更新数字人板块
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# Copyright (C) 2025 AIDC-AI
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Style configuration components for web UI (middle column)
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"""
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import os
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from pathlib import Path
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import streamlit as st
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from loguru import logger
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from web.i18n import tr, get_language
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from web.utils.async_helpers import run_async
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from pixelle_video.config import config_manager
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def render_style_config(pixelle_video):
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"""Render style configuration section (middle column)"""
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# TTS Section (moved from left column)
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# ====================================================================
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with st.container(border=True):
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st.markdown(f"**{tr('section.tts')}**")
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with st.expander(tr("help.feature_description"), expanded=False):
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st.markdown(f"**{tr('help.what')}**")
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st.markdown(tr("digital_tts.what"))
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# Get TTS config
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comfyui_config = config_manager.get_comfyui_config()
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tts_config = comfyui_config["tts"]
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# 只使用local模式，移除inference_mode选择
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tts_mode = "local"
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# ================================================================
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# Local Mode UI (简化版本)
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# ================================================================
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# Import voice configuration
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from pixelle_video.tts_voices import EDGE_TTS_VOICES, get_voice_display_name
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# Get saved voice from config
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local_config = tts_config.get("local", {})
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saved_voice = local_config.get("voice", "zh-CN-YunjianNeural")
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saved_speed = local_config.get("speed", 1.2)
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# Build voice options with i18n
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voice_options = []
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voice_ids = []
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default_voice_index = 0
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for idx, voice_config in enumerate(EDGE_TTS_VOICES):
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voice_id = voice_config["id"]
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display_name = get_voice_display_name(voice_id, tr, get_language())
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voice_options.append(display_name)
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voice_ids.append(voice_id)
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# Set default index if matches saved voice
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if voice_id == saved_voice:
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default_voice_index = idx
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# Two-column layout: Voice | Speed
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voice_col, speed_col = st.columns([1, 1])
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with voice_col:
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# Voice selector
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selected_voice_display = st.selectbox(
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tr("tts.voice_selector"),
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voice_options,
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index=default_voice_index,
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key="digital_tts_local_voice"
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)
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# Get actual voice ID
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selected_voice_index = voice_options.index(selected_voice_display)
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selected_voice = voice_ids[selected_voice_index]
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with speed_col:
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# Speed slider
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tts_speed = st.slider(
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tr("tts.speed"),
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min_value=0.5,
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max_value=2.0,
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value=saved_speed,
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step=0.1,
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format="%.1fx",
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key="digital_tts_local_speed"
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)
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st.caption(tr("tts.speed_label", speed=f"{tts_speed:.1f}"))
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# ================================================================
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# TTS Preview (只支持local模式)
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# ================================================================
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with st.expander(tr("tts.preview_title"), expanded=False):
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# Preview text input
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preview_text = st.text_input(
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tr("tts.preview_text"),
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value="大家好，这是一段测试语音。",
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placeholder=tr("tts.preview_text_placeholder"),
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key="digital_tts_preview_text"
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)
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# Preview button
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if st.button(tr("tts.preview_button"), key="digital_preview_tts", use_container_width=True):
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with st.spinner(tr("tts.previewing")):
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try:
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# Build TTS params for local mode
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tts_params = {
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"text": preview_text,
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"inference_mode": "local",
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"voice": selected_voice,
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"speed": tts_speed
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}
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audio_path = run_async(pixelle_video.tts(**tts_params))
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# Play the audio
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if audio_path:
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st.success(tr("tts.preview_success"))
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if audio_path.startswith('http'):
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st.audio(audio_path)
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else:
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st.audio(audio_path, format="audio/mp3")
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# Show file path
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st.caption(f"📁 {audio_path}")
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else:
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st.error("Failed to generate preview audio")
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except Exception as e:
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st.error(tr("tts.preview_failed", error=str(e)))
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logger.exception(e)
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# ====================================================================
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# Storyboard Template Section
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# ====================================================================
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def get_template_preview_path(template_path: str, language: str = "zh_CN") -> str:
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"""
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Get the preview image path for a template based on language.
