app.py

import os
import random
import sys
from typing import Sequence, Mapping, Any, Union
import spaces
import torch
import gradio as gr
from huggingface_hub import hf_hub_download
from comfy import model_management
from PIL import Image

# --- Helper Functions from original script ---

def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
    try:
        return obj[index]
    except KeyError:
        return obj["result"][index]

def find_path(name: str, path: str = None) -> str:
    if path is None:
        path = os.getcwd()
    if name in os.listdir(path):
        path_name = os.path.join(path, name)
        print(f"{name} found: {path_name}")
        return path_name
    parent_directory = os.path.dirname(path)
    if parent_directory == path:
        return None
    return find_path(name, parent_directory)

def add_comfyui_directory_to_sys_path() -> None:
    comfyui_path = find_path("ComfyUI")
    if comfyui_path is not None and os.path.isdir(comfyui_path):
        sys.path.append(comfyui_path)
        print(f"'{comfyui_path}' added to sys.path")

def add_extra_model_paths() -> None:
    try:
        from main import load_extra_path_config
    except ImportError:
        from utils.extra_config import load_extra_path_config
    extra_model_paths = find_path("extra_model_paths.yaml")
    if extra_model_paths is not None:
        load_extra_path_config(extra_model_paths)
    else:
        print("Could not find the extra_model_paths config file.")

def import_custom_nodes() -> None:
    import asyncio
    import execution
    from nodes import init_extra_nodes
    import server
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    server_instance = server.PromptServer(loop)
    execution.PromptQueue(server_instance)
    init_extra_nodes()

# --- Setup and Model Downloads ---

add_comfyui_directory_to_sys_path()
add_extra_model_paths()
import_custom_nodes()
from nodes import NODE_CLASS_MAPPINGS

print("Downlading models from Hugging Face Hub...")
# Text Encoder
hf_hub_download(repo_id="Comfy-Org/Wan_2.1_ComfyUI_repackaged", filename="split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors", local_dir="models/text_encoders")
# UNETs
hf_hub_download(repo_id="Comfy-Org/Wan_2.2_ComfyUI_Repackaged", filename="split_files/diffusion_models/wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors", local_dir="models/unet")
hf_hub_download(repo_id="Comfy-Org/Wan_2.2_ComfyUI_Repackaged", filename="split_files/diffusion_models/wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors", local_dir="models/unet")
# VAE
hf_hub_download(repo_id="Comfy-Org/Wan_2.1_ComfyUI_repackaged", filename="split_files/vae/wan_2.1_vae.safetensors", local_dir="models/vae")
# CLIP Vision
hf_hub_download(repo_id="Comfy-Org/Wan_2.1_ComfyUI_repackaged", filename="split_files/clip_vision/clip_vision_h.safetensors", local_dir="models/clip_vision")
# LoRAs
hf_hub_download(repo_id="Kijai/WanVideo_comfy", filename="Wan22-Lightning/Wan2.2-Lightning_I2V-A14B-4steps-lora_HIGH_fp16.safetensors", local_dir="models/loras")
hf_hub_download(repo_id="Kijai/WanVideo_comfy", filename="Wan22-Lightning/Wan2.2-Lightning_I2V-A14B-4steps-lora_LOW_fp16.safetensors", local_dir="models/loras")
print("Downloads complete.")

# --- ZeroGPU: Pre-load models and instantiate nodes globally ---

# Instantiate Nodes
cliploader = NODE_CLASS_MAPPINGS["CLIPLoader"]()
cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
unetloader = NODE_CLASS_MAPPINGS["UNETLoader"]()
vaeloader = NODE_CLASS_MAPPINGS["VAELoader"]()
clipvisionloader = NODE_CLASS_MAPPINGS["CLIPVisionLoader"]()
loadimage = NODE_CLASS_MAPPINGS["LoadImage"]()
clipvisionencode = NODE_CLASS_MAPPINGS["CLIPVisionEncode"]()
loraloadermodelonly = NODE_CLASS_MAPPINGS["LoraLoaderModelOnly"]()
modelsamplingsd3 = NODE_CLASS_MAPPINGS["ModelSamplingSD3"]()
pathchsageattentionkj = NODE_CLASS_MAPPINGS["PathchSageAttentionKJ"]()
wanfirstlastframetovideo = NODE_CLASS_MAPPINGS["WanFirstLastFrameToVideo"]()
ksampleradvanced = NODE_CLASS_MAPPINGS["KSamplerAdvanced"]()
vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
createvideo = NODE_CLASS_MAPPINGS["CreateVideo"]()
savevideo = NODE_CLASS_MAPPINGS["SaveVideo"]()
imageresize = NODE_CLASS_MAPPINGS["ImageResize+"]() # For dynamic resizing

