app_lora.py

import torch
from diffusers import AutoencoderKLWan, WanImageToVideoPipeline, UniPCMultistepScheduler
from diffusers.utils import export_to_video
from transformers import CLIPVisionModel
import gradio as gr
import tempfile
import os
import subprocess

from huggingface_hub import hf_hub_download
import numpy as np
from PIL import Image
import random

import warnings
warnings.filterwarnings("ignore", message=".*Attempting to use legacy OpenCV backend.*")
warnings.filterwarnings("ignore", message=".*num_frames - 1.*")

# This decorator is specific to HuggingFace Spaces and will cause an error in other environments.
# import spaces

MODEL_ID = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"

LORA_REPO_ID = "vrgamedevgirl84/Wan14BT2VFusioniX"
LORA_FILENAME = "FusionX_LoRa/Wan2.1_I2V_14B_FusionX_LoRA.safetensors"

# Initialize models with proper dtype handling
# This section requires a GPU and CUDA to be available
pipe = None
if torch.cuda.is_available():
    image_encoder = CLIPVisionModel.from_pretrained(MODEL_ID, subfolder="image_encoder", torch_dtype=torch.float16)
    vae = AutoencoderKLWan.from_pretrained(MODEL_ID, subfolder="vae", torch_dtype=torch.float16)
    pipe = WanImageToVideoPipeline.from_pretrained(
        MODEL_ID, vae=vae, image_encoder=image_encoder, torch_dtype=torch.float16
    )
    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=8.0)

    # Enable memory efficient attention and CPU offloading for large videos
    pipe.enable_model_cpu_offload()

    try:
        causvid_path = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME)
        print("✅ LoRA downloaded to:", causvid_path)

        pipe.load_lora_weights(causvid_path, adapter_name="causvid_lora")
        pipe.set_adapters(["causvid_lora"], adapter_weights=[0.75])
        pipe.fuse_lora()

    except Exception as e:
        import traceback
        print("❌ Error during LoRA loading:")
        traceback.print_exc()
else:
    print("CUDA is not available. This script requires a GPU to run.")


MOD_VALUE = 32
DEFAULT_H_SLIDER_VALUE = 640
DEFAULT_W_SLIDER_VALUE = 1024
NEW_FORMULA_MAX_AREA = 640.0 * 1024.0

SLIDER_MIN_H, SLIDER_MAX_H = 128, 1024
SLIDER_MIN_W, SLIDER_MAX_W = 128, 1024
MAX_SEED = np.iinfo(np.int32).max

FIXED_FPS = 24
MIN_FRAMES_MODEL = 8  # Minimum 8 frames (~0.33s)
MAX_FRAMES_MODEL = 240  # Maximum 240 frames (10 seconds at 24fps)

default_prompt_i2v = "make this image come alive, cinematic motion, smooth animation"
default_negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards, watermark, text, signature"


def _calculate_new_dimensions_wan(pil_image, mod_val, calculation_max_area,
                                 min_slider_h, max_slider_h,
                                 min_slider_w, max_slider_w,
                                 default_h, default_w):
    orig_w, orig_h = pil_image.size
    if orig_w <= 0 or orig_h <= 0:
        return default_h, default_w

    aspect_ratio = orig_h / orig_w

    calc_h = round(np.sqrt(calculation_max_area * aspect_ratio))
    calc_w = round(np.sqrt(calculation_max_area / aspect_ratio))

    calc_h = max(mod_val, (calc_h // mod_val) * mod_val)
    calc_w = max(mod_val, (calc_w // mod_val) * mod_val)

    new_h = int(np.clip(calc_h, min_slider_h, (max_slider_h // mod_val) * mod_val))
    new_w = int(np.clip(calc_w, min_slider_w, (max_slider_w // mod_val) * mod_val))