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Args:
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template_path: Template path like "1080x1920/image_default.html"
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language: Language code, either "zh_CN" or "en"
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Returns:
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Path to preview image in docs/images/
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"""
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# Extract size and template name from path
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# e.g., "1080x1920/image_default.html" -> size="1080x1920", name="image_default"
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path_parts = template_path.split('/')
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if len(path_parts) >= 2:
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size = path_parts[0] # e.g., "1080x1920"
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template_file = path_parts[1] # e.g., "image_default.html"
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template_name = template_file.replace('.html', '') # e.g., "image_default"
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# Build preview image path
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# Format: docs/images/{size}/{template_name}.jpg or {template_name}_en.jpg
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# Chinese uses Chinese preview, all other languages use English preview for better i18n
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suffix = "" if language == "zh_CN" else "_en"
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# Try different image extensions
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for ext in ['.jpg', '.png']:
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preview_path = f"docs/images/{size}/{template_name}{suffix}{ext}"
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if os.path.exists(preview_path):
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return preview_path
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# Fallback: try without language suffix (for templates with only one version)
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for ext in ['.jpg', '.png']:
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preview_path = f"docs/images/{size}/{template_name}{ext}"
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if os.path.exists(preview_path):
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return preview_path
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# If no preview found, return empty string
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return ""
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# ====================================================================
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# Workflow Selection Section (Auto-configured for Digital Human)
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# ====================================================================
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with st.container(border=True):
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st.markdown(f"**{tr('digital_human.section.workflow')}**")
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with st.expander(tr("help.feature_description"), expanded=False):
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st.markdown(f"**{tr('help.what')}**")
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st.markdown(tr("digital_human.workflow.what"))
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# Get available workflows
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all_workflows = pixelle_video.media.list_workflows()
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# Auto-select digital human workflows (check file existence directly)
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# First workflow: digital_image (generates collage and copy)
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first_workflow = "runninghub/digital_image.json"
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first_workflow_path = "workflows/runninghub/digital_image.json"
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first_workflow_found = os.path.exists(first_workflow_path)
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# Second workflow: digital_combination (generates final video)
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second_workflow = "runninghub/digital_combination.json"
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second_workflow_path = "workflows/runninghub/digital_combination.json"
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second_workflow_found = os.path.exists(second_workflow_path)
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# First workflow status
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if first_workflow_found:
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st.success(f"✅ {tr('digital_human.workflow.first_step')}")
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else:
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st.error(f"❌ {tr('digital_human.workflow.first_step')} {tr('digital_human.workflow.not_found')}")
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# Second workflow status
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if second_workflow_found:
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st.success(f"✅ {tr('digital_human.workflow.second_step')}")
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else:
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st.error(f"❌ {tr('digital_human.workflow.second_step')} {tr('digital_human.workflow.not_found')}")
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# Show workflow info
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if first_workflow_found and second_workflow_found:
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st.info(f"🎯 {tr('digital_human.workflow.ready')}")
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else:
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st.warning(f"⚠️ {tr('digital_human.workflow.missing')}")
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# Return all style configuration parameters (简化版本，只支持local TTS)
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return {
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"tts_inference_mode": "local",
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"tts_voice": selected_voice,
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"tts_speed": tts_speed,
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"first_workflow": first_workflow,
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"second_workflow": second_workflow
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}
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+50
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@@ -203,20 +203,20 @@
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"tts.voice_selector": "Voice Selection",
