app.py

import spaces
import gradio as gr
import torch
import numpy as np
import os
import tempfile
from diffusers import DiffusionPipeline
from diffusers.quantizers import PipelineQuantizationConfig
from diffusers.utils.export_utils import export_to_video

# Constants
LANDSCAPE_WIDTH = 832
LANDSCAPE_HEIGHT = 480
MAX_SEED = np.iinfo(np.int32).max
FIXED_FPS = 16
MIN_FRAMES_MODEL = 8
MAX_FRAMES_MODEL = 81
T2V_FIXED_FPS = 16
MIN_DURATION = round(MIN_FRAMES_MODEL / FIXED_FPS, 1)
MAX_DURATION = round(MAX_FRAMES_MODEL / FIXED_FPS, 1)

# Checkpoint ID
ckpt_id = "Wan-AI/Wan2.1-T2V-14B-Diffusers"

# Quantization config
quant_config = PipelineQuantizationConfig(
    quant_backend="bitsandbytes_4bit",
    quant_kwargs={
        "load_in_4bit": True,
        "bnb_4bit_quant_type": "nf4",
        "bnb_4bit_compute_dtype": torch.bfloat16,
    },
    components_to_quantize=["transformer", "text_encoder"],
)

# Load pipeline
pipe = DiffusionPipeline.from_pretrained(
    ckpt_id,
    quantization_config=quant_config,
    torch_dtype=torch.bfloat16,
)
pipe.enable_model_cpu_offload()

# Duration estimator
def get_duration(prompt, height, width, negative_prompt, duration_seconds, guidance_scale, steps, seed, randomize_seed, progress):
    return steps * 18 if duration_seconds <= 2.5 else steps * 25

# Inference function
@spaces.GPU(duration=get_duration)
def generate_video(prompt, height, width, negative_prompt, duration_seconds,
                   guidance_scale, steps, seed, randomize_seed,
                   progress=gr.Progress(track_tqdm=True)):
    
    num_frames = np.clip(int(round(duration_seconds * FIXED_FPS)),
                         MIN_FRAMES_MODEL, MAX_FRAMES_MODEL)
    current_seed = np.random.randint(0, MAX_SEED) if randomize_seed else int(seed)

    output_frames_list = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        height=int(height),
        width=int(width),
        num_frames=num_frames,
        guidance_scale=float(guidance_scale),
        num_inference_steps=int(steps),
        generator=torch.manual_seed(current_seed),
    ).frames[0]

    temp_dir = tempfile.mkdtemp()
    video_path = os.path.join(temp_dir, "t2v_output.mp4")
    export_to_video(output_frames_list, video_path, fps=T2V_FIXED_FPS)

    print(f"✅ Video saved to: {video_path}")
    return video_path  # Only return video

# Gradio UI
with gr.Blocks(css="body { max-width: 100vw; overflow-x: hidden; }") as demo:
    gr.Markdown("## 🚀 Wan2.1 T2V - Text to Video Generator (Quantized, Smart Duration)")
    with gr.Row():
        with gr.Column():
            prompt_input = gr.Textbox(label="Prompt", lines=3, value="A futuristic cityscape with flying cars and neon lights.")
            negative_prompt_input = gr.Textbox(label="Negative Prompt", lines=3, value="")
            height_input = gr.Slider(256, 1024, step=8, value=512, label="Height")
            width_input = gr.Slider(256, 1024, step=8, value=512, label="Width")
            duration_input = gr.Slider(1, 10, value=2, step=0.1, label="Duration (seconds)")
            steps_input = gr.Slider(1, 50, value=20, step=1, label="Inference Steps")
            guidance_scale_input = gr.Slider(0.0, 20.0, step=0.5, value=7.5, label="Guidance Scale")
            seed_input = gr.Number(value=42, label="Seed (optional)")
            randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=True)
            run_btn = gr.Button("Generate Video")
        with gr.Column():
            output_video = gr.Video(label="Generated Video")

    ui_inputs = [
        prompt_input, height_input, width_input, negative_prompt_input,
        duration_input, guidance_scale_input, steps_input, seed_input,
        randomize_seed_checkbox
    ]
    run_btn.click(fn=generate_video, inputs=ui_inputs, outputs=output_video)

# Launch
demo.launch()