"""MiniMax-H3-Turbo-LoRA-Fast — the denoising half of a split MiniMax-H3 deployment.

The 62 GiB Qwen3-VL text encoder runs in a separate conditioner Space; this Space loads the transformer and the
VAEs, folds the selected Turbo/Style LoRA into the bf16 weights, and serves first/last-frame video generation up
to 20 seconds with optional audio.
"""

from __future__ import annotations

import math
import os
import shutil
import subprocess
import tempfile
import time
import traceback
from functools import cache

import spaces
import gradio as gr

MODEL_REPO = os.environ.get("H3_MODEL_REPO", "MiniMaxAI/MiniMax-H3")
CONDITIONER_SPACE = os.environ.get("H3_CONDITIONER", "multimodalart/qwen3vl-conditioner")
PLACEMENT = os.environ.get("H3_PLACEMENT", "pack").lower()
ATTENTION = os.environ.get("H3_ATTENTION", "_native_cudnn").lower()
GPU_SIZE = os.environ.get("H3_GPU_SIZE", "xlarge")

CANVASES = {
    "960x544 · 16:9 fast": (544, 960),
    "1024x576 · 16:9 fast": (576, 1024),
    "1152x640 · 16:9": (640, 1152),
    "1280x704 · 16:9": (704, 1280),
    "1344x768 · 16:9 full": (768, 1344),
    "544x960 · 9:16 fast": (960, 544),
    "640x1152 · 9:16": (1152, 640),
    "768x1344 · 9:16 full": (1344, 768),
    "544x544 · 1:1 fast": (544, 544),
    "768x768 · 1:1 full": (768, 768),
    "768x576 · 4:3 fast": (576, 768),
    "1024x768 · 4:3 full": (768, 1024),
    "576x768 · 3:4 fast": (768, 576),
    "768x1024 · 3:4 full": (1024, 768),
    "1152x512 · 21:9 fast": (512, 1152),
    "1536x672 · 21:9 full": (672, 1536),
}
DEFAULT_CANVAS = "960x544 · 16:9 fast"

FPS, FRAMES_PER_CHUNK, LATENTS_PER_CHUNK = 24, 17, 5

MIN_UI_DURATION = float(os.environ.get("H3_MIN_DURATION", "2"))
MAX_UI_DURATION = float(os.environ.get("H3_MAX_DURATION", "20"))
PIPELINE_MAX_DURATION = MAX_UI_DURATION + 1.0
CHUNK_SECONDS = float(os.environ.get("H3_CHUNK_SECONDS", "14"))
CONDITIONER_MAX_FRAMES = int(os.environ.get("H3_CONDITIONER_MAX_FRAMES", "345"))

GPU_DURATION_CHOICES = (60, 90, 120, 150, 180, 250)
DEFAULT_GPU_DURATION = 120

OUTPUT_DIR = os.path.join(tempfile.gettempdir(), "h3-outputs")

PIPE = None
MUTE_PIPE = None
MANAGER = None
LOAD_ERROR: str | None = None
MUTE_ERROR: str | None = None
LOADED_IN: float | None = None
LORA_STATUS: str | None = None


def snap_frames(seconds: float) -> int:
    frames = max(1, round(float(seconds) * FPS))
    while frames % FRAMES_PER_CHUNK != LATENTS_PER_CHUNK:
        frames += 1
    return frames


def patch_duration_window(minimum: float, maximum: float) -> None:
    from diffusers.modular_pipelines.minimax_h3.modular_pipeline import MiniMaxH3ModularPipeline

    MiniMaxH3ModularPipeline.min_duration = property(lambda self: float(minimum))
    MiniMaxH3ModularPipeline.max_duration = property(lambda self: float(maximum))


def _apply_lora(transformer) -> str | None:
    try:
        import h3_lora

        return h3_lora.apply_lora(transformer)
    except ImportError:
        return "⚠️ `h3_lora.py` is missing — running without the Turbo LoRA. Raise the steps or add the file (README)"
    except Exception as error:
        traceback.print_exc()
        return f"⚠️ LoRA loading failed ({type(error).__name__}: {error}) — running without"


def _set_lora(transformer, name: str) -> str:
    try:
        import h3_lora

        return h3_lora.set_active(transformer, name)
    except Exception:
        return "off"


def load_models() -> str | None:
    global PIPE, MUTE_PIPE, MANAGER, LOAD_ERROR, MUTE_ERROR, LOADED_IN, LORA_STATUS