# Load Models
cliploader_38 = cliploader.load_clip(clip_name="umt5_xxl_fp8_e4m3fn_scaled.safetensors", type="wan", device="cpu")
unetloader_37_low_noise = unetloader.load_unet(unet_name="wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors", weight_dtype="default")
unetloader_91_high_noise = unetloader.load_unet(unet_name="wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors", weight_dtype="default")
vaeloader_39 = vaeloader.load_vae(vae_name="wan_2.1_vae.safetensors")
clipvisionloader_49 = clipvisionloader.load_clip(clip_name="clip_vision_h.safetensors")

# Apply LoRAs and Patches
loraloadermodelonly_94_high = loraloadermodelonly.load_lora_model_only(lora_name="Wan2.2-Lightning_I2V-A14B-4steps-lora_HIGH_fp16.safetensors", strength_model=0.8, model=get_value_at_index(unetloader_91_high_noise, 0))
loraloadermodelonly_95_low = loraloadermodelonly.load_lora_model_only(lora_name="Wan2.2-Lightning_I2V-A14B-4steps-lora_LOW_fp16.safetensors", strength_model=0.8, model=get_value_at_index(unetloader_37_low_noise, 0))
modelsamplingsd3_93_low = modelsamplingsd3.patch(shift=8, model=get_value_at_index(loraloadermodelonly_95_low, 0))
pathchsageattentionkj_98_low = pathchsageattentionkj.patch(sage_attention="auto", model=get_value_at_index(modelsamplingsd3_93_low, 0))
modelsamplingsd3_79_high = modelsamplingsd3.patch(shift=8, model=get_value_at_index(loraloadermodelonly_94_high, 0))
pathchsageattentionkj_96_high = pathchsageattentionkj.patch(sage_attention="auto", model=get_value_at_index(modelsamplingsd3_79_high, 0))

# Pre-load models to GPU
model_loaders = [cliploader_38, unetloader_37_low_noise, unetloader_91_high_noise, vaeloader_39, clipvisionloader_49, loraloadermodelonly_94_high, loraloadermodelonly_95_low]
valid_models = [getattr(loader[0], 'patcher', loader[0]) for loader in model_loaders if not isinstance(loader[0], dict) and not isinstance(getattr(loader[0], 'patcher', None), dict)]
model_management.load_models_gpu(valid_models)

# --- Custom Logic for this App ---

def calculate_dimensions(image_path):
    with Image.open(image_path) as img:
        width, height = img.size
    
    if width == height:
        return 480, 480
        
    if width > height:
        new_width = 832
        new_height = int(height * (832 / width))
    else:
        new_height = 832
        new_width = int(width * (832 / height))
        
    # Ensure dimensions are multiples of 16
    new_width = (new_width // 16) * 16
    new_height = (new_height // 16) * 16
    
    return new_width, new_height

# --- Main Generation Function ---

@spaces.GPU(duration=120)
def generate_video(prompt, first_image_path, last_image_path):
    # This function now only handles per-request logic
    with torch.inference_mode():
        # Calculate target dimensions based on the first image
        target_width, target_height = calculate_dimensions(first_image_path)

        # 1. Load and resize images
        # Since LoadImage returns a tensor, we pass it to the resize node
        loaded_first_image = loadimage.load_image(image=first_image_path)
        resized_first_image = imageresize.execute(
            width=target_width, height=target_height, interpolation="bicubic",
            method="stretch", condition="always", multiple_of=1,
            image=get_value_at_index(loaded_first_image, 0)
        )
        
        loaded_last_image = loadimage.load_image(image=last_image_path)
        resized_last_image = imageresize.execute(
            width=target_width, height=target_height, interpolation="bicubic",
            method="stretch", condition="always", multiple_of=1,
            image=get_value_at_index(loaded_last_image, 0)
        )

