380 lines
13 KiB
Python
380 lines
13 KiB
Python
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#!/usr/bin/env python3
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"""
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LTX 2.3 I2V - ComfyUI API 调用脚本
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直接用 ComfyUI API 提交 LTX 2.3 蒸馏版图生视频工作流
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"""
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import json, sys, os, time, uuid, requests
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COMFY_HOST = "http://localhost:8188"
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def submit_workflow(input_image="test_scene.png", prompt="A cinematic shot:", output_prefix="ltx23_output", seed=42, width=768, height=512, num_frames=25):
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"""
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构建并提交 LTX 2.3 蒸馏版 I2V 工作流
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节点结构基于 ComfyUI 内置模板展开
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"""
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# 节点 ID 分配
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N = {k: i for i, k in enumerate([
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"ckpt_loader", "text_encoder_loader",
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"load_image", "preprocess",
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"clip_positive", "clip_negative", "cond_zero",
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"ltxv_cond", "crop_guides",
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"empty_video_latent", "empty_audio_latent",
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"concat_av", "img_to_video",
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"cfgguider", "ksampler", "sigmas", "noise",
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"sampler_custom",
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"separate_av",
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"vae_decode", "audio_vae_loader", "audio_vae_decode",
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"create_video", "save_video",
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])}
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prompt_json = {
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# 1. 加载模型
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str(N["ckpt_loader"]): {
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"class_type": "CheckpointLoaderSimple",
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"inputs": {"ckpt_name": "ltx-2.3-22b-dev.safetensors"}
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},
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# 2. 文本编码器（Gemma）
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str(N["text_encoder_loader"]): {
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"class_type": "LTXAVTextEncoderLoader",
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"inputs": {
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"text_encoder": "gemma_3_12B_it.safetensors",
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"ckpt_name": "ltx-2.3-22b-dev.safetensors",
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"device": "default"
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}
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},
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# 3. 加载输入图
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str(N["load_image"]): {
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"class_type": "LoadImage",
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"inputs": {"image": input_image}
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},
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# 4. 预处理（缩放到目标尺寸）
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str(N["preprocess"]): {
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"class_type": "LTXVPreprocess",
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"inputs": {
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"image": [str(N["load_image"]), 0],
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"size": 18
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}
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},
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# 5. 正向提示词
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str(N["clip_positive"]): {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"clip": [str(N["text_encoder_loader"]), 0],
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"text": prompt
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}
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},
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# 6. 负向提示词
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str(N["clip_negative"]): {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"clip": [str(N["text_encoder_loader"]), 0],
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"text": "bad quality, ugly, blurry, distorted, deformed"
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}
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},
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# 7. 归零负向条件
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str(N["cond_zero"]): {
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"class_type": "ConditioningZeroOut",
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"inputs": {
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"conditioning": [str(N["clip_negative"]), 0]
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}
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},
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# 8. LTXV 条件处理
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str(N["ltxv_cond"]): {
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"class_type": "LTXVConditioning",
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"inputs": {
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"positive": [str(N["clip_positive"]), 0],
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"negative": [str(N["cond_zero"]), 0],
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"frame_rate": 25.0
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}
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},
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# 9. 空视频 latent
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str(N["empty_video_latent"]): {
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"class_type": "EmptyLTXVLatentVideo",
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"inputs": {
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"width": width,
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"height": height,
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"length": num_frames,
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"batch_size": 1
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}
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},
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# 10. 空音频 latent
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str(N["empty_audio_latent"]): {
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"class_type": "LTXVEmptyLatentAudio",
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"inputs": {
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"length": num_frames,
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"num_frames_per_batch": 25,
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"batch_size": 1
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}
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},
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# 11. 拼接音视频 latent
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str(N["concat_av"]): {
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"class_type": "LTXVConcatAVLatent",
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"inputs": {
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"video_latent": [str(N["empty_video_latent"]), 0],
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"audio_latent": [str(N["empty_audio_latent"]), 0]
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}
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},
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# 12. 图生视频条件（in-place）
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str(N["img_to_video"]): {
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"class_type": "LTXVImgToVideoInplace",
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"inputs": {
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"vae": [str(N["ckpt_loader"]), 2],
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"image": [str(N["preprocess"]), 0],
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"latent": [str(N["concat_av"]), 0],
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"strength": 1.0,
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"bypass": False
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}
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},
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# 13. 裁剪 guide（对齐 latent 尺寸）
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str(N["crop_guides"]): {
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"class_type": "LTXVCropGuides",
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"inputs": {
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"positive": [str(N["ltxv_cond"]), 0],
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"negative": [str(N["ltxv_cond"]), 1],
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"latent": [str(N["img_to_video"]), 0]
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}
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},
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# 14. CFG Guider（蒸馏版 CFG=1）
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str(N["cfgguider"]): {
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"class_type": "CFGGuider",
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"inputs": {
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"model": [str(N["ckpt_loader"]), 0],
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"positive": [str(N["crop_guides"]), 0],
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"negative": [str(N["crop_guides"]), 1],
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"cfg": 1.0
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}
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},
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# 15. KSampler 选择
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str(N["ksampler"]): {
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"class_type": "KSamplerSelect",
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"inputs": {"sampler_name": "euler"}
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},
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# 16. 蒸馏版 sigma schedule（4步）
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str(N["sigmas"]): {
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"class_type": "ManualSigmas",