    if PIPE is not None or LOAD_ERROR is not None:
        return LOAD_ERROR

    started = time.time()
    try:
        import torch
        from diffusers import ComponentsManager

        from h3_split_blocks import MiniMaxH3GeneratorBlocks

        patch_duration_window(MIN_UI_DURATION, PIPELINE_MAX_DURATION)
        manager = ComponentsManager()
        blocks = MiniMaxH3GeneratorBlocks()
        print(f"[gen] loading {[c.name for c in blocks.expected_components]} from {MODEL_REPO} ...", flush=True)
        pipe = blocks.init_pipeline(MODEL_REPO, components_manager=manager, collection="h3")
        pipe.load_components(dtype=torch.bfloat16)

        LORA_STATUS = _apply_lora(pipe.transformer)
        if LORA_STATUS:
            print(f"[gen] {LORA_STATUS}", flush=True)

        pipe.transformer.set_attention_backend(ATTENTION)

        if PLACEMENT == "pack":
            pipe.transformer.to("cuda")
        if PLACEMENT == "offload":
            manager.enable_auto_cpu_offload(device="cuda")
            _arm_decode_hooks(pipe)

        PIPE, MANAGER = pipe, manager
        MUTE_PIPE, MUTE_ERROR = _build_mute_pipe(pipe)
        LOADED_IN = time.time() - started
        print(f"[gen] ready in {LOADED_IN:.0f}s", flush=True)
    except Exception as error:
        traceback.print_exc()
        LOAD_ERROR = (
            f"**Loading `{MODEL_REPO}` failed** after {time.time() - started:.0f}s: "
            f"`{type(error).__name__}: {error}`"
        )
    return LOAD_ERROR


def _build_mute_pipe(pipe):
    try:
        from h3_split_blocks import MiniMaxH3MuteGeneratorBlocks

        blocks = MiniMaxH3MuteGeneratorBlocks()
        mute = blocks.init_pipeline(MODEL_REPO)
        shared = {}
        for spec in blocks.expected_components:
            component = getattr(pipe, spec.name, None)
            if component is not None:
                shared[spec.name] = component
        mute.update_components(**shared)
        print(f"[gen] video-only pipeline over {sorted(shared)}", flush=True)
        return mute, None
    except Exception as error:
        traceback.print_exc()
        return None, f"{type(error).__name__}: {error}"


def _arm_decode_hooks(pipe):
    for name in ("vae", "audio_vae"):
        module = getattr(pipe, name, None)
        if module is None:
            continue
        inner = module.decode

        def armed(*args, _module=module, _decode=inner, **kwargs):
            hook = getattr(_module, "_hf_hook", None)
            if hook is not None:
                hook.pre_forward(_module)
            return _decode(*args, **kwargs)

        module.decode = armed


@cache
def conditioner():
    from gradio_client import Client

    return Client(CONDITIONER_SPACE)


def conditioner_client(ip_token):
    if not ip_token:
        return conditioner()
    from gradio_client import Client

    return Client(CONDITIONER_SPACE, headers={"x-ip-token": ip_token})


def encode_remote(prompt, image_path, last_image_path, canvas, num_frames, rewrite_prompt=False, ip_token=None):
    from gradio_client import handle_file
    from safetensors import safe_open

    requested = int(num_frames)
    wire_frames = min(requested, CONDITIONER_MAX_FRAMES)
    path, plan = conditioner_client(ip_token).predict(
        prompt=prompt,
        image_path=handle_file(image_path) if image_path else None,
        last_image_path=handle_file(last_image_path) if last_image_path else None,
        canvas=canvas,
        num_frames=wire_frames,
        rewrite_prompt=bool(rewrite_prompt),
        api_name="/encode",
    )
    with safe_open(path, framework="pt") as handle:
        metadata = dict(handle.metadata())
        metadata["num_frames"] = str(requested)
        return handle.get_tensor("prompt_embeds"), handle.get_tensor("text_token_tags"), metadata, plan