        # 2. Encode text and images
        cliptextencode_6 = cliptextencode.encode(text=prompt, clip=get_value_at_index(cliploader_38, 0))
        cliptextencode_7_negative = cliptextencode.encode(
            text="low quality, worst quality, jpeg artifacts, ugly, deformed, blurry",
            clip=get_value_at_index(cliploader_38, 0),
        )
        clipvisionencode_51 = clipvisionencode.encode(crop="none", clip_vision=get_value_at_index(clipvisionloader_49, 0), image=get_value_at_index(resized_first_image, 0))
        clipvisionencode_87 = clipvisionencode.encode(crop="none", clip_vision=get_value_at_index(clipvisionloader_49, 0), image=get_value_at_index(resized_last_image, 0))

        # 3. Prepare latents for video generation
        wanfirstlastframetovideo_83 = wanfirstlastframetovideo.EXECUTE_NORMALIZED(
            width=target_width, height=target_height, length=33, batch_size=1,
            positive=get_value_at_index(cliptextencode_6, 0),
            negative=get_value_at_index(cliptextencode_7_negative, 0),
            vae=get_value_at_index(vaeloader_39, 0),
            clip_vision_start_image=get_value_at_index(clipvisionencode_51, 0),
            clip_vision_end_image=get_value_at_index(clipvisionencode_87, 0),
            start_image=get_value_at_index(resized_first_image, 0),
            end_image=get_value_at_index(resized_last_image, 0),
        )

        # 4. KSampler pipeline
        ksampleradvanced_101 = ksampleradvanced.sample(
            add_noise="enable", noise_seed=random.randint(1, 2**64), steps=8, cfg=1,
            sampler_name="euler", scheduler="simple", start_at_step=0, end_at_step=4,
            return_with_leftover_noise="enable", model=get_value_at_index(pathchsageattentionkj_96_high, 0),
            positive=get_value_at_index(wanfirstlastframetovideo_83, 0),
            negative=get_value_at_index(wanfirstlastframetovideo_83, 1),
            latent_image=get_value_at_index(wanfirstlastframetovideo_83, 2),
        )
        ksampleradvanced_102 = ksampleradvanced.sample(
            add_noise="disable", noise_seed=random.randint(1, 2**64), steps=8, cfg=1,
            sampler_name="euler", scheduler="simple", start_at_step=4, end_at_step=10000,
            return_with_leftover_noise="disable", model=get_value_at_index(pathchsageattentionkj_98_low, 0),
            positive=get_value_at_index(wanfirstlastframetovideo_83, 0),
            negative=get_value_at_index(wanfirstlastframetovideo_83, 1),
            latent_image=get_value_at_index(ksampleradvanced_101, 0),
        )

        # 5. Decode and save video
        vaedecode_8 = vaedecode.decode(samples=get_value_at_index(ksampleradvanced_102, 0), vae=get_value_at_index(vaeloader_39, 0))
        createvideo_104 = createvideo.create_video(fps=16, images=get_value_at_index(vaedecode_8, 0))
        savevideo_103 = savevideo.save_video(filename_prefix="ComfyUI_Video", format="mp4", codec="libx264", video=get_value_at_index(createvideo_104, 0))

        # Return the path to the saved video
        video_filename = savevideo_103['ui']['videos'][0]['filename']
        return f"output/{video_filename}"

# --- Gradio Interface ---

with gr.Blocks() as app:
    gr.Markdown("# Wan 2.2 First/Last Frame to Video")
    gr.Markdown("Provide a starting image, an ending image, and a text prompt to generate a video transitioning between them.")

    with gr.Row():
        with gr.Column(scale=1):
            prompt_input = gr.Textbox(label="Prompt", value="the guy turns")
            first_image = gr.Image(label="First Frame", type="filepath")
            last_image = gr.Image(label="Last Frame", type="filepath")
            generate_btn = gr.Button("Generate Video")
        with gr.Column(scale=2):
            output_video = gr.Video(label="Generated Video")

    generate_btn.click(
        fn=generate_video,
        inputs=[prompt_input, first_image, last_image],
        outputs=[output_video]
    )
    
    gr.Examples(
        examples=[
            ["a beautiful woman, cinematic", "examples/start.png", "examples/end.png"]
        ],
        inputs=[prompt_input, first_image, last_image]
    )

if __name__ == "__main__":
    # Create example images if they don't exist
    if not os.path.exists("examples"):
        os.makedirs("examples")
    if not os.path.exists("examples/start.png"):
        Image.new('RGB', (512, 512), color = 'red').save('examples/start.png')
    if not os.path.exists("examples/end.png"):
        Image.new('RGB', (512, 512), color = 'blue').save('examples/end.png')

    app.launch()