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"tts.speed": "Speed",
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"tts.speed_label": "{speed}x",
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"tts.voice.zh_CN_XiaoxiaoNeural": "zh-CN-XiaoxiaoNeural",
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"tts.voice.zh_CN_XiaoyiNeural": "zh-CN-XiaoyiNeural",
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"tts.voice.zh_CN_YunjianNeural": "zh-CN-YunjianNeural",
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"tts.voice.zh_CN_YunxiNeural": "zh-CN-YunxiNeural",
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"tts.voice.zh_CN_YunyangNeural": "zh-CN-YunyangNeural",
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"tts.voice.zh_CN_YunyeNeural": "zh-CN-YunyeNeural",
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"tts.voice.zh_CN_YunfengNeural": "zh-CN-YunfengNeural",
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"tts.voice.zh_CN_liaoning_XiaobeiNeural": "zh-CN-liaoning-XiaobeiNeural",
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"tts.voice.en_US_AriaNeural": "en-US-AriaNeural",
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"tts.voice.en_US_JennyNeural": "en-US-JennyNeural",
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"tts.voice.en_US_GuyNeural": "en-US-GuyNeural",
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"tts.voice.en_US_DavisNeural": "en-US-DavisNeural",
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"tts.voice.en_GB_SoniaNeural": "en-GB-SoniaNeural",
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"tts.voice.en_GB_RyanNeural": "en-GB-RyanNeural",
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"tts.voice.ko-KR-InJoonNeural": "ko-KR-InJoonNeural",
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"tts.voice.ko-KR-SunHiNeural": "ko-KR-SunHiNeural",
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"tts.voice.fr-FR-EloiseNeural": "fr-FR-EloiseNeural",
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"tts.voice.fr-FR-HenriNeural": "fr-FR-HenriNeural",
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"tts.voice.pt-PT-DuarteNeural": "pt-PT-DuarteNeural",
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"tts.voice.pt-PT-RaquelNeural": "pt-PT-RaquelNeural",
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"tts.voice.de-DE-AmalaNeural": "de-DE-AmalaNeural",
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"tts.voice.de-DE-ConradNeural": "de-DE-ConradNeural",
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"tts.voice.ru-RU-DmitryNeural": "ru-RU-DmitryNeural",
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"tts.voice.ru-RU-SvetlanaNeural": "ru-RU-SvetlanaNeural",
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"tts.voice.tr-TR-AhmetNeural": "tr-TR-AhmetNeural",
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"tts.voice.tr-TR-EmelNeural": "tr-TR-EmelNeural",
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"tts.voice.es-ES-AlvaroNeural": "es-ES-AlvaroNeural",
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"tts.voice.es-ES-ElviraNeural": "es-ES-ElviraNeural",
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"tts.selector": "Workflow Selection",
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"tts.what": "Converts narration text to natural human-like speech (some workflows support reference audio for voice cloning)",
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"tts.how": "Place the exported tts_xxx.json workflow file(API format) into the workflows/selfhost/ folder (for local ComfyUI) or the workflows/runninghub/ folder (for cloud)",
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@@ -348,6 +348,42 @@
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"pipeline.quick_create.description": "Input an idea, AI completes the entire video for you",
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"pipeline.custom_media.name": "Custom Media",
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"pipeline.custom_media.description": "Use your own photos/videos, AI adds narration and voiceover",
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"pipeline.digital_human.name": "Digital Human Generate",
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"pipeline.digital_human.description": "Using a piece of text, two images, and an audio clip, AI can help you generate a digital human video.",
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"digital_human.section.character_assets": "😊 Character Image Upload",
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"digital_human.assets.character_what": "Upload digital human character images",
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"digital_human.assets.how": "Supports JPG/PNG/WebP format images, recommend clear and relevant materials",
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"digital_human.assets.upload": "Upload Assets",
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"digital_human.assets.upload_help": "Only supports single image upload",
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"digital_human.assets.character_sucess": "Character image uploaded successfully",
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"digital_human.assets.preview": "📷 Asset Preview",
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"digital_human.assets.character_empty_hint": "💡 Please upload a digital human character image",
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"digital_human.section.goods_assets": "👝 Product Image Upload",
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"digital_human.assets.goods_what": "Upload product images",
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"digital_human.assets.goods_sucess": "Product image uploaded successfully",
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"digital_human.assets.goods_empty_hint": "💡 Please upload a product image",
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"digital_human.section.goods_info": "🗃️ Product Information",
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"digital_human.goods_title": "Product Name",
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"digital_human.goods_title_placeholder": "e.g.: lipstick, coffee machine, robot vacuum, etc.",
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"digital_human.goods_title_help": "Product name, cannot be empty",
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"digital_human.section.workflow": "⚙️ Workflow Configuration",
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"digital_human.workflow.what": "Configure the two-step workflow for digital human video generation: first step generates collage and copy, second step generates final video",
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"digital_human.workflow.how": "Select appropriate workflows to handle different generation steps, ensuring output quality",
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"digital_human.workflow.first_step": "First Step Workflow",
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"digital_human.workflow.first_select": "Select First Step Workflow",