_DUR_B, _DUR_C = 1.1745e-4, 3.8396e-9
_DECODE_BASE, _DECODE_PER_DEFAULT_CANVAS, _DEFAULT_CANVAS_PIXELS = 15, 15, 960 * 544 * 124
_PLACEMENT_ALLOWANCE, _PAD = 12, 10


def estimate_seconds(height, width, num_frames, steps, num_keyframes=0, with_audio=True) -> int:
    height, width, num_frames, steps = int(height), int(width), int(num_frames), int(steps)
    latent_frames = (num_frames - LATENTS_PER_CHUNK) // FRAMES_PER_CHUNK * LATENTS_PER_CHUNK + 2
    patches = (height // 32) * (width // 32)
    rows = latent_frames * patches + int(num_keyframes) * patches
    denoise = steps * (_DUR_B * rows + _DUR_C * rows**2)
    decode = _DECODE_BASE + _DECODE_PER_DEFAULT_CANVAS * (height * width * num_frames) / _DEFAULT_CANVAS_PIXELS
    if not with_audio:
        decode *= 0.85
    return max(60, int(denoise + decode) + _PLACEMENT_ALLOWANCE + _PAD)


def get_duration(
    prompt_embeds, text_token_tags, image, last_image, height, width, num_frames, steps, seed,
    lora="larry", with_audio=True, gpu_duration=DEFAULT_GPU_DURATION, *a, **k
):
    """Duration callback for `@spaces.GPU`. Honors an explicit user override (`gpu_duration`) when it is one of
    the allowed choices; otherwise falls back to the empirical estimate."""
    if gpu_duration:
        try:
            value = int(gpu_duration)
        except (TypeError, ValueError):
            value = 0
        if value in GPU_DURATION_CHOICES:
            return value
    keyframes = int(image is not None) + int(last_image is not None)
    return estimate_seconds(height, width, num_frames, steps, keyframes, with_audio)


@spaces.GPU(duration=get_duration, size=GPU_SIZE)
def _generate(
    prompt_embeds, text_token_tags, image, last_image, height, width, num_frames, steps, seed,
    lora="larry", with_audio=True, gpu_duration=DEFAULT_GPU_DURATION,
):
    import torch

    active_lora = _set_lora(PIPE.transformer, lora)

    pipe = PIPE if (with_audio or MUTE_PIPE is None) else MUTE_PIPE
    decoded_audio = with_audio or MUTE_PIPE is None

    if PLACEMENT == "lazy":
        PIPE.to("cuda")
    elif PLACEMENT == "pack":
        PIPE.vae.to("cuda")
        if decoded_audio:
            PIPE.audio_vae.to("cuda")

    state = pipe(
        prompt_embeds=prompt_embeds.to("cuda"),
        text_token_tags=text_token_tags,
        image=image,
        last_image=last_image,
        height=int(height),
        width=int(width),
        num_frames=int(num_frames),
        num_inference_steps=int(steps) + 1,
        generator=torch.Generator("cpu").manual_seed(int(seed)),
    )
    frames = state.get("videos")[0]
    if not decoded_audio:
        return frames, None, None, active_lora
    return frames, state.get("audio")[0].cpu(), state.get("sampling_rate"), active_lora


def _fit_keyframe(image_path, current_canvas):
    from PIL import Image as _Image

    img = _Image.open(image_path)
    aspect = img.width / img.height
    fastest = {}
    for label, (h, w) in CANVASES.items():
        r = w / h
        if r not in fastest or w * h < fastest[r][1][0] * fastest[r][1][1]:
            fastest[r] = (label, (h, w))
    ratio = min(fastest, key=lambda r: abs(r - aspect))
    label, (h, w) = fastest[ratio]

    cur_h, cur_w = CANVASES[current_canvas]
    if abs(cur_w / cur_h - aspect) <= abs(ratio - aspect):
        label = current_canvas
        h, w = cur_h, cur_w

    target = w / h
    if abs(img.width / img.height - target) > 1e-3:
        if img.width / img.height > target:
            new_w = int(img.height * target)
            left = (img.width - new_w) // 2
            img = img.crop((left, 0, left + new_w, img.height))
        else:
            new_h = int(img.width / target)
            top = (img.height - new_h) // 2
            img = img.crop((0, top, img.width, top + new_h))
        img.save(image_path)
    return image_path, label


LORA_NAMES = ("larry", "lightx", "lightx8", "realism", "joyfox", "H3-Facial-Realism-CloseUp", "H3-I2V-Anime-Motion", "H3-Rough-2D-Cartoon-Illustration", "off")

def _resolve_lora(lora) -> str:
    if not isinstance(lora, str) or not lora.strip():
        return "larry"
    value = lora.strip().lower()
    lowered = {name.lower(): name for name in LORA_NAMES}
    if value in lowered:
        return lowered[value]
    matches = [name for lower, name in lowered.items() if lower.startswith(value)]
    if len(matches) == 1:
        return matches[0]
    raise gr.Error(f"Unknown LoRA `{lora}`. Allowed: {', '.join(LORA_NAMES)}.")


