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| import os | |
| if os.getcwd() != '/home/user/app': | |
| os.chdir('/home/user/app') | |
| import sys | |
| import subprocess | |
| import asyncio | |
| from typing import Sequence, Mapping, Any, Union | |
| print("Importing ComfyUI's main.py for setup...") | |
| import main | |
| print("ComfyUI main imported.") | |
| import torch | |
| import gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| from comfy import model_management | |
| import spaces | |
| from PIL import Image | |
| import random | |
| import nodes # Import nodes after main has set everything up | |
| # --- Manually trigger the node initialization --- | |
| # This step is normally done inside main.start_comfyui(), but we do it here. | |
| # It loads all built-in, extra, and custom nodes into the NODE_CLASS_MAPPINGS. | |
| print("Initializing ComfyUI nodes...") | |
| loop = asyncio.new_event_loop() | |
| asyncio.set_event_loop(loop) | |
| loop.run_until_complete(nodes.init_extra_nodes()) | |
| print("Nodes initialized.") | |
| # --- Helper function from the original script --- | |
| def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any: | |
| try: | |
| return obj[index] | |
| except KeyError: | |
| return obj["result"][index] | |
| # --- Model Downloads --- | |
| print("Downloading models from Hugging Face Hub...") | |
| hf_hub_download(repo_id="Comfy-Org/Wan_2.1_ComfyUI_repackaged", filename="split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safensors", local_dir="models/text_encoders") | |
| 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.safensors", local_dir="models/diffusion_models") | |
| 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/diffusion_models") | |
| 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") | |
| 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") | |
| 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 --- | |
| # This part will now work because NODE_CLASS_MAPPINGS is correctly populated. | |
| cliploader = nodes.NODE_CLASS_MAPPINGS["CLIPLoader"]() | |
| cliptextencode = nodes.NODE_CLASS_MAPPINGS["CLIPTextEncode"]() | |
| unetloader = nodes.NODE_CLASS_MAPPINGS["UNETLoader"]() | |
| vaeloader = nodes.NODE_CLASS_MAPPINGS["VAELoader"]() | |
| clipvisionloader = nodes.NODE_CLASS_MAPPINGS["CLIPVisionLoader"]() | |
| loadimage = nodes.NODE_CLASS_MAPPINGS["LoadImage"]() | |
| clipvisionencode = nodes.NODE_CLASS_MAPPINGS["CLIPVisionEncode"]() | |
| loraloadermodelonly = nodes.NODE_CLASS_MAPPINGS["LoraLoaderModelOnly"]() | |
| modelsamplingsd3 = nodes.NODE_CLASS_MAPPINGS["ModelSamplingSD3"]() | |
| pathchsageattentionkj = nodes.NODE_CLASS_MAPPINGS["PathchSageAttentionKJ"]() | |
| wanfirstlastframetovideo = nodes.NODE_CLASS_MAPPINGS["WanFirstLastFrameToVideo"]() | |
| ksampleradvanced = nodes.NODE_CLASS_MAPPINGS["KSamplerAdvanced"]() | |
| vaedecode = nodes.NODE_CLASS_MAPPINGS["VAEDecode"]() | |
| createvideo = nodes.NODE_CLASS_MAPPINGS["CreateVideo"]() | |
| savevideo = nodes.NODE_CLASS_MAPPINGS["SaveVideo"]() | |
| imageresize = nodes.NODE_CLASS_MAPPINGS["ImageResize+"]() | |
| 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") | |
| 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)) | |
| 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) | |
| # --- App Logic --- | |
| 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, new_height = 832, int(height * (832 / width)) | |
| else: new_height, new_width = 832, int(width * (832 / height)) | |
| return (new_width // 16) * 16, (new_height // 16) * 16 | |
| def generate_video(prompt, first_image_path, last_image_path, duration_seconds): | |
| with torch.inference_mode(): | |
| FPS, MAX_FRAMES = 16, 81 | |
| length_in_frames = max(1, min(int(duration_seconds * FPS), MAX_FRAMES)) | |
| print(f"Requested duration: {duration_seconds}s. Calculated frames: {length_in_frames}") | |
| target_width, target_height = calculate_dimensions(first_image_path) | |
| 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", 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", image=get_value_at_index(loaded_last_image, 0)) | |
| 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)) | |
| wanfirstlastframetovideo_83 = wanfirstlastframetovideo.EXECUTE_NORMALIZED(width=target_width, height=target_height, length=length_in_frames, 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)) | |
| ksampler_positive = get_value_at_index(wanfirstlastframetovideo_83, 0) | |
| ksampler_negative = get_value_at_index(wanfirstlastframetovideo_83, 1) | |
| ksampler_latent = get_value_at_index(wanfirstlastframetovideo_83, 2) | |
| 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=ksampler_positive, negative=ksampler_negative, latent_image=ksampler_latent) | |
| 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=ksampler_positive, negative=ksampler_negative, latent_image=get_value_at_index(ksampleradvanced_101, 0)) | |
| 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 f"output/{savevideo_103['ui']['videos'][0]['filename']}" | |
| # --- Gradio Interface (no changes needed) --- | |
| with gr.Blocks() as app: | |
| gr.Markdown("# Wan 2.2 First/Last Frame to Video") | |
| gr.Markdown("Provide a starting image, an ending image, a text prompt, and a desired duration to generate a video transitioning between them.") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| prompt_input = gr.Textbox(label="Prompt", value="a man dancing in the street, cinematic") | |
| duration_slider = gr.Slider(minimum=1.0, maximum=5.0, value=2.0, step=0.1, label="Video Duration (seconds)") | |
| with gr.Row(): | |
| 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, duration_slider], outputs=[output_video]) | |
| gr.Examples(examples=[["a beautiful woman, cinematic", "examples/start.png", "examples/end.png", 2.5]], inputs=[prompt_input, first_image, last_image, duration_slider]) | |
| if __name__ == "__main__": | |
| 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() |