    return new_h, new_w

def handle_image_upload_for_dims_wan(uploaded_pil_image, current_h_val, current_w_val):
    if uploaded_pil_image is None:
        return gr.update(value=DEFAULT_H_SLIDER_VALUE), gr.update(value=DEFAULT_W_SLIDER_VALUE)
    try:
        new_h, new_w = _calculate_new_dimensions_wan(
            uploaded_pil_image, MOD_VALUE, NEW_FORMULA_MAX_AREA,
            SLIDER_MIN_H, SLIDER_MAX_H, SLIDER_MIN_W, SLIDER_MAX_W,
            DEFAULT_H_SLIDER_VALUE, DEFAULT_W_SLIDER_VALUE
        )
        return gr.update(value=new_h), gr.update(value=new_w)
    except Exception as e:
        gr.Warning("Error attempting to calculate new dimensions")
        return gr.update(value=DEFAULT_H_SLIDER_VALUE), gr.update(value=DEFAULT_W_SLIDER_VALUE)


def export_video_with_ffmpeg(frames, output_path, fps=24):
    """Export video using imageio if available, otherwise fall back to OpenCV"""
    try:
        import imageio
        writer = imageio.get_writer(output_path, fps=fps, codec='libx264',
                                   pixelformat='yuv420p', quality=8)
        for frame in frames:
            writer.append_data(np.array(frame))
        writer.close()
        return True
    except ImportError:
        export_to_video(frames, output_path, fps=fps)
        return False

def generate_video(input_image, prompt, height, width,
                   negative_prompt=default_negative_prompt, duration_seconds=2,
                   guidance_scale=1, steps=4,
                   seed=42, randomize_seed=False,
                   progress=gr.Progress(track_tqdm=True)):

    if pipe is None or not torch.cuda.is_available():
        raise gr.Error("Pipeline not initialized or CUDA not available. Please check the console for errors.")

    if input_image is None:
        raise gr.Error("Please upload an input image.")

    target_h = max(MOD_VALUE, (int(height) // MOD_VALUE) * MOD_VALUE)
    target_w = max(MOD_VALUE, (int(width) // MOD_VALUE) * MOD_VALUE)

    raw_frames = int(round(duration_seconds * FIXED_FPS))
    num_frames = ((raw_frames - 1) // 4) * 4 + 1
    num_frames = np.clip(num_frames, MIN_FRAMES_MODEL, MAX_FRAMES_MODEL)

    if num_frames > 120:
        max_dim = max(target_h, target_w)
        if max_dim > 768:
            scale_factor = 768 / max_dim
            target_h = max(MOD_VALUE, (int(target_h * scale_factor) // MOD_VALUE) * MOD_VALUE)
            target_w = max(MOD_VALUE, (int(target_w * scale_factor) // MOD_VALUE) * MOD_VALUE)
            gr.Info(f"Reduced resolution to {target_w}x{target_h} for long video generation")

    print(f"Generating {num_frames} frames (requested {raw_frames}) at {target_w}x{target_h}")

    current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
    resized_image = input_image.resize((target_w, target_h), Image.Resampling.LANCZOS)

    torch.cuda.empty_cache()

    try:
        with torch.inference_mode():
            with torch.autocast("cuda", dtype=torch.float16):
                output_frames_list = pipe(
                    image=resized_image, prompt=prompt, negative_prompt=negative_prompt,
                    height=target_h, width=target_w, num_frames=num_frames,
                    guidance_scale=float(guidance_scale), num_inference_steps=int(steps),
                    generator=torch.Generator(device="cuda").manual_seed(current_seed),
                    return_dict=True
                ).frames[0]
    except torch.cuda.OutOfMemoryError:
        torch.cuda.empty_cache()
        raise gr.Error("Out of GPU memory. Try reducing the duration or resolution.")
    except Exception as e:
        torch.cuda.empty_cache()
        raise gr.Error(f"Generation failed: {str(e)}")

    torch.cuda.empty_cache()

    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
        video_path = tmpfile.name
        export_video_with_ffmpeg(output_frames_list, video_path, fps=FIXED_FPS)

        if os.path.exists(video_path) and os.path.getsize(video_path) > 0:
            try:
                subprocess.run(['ffmpeg', '-version'], capture_output=True, check=True)
                optimized_path = video_path + "_opt.mp4"
                cmd = [
                    'ffmpeg', '-y', '-i', video_path, '-c:v', 'libx264', '-pix_fmt', 'yuv420p',
                    '-profile:v', 'main', '-level', '4.0', '-movflags', '+faststart', '-crf', '23',
                    '-preset', 'medium', '-maxrate', '10M', '-bufsize', '20M', optimized_path
                ]
                result = subprocess.run(cmd, capture_output=True, text=True)
                if result.returncode == 0 and os.path.exists(optimized_path) and os.path.getsize(optimized_path) > 0:
                    os.unlink(video_path)
                    video_path = optimized_path
                else:
                    print(f"FFmpeg optimization failed: {result.stderr}")
            except (subprocess.CalledProcessError, FileNotFoundError):
                print("FFmpeg not available or optimization failed, using original export")

    return video_path, current_seed

# Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown("# Fast 4 steps Wan 2.1 I2V (14B) FusionX-LoRA")
    gr.Markdown("Generate videos up to 10 seconds long! Longer videos may use reduced resolution for stability.")