def _as_path(value):
    if isinstance(value, dict):
        value = value.get("path") or (value.get("url") or "").removeprefix("/gradio_api/file=")
    return value or None


def keyframe(path):
    from PIL import Image, ImageOps

    return ImageOps.exif_transpose(Image.open(path)).convert("RGB") if path else None


def _render_clip(prompt, first, last, canvas, duration, steps, seed, upsample, lora, with_audio, ip_token, work,
                 gpu_duration=DEFAULT_GPU_DURATION):
    from diffusers.utils import encode_video

    num_frames = snap_frames(duration)

    conditioned = time.time()
    prompt_embeds, text_token_tags, metadata, plan = encode_remote(
        prompt, first, last, canvas, num_frames, rewrite_prompt=upsample, ip_token=ip_token
    )
    condition_seconds = time.time() - conditioned
    height, width, num_frames = (int(metadata[key]) for key in ("height", "width", "num_frames"))
    refined = plan.get("refined_prompt") or ""

    started = time.time()
    frames, audio, sampling_rate, active_lora = _generate(
        prompt_embeds, text_token_tags, keyframe(first), keyframe(last),
        height, width, num_frames, steps, seed, lora, with_audio, gpu_duration,
    )
    generate_seconds = time.time() - started

    path = os.path.join(work, f"clip-{int(time.time() * 1000)}.mp4")
    if audio is not None:
        encode_video(frames, fps=FPS, output_path=path, audio=audio, audio_sample_rate=sampling_rate)
    else:
        encode_video(frames, fps=FPS, output_path=path)

    report = (
        f"{width}x{height} · {num_frames} frames ({num_frames / FPS:.3f} s) · {int(steps)} steps · "
        f"conditioner {condition_seconds:.0f}s ({plan['num_text_tokens']} tokens"
        f"{', upsampled' if refined else ''}) · denoise+decode {generate_seconds:.0f}s "
        f"({generate_seconds / int(steps):.1f} s/step) · LoRA {active_lora} · "
        f"audio {'yes' if audio is not None else 'no'} · seed {int(seed)} · GPU booking {int(gpu_duration)}s"
    )
    print(f"[gen] {report}", flush=True)
    return path, frames[-1], report, refined


def _concat(paths, output_path):
    listing = os.path.join(os.path.dirname(output_path), "concat.txt")
    with open(listing, "w", encoding="utf-8") as handle:
        for path in paths:
            handle.write(f"file '{os.path.abspath(path)}'\n")

    base = ["ffmpeg", "-y", "-hide_banner", "-loglevel", "error", "-f", "concat", "-safe", "0", "-i", listing]
    proc = subprocess.run(base + ["-c", "copy", output_path], capture_output=True, text=True)
    if proc.returncode != 0:
        proc = subprocess.run(
            base + ["-r", str(FPS), "-c:v", "libx264", "-pix_fmt", "yuv420p", "-c:a", "aac",
                    "-movflags", "+faststart", output_path],
            capture_output=True, text=True,
        )
        if proc.returncode != 0:
            raise gr.Error(f"ffmpeg concat failed: {proc.stderr[-1500:]}")
    return output_path


def plan_clips(total: float, chunk: float) -> list[float]:
    count = max(1, math.ceil(total / chunk))
    each = total / count
    if each < MIN_UI_DURATION:
        count = max(1, int(total // MIN_UI_DURATION))
        each = total / count
    return [each] * count


CHAIN_SUFFIX = (
    "\n\nContinue seamlessly from the provided first frame. Preserve the same subject identity, clothing, "
    "environment, lighting and camera style; do not cut to a new shot."
)


def generate(
    prompt, first_frame=None, last_frame=None, canvas=DEFAULT_CANVAS, duration=5.0, mode="single",
    steps=6, seed=42, upsample=False, lora="larry", with_audio=True, ip_token=None, progress=None,
    gpu_duration=DEFAULT_GPU_DURATION,
):
    if LOAD_ERROR:
        raise gr.Error(LOAD_ERROR.replace("**", "").replace("`", ""))
    if PIPE is None:
        raise gr.Error("The denoiser is still loading — check the Space logs and try again in a moment.")
    if not prompt or not str(prompt).strip():
        raise gr.Error("MiniMax-H3 always needs a prompt, with or without keyframes.")