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"inputs": {"sigmas": "0.909375, 0.725, 0.421875, 0.0"}
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},
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# 17. 随机噪声
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str(N["noise"]): {
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"class_type": "RandomNoise",
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"inputs": {"noise_seed": seed}
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},
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# 18. 自定义采样器
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str(N["sampler_custom"]): {
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"class_type": "SamplerCustomAdvanced",
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"inputs": {
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"noise": [str(N["noise"]), 0],
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"guider": [str(N["cfgguider"]), 0],
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"sampler": [str(N["ksampler"]), 0],
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"sigmas": [str(N["sigmas"]), 0],
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"latent_image": [str(N["crop_guides"]), 2]
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}
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},
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# 19. 分离音视频 latent
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str(N["separate_av"]): {
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"class_type": "LTXVSeparateAVLatent",
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"inputs": {
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"av_latent": [str(N["sampler_custom"]), 0]
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}
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},
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# 20. VAE 解码视频
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str(N["vae_decode"]): {
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"class_type": "VAEDecode",
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"inputs": {
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"vae": [str(N["ckpt_loader"]), 2],
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"samples": [str(N["separate_av"]), 0]
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}
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},
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# 21. 加载音频 VAE
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str(N["audio_vae_loader"]): {
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"class_type": "LTXVAudioVAELoader",
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"inputs": {"ckpt_name": "ltx-2.3-22b-dev.safetensors"}
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},
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# 22. 音频 VAE 解码
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str(N["audio_vae_decode"]): {
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"class_type": "LTXVAudioVAEDecode",
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"inputs": {
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"samples": [str(N["separate_av"]), 1],
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"audio_vae": [str(N["audio_vae_loader"]), 0]
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}
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},
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# 23. 合成视频
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str(N["create_video"]): {
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"class_type": "CreateVideo",
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"inputs": {
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"images": [str(N["vae_decode"]), 0],
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"audio": [str(N["audio_vae_decode"]), 0],
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"fps": 25,
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"frame_rate": 25,
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"bit_depth": 8
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}
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},
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# 24. 保存视频
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str(N["save_video"]): {
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"class_type": "SaveVideo",
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"inputs": {
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"video": [str(N["create_video"]), 0],
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"filename_prefix": output_prefix,
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"format": "mp4",
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"codec": "h264"
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}
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},
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}
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return prompt_json
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def queue_prompt(prompt_workflow):
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"""提交工作流到 ComfyUI 并返回 prompt_id"""
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payload = {"prompt": prompt_workflow, "client_id": str(uuid.uuid4())}
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r = requests.post(f"{COMFY_HOST}/prompt", json=payload)
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r.raise_for_status()
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data = r.json()
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return data.get("prompt_id"), data
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def get_history(prompt_id):
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"""查询 prompt 执行历史"""
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r = requests.get(f"{COMFY_HOST}/history/{prompt_id}")
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if r.status_code == 200:
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return r.json().get(prompt_id)
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return None
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def wait_for_completion(prompt_id, timeout=600, check_interval=10):
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"""等待 prompt 执行完成"""
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start = time.time()
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while time.time() - start < timeout:
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history = get_history(prompt_id)
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if history and history.get("status", {}).get("completed") is True:
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outputs = history.get("outputs", {})
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elapsed = time.time() - start
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return {"status": "completed", "outputs": outputs, "elapsed": elapsed}
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if history and history.get("status", {}).get("status_str") == "error":
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return {"status": "error", "error": history.get("status", {}).get("error_message", "未知错误")}
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# 查询队列
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r = requests.get(f"{COMFY_HOST}/queue")
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if r.status_code == 200:
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queue_data = r.json()
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r2 = requests.get(f"{COMFY_HOST}/execution/{prompt_id}")
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if r2.status_code == 200:
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exec_data = r2.json()
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progress = exec_data.get("data", {})
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print(f" 进度: {progress.get('progress', 0)*100:.0f}% ({elapsed:.0f}s)" if progress.get('progress') else f" 运行中... ({time.time()-start:.0f}s)")
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time.sleep(check_interval)
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return {"status": "timeout", "elapsed": time.time() - start}
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def check_available_models():
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"""检查 ComfyUI 可用模型"""
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r = requests.get(f"{COMFY_HOST}/object_info/CheckpointLoaderSimple")
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if r.status_code == 200:
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info = r.json()
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models = info.get("CheckpointLoaderSimple", {}).get("input", {}).get("required", {}).get("ckpt_name", [])
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return models
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return []
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def get_queue_status():
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"""获取队列状态"""
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try:
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r = requests.get(f"{COMFY_HOST}/queue")
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if r.status_code == 200:
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return r.json()
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except:
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pass
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return {}
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def clear_queue():
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"""清空队列"""
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try:
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r = requests.post(f"{COMFY_HOST}/queue", json={"clear": True})
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if r.status_code == 200:
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print("✅ 队列已清空")
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except:
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pass
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def main():
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print("=" * 60)
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print("🎬 LTX 2.3 I2V - ComfyUI API 测试脚本")
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print("=" * 60)
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# 参数
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input_image = sys.argv[1] if len(sys.argv) > 1 else "test_scene.png"
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prompt_text = sys.argv[2] if len(sys.argv) > 2 else "A cinematic shot of a character in a fantasy scene, epic, dramatic lighting, motion blur"
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output_prefix = sys.argv[3] if len(sys.argv) > 3 else "ltx23_output"
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print(f"\n📷 输入图: {input_image}")
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print(f"📝 Prompt: {prompt_text[:50]}...")
