import spaces import torch from diffusers.pipelines.wan.pipeline_wan_i2v import WanImageToVideoPipeline from diffusers.models.transformers.transformer_wan import WanTransformer3DModel from diffusers.utils.export_utils import export_to_video import gradio as gr import tempfile import numpy as np from PIL import Image import random import gc from torchao.quantization import quantize_ from torchao.quantization import Float8DynamicActivationFloat8WeightConfig from torchao.quantization import Int8WeightOnlyConfig import aoti MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers" MAX_DIM = 832 MIN_DIM = 480 SQUARE_DIM = 640 MULTIPLE_OF = 16 MAX_SEED = np.iinfo(np.int32).max FIXED_FPS = 16 MIN_FRAMES_MODEL = 8 MIN_DURATION = round(MIN_FRAMES_MODEL / FIXED_FPS, 1) DEFAULT_DURATION = 5.0 # Модель загружается с device_map='auto' для распределения больших трансформеров pipe = WanImageToVideoPipeline.from_pretrained( MODEL_ID, transformer=WanTransformer3DModel.from_pretrained( 'cbensimon/Wan2.2-I2V-A14B-bf16-Diffusers', subfolder='transformer', torch_dtype=torch.bfloat16, device_map='auto', ), transformer_2=WanTransformer3DModel.from_pretrained( 'cbensimon/Wan2.2-I2V-A14B-bf16-Diffusers', subfolder='transformer_2', torch_dtype=torch.bfloat16, device_map='auto', ), torch_dtype=torch.bfloat16, ) # Загрузка и фьюзинг LoRA pipe.load_lora_weights( "Kijai/WanVideo_comfy", weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors", adapter_name="lightx2v" ) kwargs_lora = {"load_into_transformer_2": True} pipe.load_lora_weights( "Kijai/WanVideo_comfy", weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors", adapter_name="lightx2v_2", **kwargs_lora ) pipe.set_adapters(["lightx2v", "lightx2v_2"], adapter_weights=[1., 1.]) pipe.fuse_lora(adapter_names=["lightx2v"], lora_scale=3., components=["transformer"]) pipe.fuse_lora(adapter_names=["lightx2v_2"], lora_scale=1., components=["transformer_2"]) pipe.unload_lora_weights() # Квантизация quantize_(pipe.text_encoder, Int8WeightOnlyConfig()) quantize_(pipe.transformer, Float8DynamicActivationFloat8WeightConfig()) quantize_(pipe.transformer_2, Float8DynamicActivationFloat8WeightConfig()) # AOTI aoti.aoti_blocks_load(pipe.transformer, 'zerogpu-aoti/Wan2', variant='fp8da') aoti.aoti_blocks_load(pipe.transformer_2, 'zerogpu-aoti/Wan2', variant='fp8da') # 🟢 ИСПРАВЛЕНИЕ 1: Явно переводим пайплайн на GPU. # Это решает проблему "Cannot generate a cpu tensor from a generator of type cuda." pipe.to("cuda") default_prompt_i2v = "make this image come alive, cinematic motion, smooth animation" default_negative_prompt = ( "色调艳丽, 过曝, 静态, 细节模糊不清, 字幕, 风格, 作品, 画作, 画面, 静止, " "整体发灰, 最差质量, 低质量, JPEG压缩残留, 丑陋의, 残缺的, 多余的手指, " "画得不хорошие руки, 画得不хорошие лица, 畸形の, 毀容の, 形态畸形的肢体, 手指融合, " "静止不动的画面, 杂乱的背景, 三条腿, 背景人很多, 倒着走" ) def resize_image(image: Image.Image) -> Image.Image: width, height = image.size if width == height: return image.resize((SQUARE_DIM, SQUARE_DIM), Image.LANCZOS) aspect_ratio = width / height MAX_ASPECT_RATIO = MAX_DIM / MIN_DIM MIN_ASPECT_RATIO = MIN_DIM / MAX_DIM target_w, target_h = width, height image_to_resize = image if aspect_ratio > MAX_ASPECT_RATIO: crop_width = int(round(height * MAX_ASPECT_RATIO)) left = (width - crop_width) // 2 image_to_resize = image.crop((left, 0, left + crop_width, height)) target_w = MAX_DIM target_h = int(round(target_w / MAX_ASPECT_RATIO)) elif aspect_ratio < MIN_ASPECT_RATIO: crop_height = int(round(width / MIN_ASPECT_RATIO)) top = (height - crop_height) // 2 image_to_resize = image.crop((0, top, width, top + crop_height)) target_h = MAX_DIM target_w = int(round(target_h * MIN_ASPECT_RATIO)) else: if width > height: target_w = MAX_DIM target_h = int(round(target_w / aspect_ratio)) else: target_h = MAX_DIM target_w = int(round(target_h * aspect_ratio)) final_w = round(target_w / MULTIPLE_OF) * MULTIPLE_OF final_h = round(target_h / MULTIPLE_OF) * MULTIPLE_OF final_w = max(MIN_DIM, min(MAX_DIM, final_w)) final_h = max(MIN_DIM, min(MAX_DIM, final_h)) return image_to_resize.resize((final_w, final_h), Image.LANCZOS) def get_num_frames(duration_seconds: float): return 1 + int(round(duration_seconds * FIXED_FPS)) def get_duration( input_image, prompt, steps, negative_prompt, duration_seconds, guidance_scale, guidance_scale_2, seed, randomize_seed, progress, ): BASE_FRAMES_HEIGHT_WIDTH = 81 * 832 * 624 BASE_STEP_DURATION = 15 width, height = resize_image(input_image).size frames = get_num_frames(duration_seconds) factor = frames * width * height / BASE_FRAMES_HEIGHT_WIDTH step_duration = BASE_STEP_DURATION * factor ** 1.5 return 10 + int(steps) * step_duration @spaces.GPU(duration=get_duration) def generate_video( input_image, prompt, steps=4, negative_prompt=default_negative_prompt, duration_seconds=DEFAULT_DURATION, guidance_scale=1, guidance_scale_2=1, seed=42, randomize_seed=False, progress=gr.Progress(track_tqdm=True), ): if input_image is None: raise gr.Error("Please upload an input image.") num_frames = get_num_frames(duration_seconds) current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed) resized_image = resize_image(input_image) # 🟢 ИСПРАВЛЕНИЕ 2: Удален аргумент 'device="cuda"', чтобы избежать TypeError, # так как пайплайн уже был переведен на CUDA перед функцией. output_frames_list = pipe( image=resized_image, prompt=prompt, negative_prompt=negative_prompt, height=resized_image.height, width=resized_image.width, num_frames=num_frames, guidance_scale=float(guidance_scale), guidance_scale_2=float(guidance_scale_2), 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 # --- Gradio Interface --- with gr.Blocks() as demo: gr.Markdown("# 🚀 Wan 2.2 I2V (14B) — Unlimited Duration Edition 🕒") gr.Markdown("Generate cinematic I2V animations without duration limits. Optimized for ZeroCPU.") with gr.Row(): with gr.Column(): input_image_component = gr.Image(type="pil", label="Input Image") prompt_input = gr.Textbox(label="Prompt", value=default_prompt_i2v) duration_seconds_input = gr.Slider( minimum=0.5, maximum=60.0, step=0.5, value=DEFAULT_DURATION, label="Duration (seconds)", info=f"Each second = {FIXED_FPS} frames. Longer videos require more VRAM/time." ) 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) steps_slider = gr.Slider(minimum=1, maximum=30, step=1, value=6, label="Inference Steps") guidance_scale_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=1, label="Guidance Scale - high noise stage") guidance_scale_2_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=1, label="Guidance Scale 2 - low noise stage") generate_button = gr.Button("Generate Video", variant="primary") with gr.Column(): video_output = gr.Video(label="Generated Video", autoplay=True, interactive=False) ui_inputs = [ input_image_component, prompt_input, steps_slider, negative_prompt_input, duration_seconds_input, guidance_scale_input, guidance_scale_2_input, seed_input, randomize_seed_checkbox ] generate_button.click(fn=generate_video, inputs=ui_inputs, outputs=[video_output, seed_input]) if __name__ == "__main__": demo.queue().launch(mcp_server=True)