    prompt = str(prompt).strip()
    lora = _resolve_lora(lora)
    canvas = canvas if canvas in CANVASES else DEFAULT_CANVAS
    duration = max(MIN_UI_DURATION, min(float(duration), MAX_UI_DURATION))
    with_audio = bool(with_audio)

    try:
        gpu_duration = int(gpu_duration)
    except (TypeError, ValueError):
        gpu_duration = DEFAULT_GPU_DURATION
    if gpu_duration not in GPU_DURATION_CHOICES:
        gpu_duration = min(GPU_DURATION_CHOICES, key=lambda v: abs(v - gpu_duration))

    first, last = _as_path(first_frame), _as_path(last_frame)
    if first:
        first, canvas = _fit_keyframe(first, canvas)
    if last:
        last, canvas = _fit_keyframe(last, canvas)

    os.makedirs(OUTPUT_DIR, exist_ok=True)
    work = tempfile.mkdtemp(prefix="h3-", dir=OUTPUT_DIR)
    durations = [duration] if mode == "single" else plan_clips(duration, CHUNK_SECONDS)

    try:
        segments, reports, refined_all = [], [], []
        carry = first
        for index, clip_duration in enumerate(durations):
            is_last = index == len(durations) - 1
            if progress is not None:
                progress(index / len(durations), desc=f"Clip {index + 1}/{len(durations)} ({clip_duration:.1f}s)")
            clip_prompt = prompt if index == 0 else prompt + CHAIN_SUFFIX
            path, tail, report, refined = _render_clip(
                clip_prompt, carry, last if is_last else None, canvas, clip_duration,
                steps, int(seed) + index, upsample and index == 0, lora, with_audio, ip_token, work, gpu_duration,
            )
            segments.append(path)
            reports.append(f"Clip {index + 1}: {report}")
            if refined:
                refined_all.append(refined)
            if not is_last:
                carry = os.path.join(work, f"handoff-{index}.png")
                tail.save(carry)

        final = os.path.join(OUTPUT_DIR, f"h3-{int(time.time() * 1000)}.mp4")
        if len(segments) == 1:
            shutil.move(segments[0], final)
        else:
            _concat(segments, final)

        summary = (
            f"**{mode}** · {len(durations)} clip(s) · target {duration:.2f}s · "
            f"actual {sum(snap_frames(d) for d in durations) / FPS:.2f}s · GPU booking {gpu_duration}s/clip\n\n"
            + "\n\n".join(reports)
        )
        if mode == "single" and duration > 15:
            summary += "\n\n> Beyond 15 s is outside the H3 training distribution. Try chain mode if the video drifts."
        return final, summary, "\n\n---\n\n".join(refined_all)
    except gr.Error:
        raise
    except Exception as error:
        message = str(error).lower()
        if any(hint in message for hint in ("gpu limit", "quota", "could not allocate", "too many", "concurrent")):
            raise gr.Error(
                "The shared ZeroGPU pool is currently at its limit — this is not a problem with your inputs. "
                "Wait a minute and try again."
            ) from error
        traceback.print_exc()
        raise gr.Error(f"Generation failed: {type(error).__name__}: {error}") from error
    finally:
        shutil.rmtree(work, ignore_errors=True)


def _caller_ip_token(request: gr.Request | None) -> str | None:
    if request is None:
        return None
    headers = getattr(request, "headers", None)
    return headers.get("x-ip-token") if headers is not None else None


def run(prompt, first_frame, last_frame, canvas, duration, mode, steps, seed, upsample, lora, with_audio,
        gpu_duration, request: gr.Request = None, progress=gr.Progress()):
    return generate(
        prompt, first_frame, last_frame, canvas, duration, mode, steps, seed, upsample, lora, with_audio,
        ip_token=_caller_ip_token(request), progress=progress, gpu_duration=gpu_duration,
    )