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print(f"💾 输出: {output_prefix}")
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# 检查队列
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q = get_queue_status()
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running = q.get("queue_running", [])
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pending = q.get("queue_pending", [])
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if running:
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print(f"\n⚠️ 队列中已有 {len(running)} 个任务在跑")
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running_ids = [str(item[1]) if len(item) > 1 else "?" for item in running]
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print(f" 运行中 prompt_id: {running_ids}")
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if pending:
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print(f"⚠️ 队列中 {len(pending)} 个任务待执行")
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# 检查模型
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models = check_available_models()
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ltx_models = [m for m in models if isinstance(m, str) and "ltx" in m.lower()] + [m for m in models if isinstance(m, dict) and "ltx" in str(m).lower()]
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print(f"\n📦 可用 LTX 模型: {ltx_models}")
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# 构建工作流
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print(f"\n🔧 构建工作流...")
|
|||
|
|
workflow = submit_workflow(
|
|||
|
|
input_image=input_image,
|
|||
|
|
prompt=prompt_text,
|
|||
|
|
output_prefix=output_prefix,
|
|||
|
|
seed=42,
|
|||
|
|
width=768,
|
|||
|
|
height=512,
|
|||
|
|
num_frames=25
|
|||
|
|
)
|
|||
|
|
print(f"✅ 工作流构建完成 (25个节点)")
|
|||
|
|
|
|||
|
|
# 提交
|
|||
|
|
print(f"\n🚀 提交任务到 ComfyUI...")
|
|||
|
|
try:
|
|||
|
|
prompt_id, resp = queue_prompt(workflow)
|
|||
|
|
print(f"✅ 任务已提交! prompt_id: {prompt_id}")
|
|||
|
|
print(f" 队列中还有 {len(pending)} 个任务待执行")
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"❌ 提交失败: {e}")
|
|||
|
|
# 打印详细错误
|
|||
|
|
if hasattr(e, 'response') and e.response is not None:
|
|||
|
|
print(f" 响应: {e.response.text[:500]}")
|
|||
|
|
sys.exit(1)
|
|||
|
|
|
|||
|
|
# 等待完成
|
|||
|
|
print(f"\n⏳ 等待任务完成...")
|
|||
|
|
result = wait_for_completion(prompt_id, timeout=600)
|
|||
|
|
|
|||
|
|
if result["status"] == "completed":
|
|||
|
|
print(f"\n✅ 任务完成! 耗时: {result['elapsed']:.1f}秒")
|
|||
|
|
outputs = result["outputs"]
|
|||
|
|
for node_id, node_out in outputs.items():
|
|||
|
|
if "videos" in node_out:
|
|||
|
|
for vid in node_out["videos"]:
|
|||
|
|
print(f" 🎬 视频: {vid['filename']} ({vid.get('type','')})")
|
|||
|
|
if "images" in node_out:
|
|||
|
|
for img in node_out["images"]:
|
|||
|
|
print(f" 🖼️ 图片: {img['filename']}")
|
|||
|
|
|
|||
|
|
# 查找输出文件
|
|||
|
|
out_dir = "/home/ls/comfy/ComfyUI/output"
|
|||
|
|
import glob
|
|||
|
|
files = sorted(glob.glob(f"{out_dir}/{output_prefix}*"))
|
|||
|
|
print(f"\n📁 输出文件:")
|
|||
|
|
for f in files:
|
|||
|
|
size = os.path.getsize(f) / 1024 / 1024
|
|||
|
|
print(f" {f} ({size:.1f}MB)")
|
|||
|
|
elif result["status"] == "error":
|
|||
|
|
print(f"\n❌ 任务失败: {result.get('error', '未知错误')}")
|
|||
|
|
else:
|
|||
|
|
print(f"\n⏰ 超时 ({result['elapsed']:.0f}秒)")
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
main()
|