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"digital_human.workflow.first_help": "Used to generate collage and copy based on character image, product image and product name",
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"digital_human.workflow.second_step": "Second Step Workflow",
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"digital_human.workflow.second_select": "Select Second Step Workflow",
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"digital_human.workflow.second_help": "Used to generate final digital human video based on generated image and audio",
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"digital_human.workflow.selected_info": "Selected workflow configuration",
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"digital_human.workflow.auto_selected": "Auto-selected workflow",
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"digital_human.workflow.configuration": "Workflow Configuration Status",
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"digital_human.workflow.not_found": "not found",
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"digital_human.workflow.ready": "Digital human workflows are ready, you can start generating videos",
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"digital_human.workflow.missing": "Missing required digital human workflows, please check configuration",
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"digital_human.workflow.advanced_options": "Advanced Options (Manual Override)",
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"digital_human.workflow.manual_override": "Manual Workflow Selection (use only when needed)",
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"digital_tts.what": "Convert narration text to natural human-like speech",
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"asset_based.section.assets": "📦 Asset Upload",
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"asset_based.section.video_info": "📝 Video Information",
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"asset_based.section.source": "⚙️ Service Configuration",
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@@ -348,6 +348,8 @@
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"pipeline.quick_create.description": "输入一个想法,AI帮你完成整条视频",
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"pipeline.custom_media.name": "自定义素材",
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"pipeline.custom_media.description": "用你自己的照片/视频,AI帮你配文案和配音",
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"pipeline.digital_human.name": "数字人生成",
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"pipeline.digital_human.description": "用一段文本、两张图片、一段音频,AI帮你生成一个数字人视频",
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"asset_based.section.assets": "📦 素材上传",
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"asset_based.section.video_info": "📝 视频信息",
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"asset_based.section.source": "⚙️ 服务配置",
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@@ -385,6 +387,30 @@
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"asset_based.progress.generating_script": "📝 正在生成视频脚本...",
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"asset_based.progress.script_complete": "✅ 脚本生成完成",
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"asset_based.progress.concat_complete": "✅ 视频合成完成",
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"digital_human.section.character_assets": "😊 人物形象上传",
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"digital_human.assets.character_what": "上传数字人形象的图片",
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"digital_human.assets.how": "支持 JPG/PNG/WebP 等格式图片，建议素材清晰且内容相关",
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"digital_human.assets.upload": "上传素材",
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"digital_human.assets.upload_help": "仅支持单张图片上传",
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"digital_human.assets.character_sucess": "上传形象图片成功",
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"digital_human.assets.preview": "📷 素材预览",
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"digital_human.assets.character_empty_hint": "💡 请上传一个数字人形象图片素材",
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"digital_human.section.goods_assets": "👝 商品图片上传",
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"digital_human.assets.goods_what": "上传商品的图片",
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"digital_human.assets.goods_sucess": "上传商品图片成功",
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"digital_human.assets.goods_empty_hint": "💡 请上传一个商品图片素材",
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"digital_human.section.goods_info": "🗃️ 商品信息",
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"digital_human.goods_title": "商品名称",
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"digital_human.goods_title_placeholder": "例如：口红、咖啡机、扫地机器人等",
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"digital_human.goods_title_help": "商品的名称，不可为空",
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"digital_human.section.workflow": "⚙️ 工作流加载",
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"digital_human.workflow.what": "配置数字人视频生成的两步工作流：第一步生成拼图和文案，第二步生成最终视频",
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"digital_human.workflow.first_step": "口播搞&拼图工作流成功加载",
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"digital_human.workflow.second_step": "数字人合成工作流成功加载",
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"digital_human.workflow.ready": "数字人工作流已就绪，可以开始生成视频",
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"digital_human.workflow.missing": "缺少必要的数字人工作流，请检查配置",
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"digital_tts.what": "将旁白文本转换为真人般的自然语音",
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"faq.expand_to_view": "常见问题",
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"faq.load_error": "无法加载常见问题内容",
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"faq.more_help": "需要更多帮助？"
|
||||
|
||||
@@ -26,6 +26,7 @@ from web.pipelines.base import (
|
||||
# Import all pipeline UI modules to ensure they register themselves
|
||||
from web.pipelines import standard
|
||||
from web.pipelines import asset_based
|
||||
from web.pipelines import digital_human
|
||||
|
||||
__all__ = [
|
||||
"PipelineUI",
|
||||
|
||||
@@ -0,0 +1,644 @@
|
||||
import os
|
||||
import time
|
||||
import uuid
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import streamlit as st
|
||||
from loguru import logger
|
||||
import httpx
|
||||
|
||||
from web.i18n import tr, get_language
|
||||
from web.pipelines.base import PipelineUI, register_pipeline_ui
|
||||
from web.components.content_input import render_bgm_section, render_version_info
|
||||
from web.components.digital_tts_config import render_style_config
|
||||
from web.utils.async_helpers import run_async
|
||||
from pixelle_video.config import config_manager
|
||||
from pixelle_video.utils.os_util import create_task_output_dir
|
||||
|
||||
class DigitalHumanPipelineUI(PipelineUI):
|
||||
"""
|
||||
UI for the Digital_Human Video Generation Pipeline.