def cost_hint(canvas, duration, mode, steps, first_frame, last_frame, with_audio, gpu_duration):
    try:
        height, width = CANVASES.get(canvas, CANVASES[DEFAULT_CANVAS])
        keyframes = int(bool(first_frame)) + int(bool(last_frame))
        durations = [float(duration)] if mode == "single" else plan_clips(float(duration), CHUNK_SECONDS)
        estimated = sum(
            estimate_seconds(height, width, snap_frames(d), int(steps), keyframes, bool(with_audio))
            for d in durations
        )
        try:
            gd = int(gpu_duration)
        except (TypeError, ValueError):
            gd = DEFAULT_GPU_DURATION
        booking = gd * len(durations) if gd in GPU_DURATION_CHOICES else estimated
        frames = sum(snap_frames(d) for d in durations)
        parts = [
            f"{width}x{height}",
            f"{frames} frames ({frames / FPS:.2f}s)",
            f"{len(durations)} pass(es)",
            f"~{booking}s GPU booking" + (f" ({gd}s/clip override)" if gd in GPU_DURATION_CHOICES else " (auto-estimate)"),
        ]
        if not with_audio:
            parts.append("audio VAE stays on the host")
        if mode == "single" and float(duration) > 15:
            parts.append("⚠️ >15s extrapolates")
        return " · ".join(parts)
    except Exception as error:
        return f"(estimate unavailable: {error})"


with gr.Blocks(title="MiniMax-H3-Turbo-LoRA-Fast") as demo:
    gr.Markdown("""# MiniMax-H3-Turbo-LoRA-Fast""")
    
    with gr.Row():
        with gr.Column(scale=3):
            prompt = gr.Textbox(
                label="Prompt",
                lines=4,
                value="An extreme close-up shot focuses on a young Asian woman with auburn-tinted hair and silver ear piercings, gazing intensely toward the camera. She subtly blinks and shifts her deep brown eyes with a contemplative expression as soft, warm ambient light highlights fine skin texture, natural blemishes, and delicate facial micro-movements.",
            )
            with gr.Row():
                first_frame = gr.Image(label="First Frame (optional)", type="filepath", height=200)
                last_frame = gr.Image(label="Last Frame (optional)", type="filepath", height=200)
            with gr.Row():
                canvas = gr.Dropdown(label="Canvas", choices=list(CANVASES), value=DEFAULT_CANVAS)
                mode = gr.Radio(
                    label="Duration mode",
                    choices=[("single (1 pass)", "single"), (f"chain (clips ≤ {CHUNK_SECONDS:g}s)", "chain")],
                    value="single",
                )
            duration = gr.Slider(
                MIN_UI_DURATION, MAX_UI_DURATION, value=5, step=0.5,
                label="Length (s)", info="Rounded up to the next 17n+5 at 24 fps.",
            )
            with gr.Row():
                with_audio = gr.Checkbox(label="Generate audio", value=False)
                upsample = gr.Checkbox(label="Prompt upsampling (+15–50s)", value=False)
            with gr.Accordion("Advanced", open=False):
                steps = gr.Slider(2, 12, value=6, step=1, label="Steps")
                lora = gr.Dropdown(label="Turbo/Style-LoRA", choices=list(LORA_NAMES), value="larry")
                seed = gr.Number(label="Seed (clip i = seed + i)", value=42, precision=0)
                gpu_duration = gr.Slider(
                    minimum=GPU_DURATION_CHOICES[0],
                    maximum=GPU_DURATION_CHOICES[-1],
                    value=DEFAULT_GPU_DURATION,
                    step=10,
                    label="GPU Duration (s per clip)",
                    info=f"Allowed: {', '.join(str(v) for v in GPU_DURATION_CHOICES)} s · "
                         f"default {DEFAULT_GPU_DURATION} s · overrides ZeroGPU booking per clip.",
                )
            cost = gr.Markdown()
            run_button = gr.Button("Generate video", variant="primary")

        with gr.Column(scale=2):
            output_video = gr.Video(label="Result", format="mp4", autoplay=True)
            report = gr.Markdown(label="Report")
            refined = gr.Textbox(label="Refined Prompt", lines=4)

    cost_inputs = [canvas, duration, mode, steps, first_frame, last_frame, with_audio, gpu_duration]
    for control in cost_inputs:
        control.change(cost_hint, cost_inputs, cost)
    demo.load(cost_hint, cost_inputs, cost)

    run_button.click(
        run,
        [prompt, first_frame, last_frame, canvas, duration, mode, steps, seed, upsample, lora, with_audio, gpu_duration],
        [output_video, report, refined],
        api_name="generate",
    )


load_models()

if __name__ == "__main__":
    demo.queue(default_concurrency_limit=1, max_size=10).launch(theme=gr.themes.Citrus(), show_error=True, allowed_paths=[OUTPUT_DIR])