfg_scale_slider, seed_textbox, seed_button = create_cfg_and_seedbox(True)
                    seed_textbox.value = "0"
                    cfg_scale_slider.value = 1.0
                    
                    generate_button = gr.Button(value="Generate", variant='primary')

                result_image, result_video, infer_progress = create_ui_outputs()

        # Event handlers
        generate_button.click(
            fn=controller.generate,
            inputs=[
                diffusion_transformer_dropdown,
                base_model_dropdown,
                lora_model_dropdown, 
                lora_alpha_slider,
                prompt_textbox, 
                negative_prompt_textbox, 
                sampler_dropdown, 
                sample_step_slider, 
                resize_method,
                width_slider, 
                height_slider, 
                base_resolution, 
                generation_method, 
                length_slider, 
                overlap_video_length, 
                partial_video_length, 
                cfg_scale_slider, 
                start_image, 
                end_image, 
                validation_video, 
                validation_video_mask,
                control_video,
                denoise_strength, 
                seed_textbox,
                ref_image, 
                # New inputs
                source_frames_slider,
                reasoning_frames_slider,
                repeat_rope_checkbox,
                enable_acceleration
            ],
            outputs=[result_image, result_video, infer_progress]
        )

    return demo, controller

if __name__ == "__main__":
    from videox_fun.ui.controller import flow_scheduler_dict
    
    GPU_memory_mode = "sequential_cpu_offload"
    compile_dit = False
    weight_dtype = torch.bfloat16
    server_name = "0.0.0.0"
    server_port = 7860
    config_path = "config/wan2.1/wan_civitai.yaml"

    demo, controller = ui(GPU_memory_mode, flow_scheduler_dict, config_path, compile_dit, weight_dtype)
    
    demo.queue(status_update_rate=1).launch(
        server_name=server_name,
        server_port=server_port,
        prevent_thread_lock=True,
        share=False
    )
    
    while True:
        time.sleep(5)