|
||||
Generates videos from user-provided assets (images&videos&audio).
|
||||
"""
|
||||
name = "digital_human"
|
||||
icon = "🤖"
|
||||
|
||||
@property
|
||||
def display_name(self):
|
||||
return tr("pipeline.digital_human.name")
|
||||
|
||||
@property
|
||||
def description(self):
|
||||
return tr("pipeline.digital_human.description")
|
||||
|
||||
def render(self, pixelle_video: Any):
|
||||
# Three-column layout
|
||||
left_col, middle_col, right_col = st.columns([1, 1, 1])
|
||||
|
||||
# ====================================================================
|
||||
# Left Column: Asset Upload
|
||||
# ====================================================================
|
||||
with left_col:
|
||||
asset_params = self.render_digital_human_input()
|
||||
# bgm_params = render_bgm_section(key_prefix="asset_")
|
||||
render_version_info()
|
||||
|
||||
# ====================================================================
|
||||
# Middle Column: Video Configuration
|
||||
# ====================================================================
|
||||
with middle_col:
|
||||
# Style configuration (TTS, template, workflow, etc.)
|
||||
style_params = render_style_config(pixelle_video)
|
||||
|
||||
# # ====================================================================
|
||||
# # Right Column: Output Preview
|
||||
# # ====================================================================
|
||||
with right_col:
|
||||
# Combine all parameters
|
||||
video_params = {
|
||||
**asset_params,
|
||||
**style_params
|
||||
}
|
||||
|
||||
self._render_output_preview(pixelle_video, video_params)
|
||||
|
||||
def render_digital_human_input(self) -> dict:
|
||||
"""Render digital human character image upload section"""
|
||||
with st.container(border=True):
|
||||
st.markdown(f"**{tr('digital_human.section.character_assets')}**")
|
||||
|
||||
with st.expander(tr("help.feature_description"), expanded=False):
|
||||
st.markdown(f"**{tr('help.what')}**")
|
||||
st.markdown(tr("digital_human.assets.character_what"))
|
||||
st.markdown(f"**{tr('help.how')}**")
|
||||
st.markdown(tr("digital_human.assets.how"))
|
||||
|
||||
# File uploader for multiple files
|
||||
uploaded_files = st.file_uploader(
|
||||
tr("digital_human.assets.upload"),
|
||||
type=["jpg", "jpeg", "png", "webp"],
|
||||
accept_multiple_files=True,
|
||||
help=tr("digital_human.assets.upload_help"),
|
||||
key="character_files"
|
||||
)
|
||||
|
||||
# Save uploaded files to temp directory with unique session ID
|
||||
character_asset_paths = []
|
||||
if uploaded_files:
|
||||
import uuid
|
||||
session_id = str(uuid.uuid4()).replace('-', '')[:12]
|
||||
temp_dir = Path(f"temp/assets_{session_id}")
|
||||
temp_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
for uploaded_file in uploaded_files:
|
||||
file_path = temp_dir / uploaded_file.name
|
||||
with open(file_path, "wb") as f:
|
||||
f.write(uploaded_file.getbuffer())
|
||||
character_asset_paths.append(str(file_path.absolute()))
|
||||
|
||||
st.success(tr("digital_human.assets.character_sucess"))
|
||||
|
||||
# Preview uploaded assets
|
||||
with st.expander(tr("digital_human.assets.preview"), expanded=True):
|
||||
# Show in a grid (3 columns)
|
||||
cols = st.columns(3)
|
||||
for i, (file, path) in enumerate(zip(uploaded_files, character_asset_paths)):
|
||||
with cols[i % 3]:
|
||||
# Check if image
|
||||