    with gr.Row():
        with gr.Column():
            input_image_component = gr.Image(type="pil", label="Input Image (auto-resized to target H/W)")
            prompt_input = gr.Textbox(label="Prompt", value=default_prompt_i2v)
            duration_seconds_input = gr.Slider(
                minimum=round(MIN_FRAMES_MODEL/FIXED_FPS, 1),
                maximum=round(MAX_FRAMES_MODEL/FIXED_FPS, 1),
                step=0.1, value=2, label="Duration (seconds)",
                info=f"Video length: {MIN_FRAMES_MODEL/FIXED_FPS:.1f}-{MAX_FRAMES_MODEL/FIXED_FPS:.1f}s."
            )
            with gr.Accordion("Advanced Settings", open=False):
                negative_prompt_input = gr.Textbox(label="Negative Prompt", value=default_negative_prompt, lines=3)
                seed_input = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, interactive=True)
                randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=True, interactive=True)
                with gr.Row():
                    height_input = gr.Slider(minimum=SLIDER_MIN_H, maximum=SLIDER_MAX_H, step=MOD_VALUE, value=DEFAULT_H_SLIDER_VALUE, label=f"Output Height (multiple of {MOD_VALUE})")
                    width_input = gr.Slider(minimum=SLIDER_MIN_W, maximum=SLIDER_MAX_W, step=MOD_VALUE, value=DEFAULT_W_SLIDER_VALUE, label=f"Output Width (multiple of {MOD_VALUE})")
                steps_slider = gr.Slider(minimum=1, maximum=30, step=1, value=4, label="Inference Steps")
                guidance_scale_input = gr.Slider(minimum=0.0, maximum=20.0, step=0.5, value=1.0, label="Guidance Scale", visible=False)

            generate_button = gr.Button("Generate Video", variant="primary")
        with gr.Column():
            video_output = gr.Video(label="Generated Video", autoplay=True, interactive=False)
            gr.Markdown("### Tips for best results:\n- For videos longer than 5 seconds, consider using lower resolutions (512-768px)\n- Clear, simple prompts often work better than complex descriptions\n- The model works best with 4-8 inference steps")

    input_image_component.upload(
        fn=handle_image_upload_for_dims_wan,
        inputs=[input_image_component, height_input, width_input],
        outputs=[height_input, width_input]
    )
    input_image_component.clear(
        fn=handle_image_upload_for_dims_wan,
        inputs=[input_image_component, height_input, width_input],
        outputs=[height_input, width_input]
    )

    ui_inputs = [
        input_image_component, prompt_input, height_input, width_input,
        negative_prompt_input, duration_seconds_input,
        guidance_scale_input, steps_slider, seed_input, randomize_seed_checkbox
    ]
    generate_button.click(fn=generate_video, inputs=ui_inputs, outputs=[video_output, seed_input])
    
    # The example images 'peng.png' and 'forg.jpg' are not present in this environment,
    # so the gr.Examples component is commented out to prevent errors.
    # gr.Examples(
    #     examples=[
    #         ["path/to/your/peng.png", "a penguin playfully dancing in the snow, Antarctica", 896, 512],
    #         ["path/to/your/forg.jpg", "the frog jumps around", 448, 832],
    #     ],
    #     inputs=[input_image_component, prompt_input, height_input, width_input],
    #     outputs=[video_output, seed_input],
    #     fn=generate_video,
    #     cache_examples="lazy"
    # )

if __name__ == "__main__":
    if pipe is not None:
        demo.queue(max_size=3).launch()
    else:
        gr.Blocks()._queue_closed = False # A hack to prevent Gradio from hanging
        gr.Info("Application not started because a GPU (CUDA) is required but not found.")