app_14B.py

# PyTorch 2.8 (temporary hack)
import os
os.system('pip install --upgrade --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu126 "torch<2.9" spaces')

# Actual demo code
import spaces
import torch
from diffusers.pipelines.wan.pipeline_wan_i2v import WanImageToVideoPipeline
from diffusers.models.transformers.transformer_wan import WanTransformer3DModel
from diffusers import AutoencoderKLWan, WanPipeline, WanImageToVideoPipeline, UniPCMultistepScheduler
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

from optimization import optimize_pipeline_


MODEL_ID = "linoyts/Wan2.2-T2V-A14B-Diffusers-BF16"

LANDSCAPE_WIDTH = 832
LANDSCAPE_HEIGHT = 480
MAX_SEED = np.iinfo(np.int32).max

FIXED_FPS = 24
MIN_FRAMES_MODEL = 8
MAX_FRAMES_MODEL = 81


# pipe =  WanPipeline.from_pretrained(MODEL_ID,
#     transformer=WanTransformer3DModel.from_pretrained('linoyts/Wan2.2-T2V-A14B-Diffusers-BF16',
#         subfolder='transformer',
#         torch_dtype=torch.bfloat16,
#         device_map='cuda',
#     ),
#     transformer_2=WanTransformer3DModel.from_pretrained('linoyts/Wan2.2-T2V-A14B-Diffusers-BF16',
#         subfolder='transformer_2',
#         torch_dtype=torch.bfloat16,
#         device_map='cuda',
#     ),
#     torch_dtype=torch.bfloat16,
# ).to('cuda')


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



optimize_pipeline_(pipe,
   
    prompt='prompt',
    height=LANDSCAPE_HEIGHT,
    width=LANDSCAPE_WIDTH,
    num_frames=MAX_FRAMES_MODEL,
)


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, 丑陋的, 残缺的, 多余的手指, 画得不好的手部, 画得不好的脸部, 畸形的, 毁容的, 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 get_duration(
  
    prompt,
    negative_prompt,
    num_frames,
    guidance_scale,
    steps,
    seed,
    randomize_seed,
    progress,
):
    return steps * 15

@spaces.GPU(duration=get_duration)
def generate_video(
    prompt,
    negative_prompt=default_negative_prompt,
    num_frames = MAX_FRAMES_MODEL,
    guidance_scale = 3.5,
    steps = 28,
    seed = 42,
    randomize_seed = False,
    progress=gr.Progress(track_tqdm=True),
):
    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)
    
    

    
    output_frames_list = pipe(
                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("# Fast 4 steps Wan 2.1 I2V (14B) with 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():
            
            prompt_input = gr.Textbox(label="Prompt", value=default_prompt_i2v)
            num_frames_input = gr.Slider(minimum=MIN_FRAMES_MODEL, maximum=MAX_FRAMES_MODEL, step=1, value=MAX_FRAMES_MODEL, label="Frames")
            
            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=40, step=1, value=28, label="Inference Steps")
                guidance_scale_input = gr.Slider(minimum=0.0, maximum=20.0, step=0.5, value=1.0, label="Guidance Scale")

            generate_button = gr.Button("Generate Video", variant="primary")
        with gr.Column():
            video_output = gr.Video(label="Generated Video", autoplay=True, interactive=False)
    
    ui_inputs = [
        prompt_input,
        negative_prompt_input, num_frames_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=[ 
            [
                "wan_i2v_input.JPG",
                "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.",
            ],
        ],
        inputs=[ prompt_input], outputs=[video_output, seed_input], fn=generate_video, cache_examples="lazy"
    )

if __name__ == "__main__":
    demo.queue().launch(mcp_server=True)