ext = Path(path).suffix.lower()
|
||||
if ext in [".jpg", ".jpeg", ".png", ".webp"]:
|
||||
st.image(file, caption=file.name, use_container_width=True)
|
||||
else:
|
||||
st.info(tr("digital_human.assets.character_empty_hint"))
|
||||
|
||||
with st.container(border=True):
|
||||
st.markdown(f"**{tr('digital_human.section.goods_assets')}**")
|
||||
|
||||
with st.expander(tr("help.feature_description"), expanded=False):
|
||||
st.markdown(f"**{tr('help.what')}**")
|
||||
st.markdown(tr("digital_human.assets.goods_what"))
|
||||
st.markdown(f"**{tr('help.how')}**")
|
||||
st.markdown(tr("digital_human.assets.how"))
|
||||
|
||||
# File uploader for multiple files
|
||||
uploaded_files = st.file_uploader(
|
||||
tr("digital_human.assets.upload"),
|
||||
type=["jpg", "jpeg", "png", "webp"],
|
||||
accept_multiple_files=True,
|
||||
help=tr("digital_human.assets.upload_help"),
|
||||
key="goods_files"
|
||||
)
|
||||
|
||||
# Save uploaded files to temp directory with unique session ID
|
||||
goods_asset_paths = []
|
||||
if uploaded_files:
|
||||
import uuid
|
||||
session_id = str(uuid.uuid4()).replace('-', '')[:12]
|
||||
temp_dir = Path(f"temp/assets_{session_id}")
|
||||
temp_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
for uploaded_file in uploaded_files:
|
||||
file_path = temp_dir / uploaded_file.name
|
||||
with open(file_path, "wb") as f:
|
||||
f.write(uploaded_file.getbuffer())
|
||||
goods_asset_paths.append(str(file_path.absolute()))
|
||||
|
||||
st.success(tr("digital_human.assets.goods_sucess"))
|
||||
|
||||
# Preview uploaded assets
|
||||
with st.expander(tr("digital_human.assets.preview"), expanded=True):
|
||||
# Show in a grid (3 columns)
|
||||
cols = st.columns(3)
|
||||
for i, (file, path) in enumerate(zip(uploaded_files, goods_asset_paths)):
|
||||
with cols[i % 3]:
|
||||
# Check if image
|
||||
ext = Path(path).suffix.lower()
|
||||
if ext in [".jpg", ".jpeg", ".png", ".webp"]:
|
||||
st.image(file, caption=file.name, use_container_width=True)
|
||||
else:
|
||||
st.info(tr("digital_human.assets.goods_empty_hint"))
|
||||
|
||||
# Goods Name or Description
|
||||
with st.container(border=True):
|
||||
st.markdown(f"**{tr('digital_human.section.goods_info')}**")
|
||||
|
||||
goods_title = st.text_input(
|
||||
tr("digital_human.goods_title"),
|
||||
placeholder=tr("digital_human.goods_title_placeholder"),
|
||||
help=tr("digital_human.goods_title_help"),
|
||||
key="goods_title"
|
||||
)
|
||||
|
||||
return {
|
||||
"character_assets": character_asset_paths,
|
||||
"goods_title": goods_title,
|
||||
"goods_assets": goods_asset_paths
|
||||
}
|
||||
|
||||
def _render_output_preview(self, pixelle_video: Any, video_params: dict):
|
||||
"""Render output preview section"""
|
||||
with st.container(border=True):
|
||||
st.markdown(f"**{tr('section.video_generation')}**")
|
||||
|
||||
# Check configuration
|
||||
if not config_manager.validate():
|
||||
st.warning(tr("settings.not_configured"))
|
||||
|
||||
# Get input data
|
||||
character_assets = video_params.get("character_assets", [])
|
||||
goods_assets = video_params.get("goods_assets", [])
|
||||
goods_title = video_params.get("goods_title", "")
|
||||
first_workflow = video_params.get("first_workflow")
|
||||
second_workflow = video_params.get("second_workflow")
|
||||
tts_voice = video_params.get("tts_voice", "zh-CN-YunjianNeural")
|
||||
