Update app.py

app.py

@@ -1,92 +1,240 @@
-import gradio as gr
+import spaces
 import torch
-from diffusers import DiffusionPipeline
+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
 
-# -------------------------
-# LOAD MODEL (MULTI-GPU)
-# -------------------------
+from torchao.quantization import quantize_
+from torchao.quantization import Float8DynamicActivationFloat8WeightConfig
+from torchao.quantization import Int8WeightOnlyConfig
 
-MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers"
+import aoti
 
-print("Loading model... This may take a minute.")
-
-# Balanced spread across ALL GPUs (8×L40S)
-pipe = DiffusionPipeline.from_pretrained(
-    MODEL_ID,
-    torch_dtype=torch.float16,
-    device_map="balanced",      # full multi-GPU sharding
-    max_memory={i: "44GiB" for i in range(torch.cuda.device_count())},
-)
 
-pipe.enable_model_cpu_offload()
-pipe.enable_vae_slicing()
+MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers"
 
-print("Model loaded on", torch.cuda.device_count(), "GPUs.")
+MAX_DIM = 832
+MIN_DIM = 480
+SQUARE_DIM = 640
+MULTIPLE_OF = 16
 
+MAX_SEED = np.iinfo(np.int32).max
 
-# -------------------------
-# INFERENCE
-# -------------------------
+FIXED_FPS = 16
+MIN_FRAMES_MODEL = 8
 
-def generate_video(image, prompt, steps, guidance, seed):
-    if image is None:
-        raise ValueError("Upload an input image first.")
+MIN_DURATION = round(MIN_FRAMES_MODEL / FIXED_FPS, 1)
+DEFAULT_DURATION = 5.0
 
-    if seed == -1:
-        seed = torch.randint(0, 2**32, (1,)).item()
+# 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,
+)
 
-    generator = torch.manual_seed(seed)
-    input_image = Image.fromarray(image)
+# 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"
+)
 
-    print(f"[INFO] Generation started, seed={seed}")
 
-    result = pipe(
+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,
-        image=input_image,
+        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),
-        guidance_scale=float(guidance),
-        generator=generator,
-    ).videos[0]
-
-    output_path = "output.mp4"
-    pipe.video_processor.save_video(result, output_path, fps=24)
+        generator=torch.Generator(device="cuda").manual_seed(current_seed),
+    ).frames[0]
 
-    print("[INFO] Video saved:", output_path)
+    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
+        video_path = tmpfile.name
 
-    return output_path
+    export_to_video(output_frames_list, video_path, fps=FIXED_FPS)
+    return video_path, current_seed
 
 
-# -------------------------
-# UI
-# -------------------------
-
+# --- Gradio Interface ---
 with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-        # 🎬 Wan2.2 I2V — Multi-GPU Video Generator  
-        **Модель автоматически распределена по всем доступным GPU (balanced device_map).**  
-        Загрузите изображение → получите видео.
-        """
-    )
-
+    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():
-            image = gr.Image(type="numpy", label="Input Image")
-            prompt = gr.Textbox(label="Prompt", placeholder="Describe the motion...")
-            steps = gr.Slider(10, 60, value=30, step=1, label="Inference Steps")
-            guidance = gr.Slider(1, 15, value=6.5, step=0.5, label="Guidance Scale")
-            seed = gr.Number(value=-1, precision=0, label="Seed (-1 = random)")
-
-            run = gr.Button("🎥 Generate Video")
-
+            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():
-            output = gr.Video(label="Generated Video")
-
-    run.click(
-        generate_video,
-        inputs=[image, prompt, steps, guidance, seed],
-        outputs=[output],
-    )
-
-demo.launch()
+            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)