app.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 spaces
from huggingface_hub import hf_hub_download
import numpy as np
from PIL import Image
import random

MODEL_ID = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
LORA_REPO_ID = "Kijai/WanVideo_comfy"
LORA_FILENAME = "Wan21_CausVid_14B_T2V_lora_rank32.safetensors"

image_encoder = CLIPVisionModel.from_pretrained(MODEL_ID, subfolder="image_encoder", torch_dtype=torch.float32)
vae = AutoencoderKLWan.from_pretrained(MODEL_ID, subfolder="vae", torch_dtype=torch.float32)
pipe = WanImageToVideoPipeline.from_pretrained(
    MODEL_ID, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=8.0)
pipe.to("cuda")

causvid_path = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME)
pipe.load_lora_weights(causvid_path, adapter_name="causvid_lora")
pipe.set_adapters(["causvid_lora"], adapter_weights=[0.95])
pipe.fuse_lora()

MOD_VALUE = 32
DEFAULT_H_SLIDER_VALUE = 320
DEFAULT_W_SLIDER_VALUE = 560
NEW_FORMULA_MAX_AREA = 480.0 * 832.0 

SLIDER_MIN_H, SLIDER_MAX_H = 128, 896
SLIDER_MIN_W, SLIDER_MAX_W = 128, 896
MAX_SEED = np.iinfo(np.int32).max

FIXED_FPS = 24
MIN_FRAMES_MODEL = 8
MAX_FRAMES_MODEL = 120

default_prompt_i2v = "Сделайте это изображение живым с кинематографичными движениями, плавной анимацией."
# Красивая девушка крепко связана. Она не может двигать руками и ногами. Она поворачивает голову и смотрит в камеру." 
#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("Ошибка при попытке вычисления новых размеров")
        return gr.update(value=DEFAULT_H_SLIDER_VALUE), gr.update(value=DEFAULT_W_SLIDER_VALUE)

def get_duration(input_image, prompt, height, width, 
                   negative_prompt, duration_seconds,
                   guidance_scale, steps,
                   seed, randomize_seed, 
                   progress):
    if steps > 4 and duration_seconds > 2:
        return 90
    elif steps > 4 or duration_seconds > 2:
        return 75
    else:
        return 60

@spaces.GPU(duration=get_duration)
def generate_video(input_image, prompt, height, width, 
                   negative_prompt=default_negative_prompt, duration_seconds = 5,
                   guidance_scale = 1, steps = 4,
                   seed = 42, randomize_seed = False, 
                   progress=gr.Progress(track_tqdm=True)):
    if input_image is None:
        raise gr.Error("Загрузите картинку.")

    target_h = max(MOD_VALUE, (int(height) // MOD_VALUE) * MOD_VALUE)
    target_w = max(MOD_VALUE, (int(width) // MOD_VALUE) * MOD_VALUE)
    
    num_frames = np.clip(int(round(duration_seconds * FIXED_FPS)), MIN_FRAMES_MODEL, MAX_FRAMES_MODEL)
    
    current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)

    resized_image = input_image.resize((target_w, target_h))
    prompt = prompt
    with torch.inference_mode():
        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)
        ).frames[0]

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

with gr.Blocks() as demo:
    gr.Markdown("# Быстрый 4х ступенчатый Wan 2.1 I2V (14B) от CausVid LoRA")
    #gr.Markdown("[CausVid](https://github.com/tianweiy/CausVid) is a distilled version of Wan 2.1 to run faster in just 4-8 steps, [extracted as LoRA by Kijai](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan21_CausVid_14B_T2V_lora_rank32.safetensors) and is compatible with 🧨 diffusers")
    with gr.Row():
        with gr.Column():
            input_image_component = gr.Image(type="pil", label="Картинка (авто-размер)")
            prompt_input = gr.Textbox(label="Промпт", 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=5, label="Продолжительность (сек)", info=f"Clamped to model's {MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL} frames at {FIXED_FPS}fps.")
            
            with gr.Accordion("Настройки", open=False):
                negative_prompt_input = gr.Textbox(label="Негативный промпт", value=default_negative_prompt, lines=3)
                seed_input = gr.Slider(label="Зерно", minimum=0, maximum=MAX_SEED, step=1, value=42, interactive=True)
                randomize_seed_checkbox = gr.Checkbox(label="Случайное зерно", 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"Высота (мастабирование от {MOD_VALUE})")
                    width_input = gr.Slider(minimum=SLIDER_MIN_W, maximum=SLIDER_MAX_W, step=MOD_VALUE, value=DEFAULT_W_SLIDER_VALUE, label=f"Ширина (мастабирование от {MOD_VALUE})")
                steps_slider = gr.Slider(minimum=1, maximum=30, step=1, value=4, label="Количество шагов") 
                guidance_scale_input = gr.Slider(minimum=0.0, maximum=20.0, step=0.5, value=1.0, label="Соответствие", visible=False)

            generate_button = gr.Button("Генерация", variant="primary")
        with gr.Column():
            video_output = gr.Video(label="Видео", autoplay=True, interactive=False)

    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])

    gr.Examples(
        examples=[ 
            ["peng.png", "пингвин игриво танцует на снегу, Антарктида", 896, 896],
            ["forg.jpg", "лягушка прыгает по кругу", 832, 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__":
    demo.queue().launch(mcp_server=True)