tts_speed = video_params.get("tts_speed", 1.2)
|
||||
|
||||
# Debug: 显示获取到的TTS参数
|
||||
logger.info(f"🔧 获取到的TTS参数:")
|
||||
logger.info(f" - tts_voice: {tts_voice}")
|
||||
logger.info(f" - tts_speed: {tts_speed}")
|
||||
logger.info(f" - video_params中的tts_voice: {video_params.get('tts_voice', 'NOT_FOUND')}")
|
||||
logger.info(f" - 所有video_params: {video_params}")
|
||||
|
||||
# Validation
|
||||
if not character_assets or not goods_assets:
|
||||
st.info("请上传人物形象图片和商品图片")
|
||||
st.button(
|
||||
tr("btn.generate"),
|
||||
type="primary",
|
||||
use_container_width=True,
|
||||
disabled=True,
|
||||
key="digital_human_generate_disabled"
|
||||
)
|
||||
return
|
||||
|
||||
if not goods_title:
|
||||
st.warning("请输入商品名称")
|
||||
st.button(
|
||||
tr("btn.generate"),
|
||||
type="primary",
|
||||
use_container_width=True,
|
||||
disabled=True,
|
||||
key="digital_human_generate_disabled_no_title"
|
||||
)
|
||||
return
|
||||
|
||||
# Workflow validation
|
||||
if not first_workflow or not second_workflow:
|
||||
st.warning("请选择工作流配置")
|
||||
st.button(
|
||||
tr("btn.generate"),
|
||||
type="primary",
|
||||
use_container_width=True,
|
||||
disabled=True,
|
||||
key="digital_human_generate_disabled_no_workflow"
|
||||
)
|
||||
return
|
||||
|
||||
# Generate button
|
||||
if st.button(tr("btn.generate"), type="primary", use_container_width=True, key="digital_human_generate"):
|
||||
# Validate
|
||||
if not config_manager.validate():
|
||||
st.error(tr("settings.not_configured"))
|
||||
st.stop()
|
||||
|
||||
# Show progress
|
||||
progress_bar = st.progress(0)
|
||||
status_text = st.empty()
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# Define async generation function
|
||||
async def generate_digital_human_video():
|
||||
# Create task directory
|
||||
task_dir, task_id = create_task_output_dir()
|
||||
logger.info(f"📁 Task directory created: {task_dir}")
|
||||
|
||||
# ============================================================
|
||||
# Step 1: Call first workflow (character_image + goods_image + goods_title)
|
||||
# ============================================================
|
||||
status_text.text("步骤 1/3: 调用第一步工作流生成拼图和文案...")
|
||||
progress_bar.progress(10)
|
||||
|
||||
# Directly load first workflow file
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
first_workflow_path = Path("workflows/runninghub/digital_image.json")
|
||||
if not first_workflow_path.exists():
|
||||
raise Exception(f"第一步工作流文件不存在: {first_workflow_path}")
|
||||
|
||||
with open(first_workflow_path, 'r', encoding='utf-8') as f:
|
||||
first_workflow_config = json.load(f)
|
||||
|
||||
# Build workflow parameters for first workflow
|
||||
# 注意：参数名需要根据你的 workflow 实际参数来调整
|
||||
# 尝试多种可能的参数名
|
||||
first_workflow_params = {
|
||||
# 根据之前的日志尝试的参数名
|
||||
"firstimage": character_assets[0],
|
||||
"secondimage": goods_assets[0],
|
||||
"goodstype": goods_title
|
||||
}
|
||||
|
||||
logger.info(f"🔧 传递给第一步工作流的参数:")
|
||||
for key, value in first_workflow_params.items():
|
||||
if isinstance(value, str) and len(value) > 100:
|
||||
logger.info(f" - {key}: {value[:100]}... (文件路径)")
|
||||
else:
|
||||
logger.info(f" - {key}: {value}")
|
||||
|
||||
# Execute first workflow
|
||||
kit = await pixelle_video._get_or_create_comfykit()
|
||||
|
||||
# Use workflow_id for runninghub workflows
|
||||
if first_workflow_config.get("source") == "runninghub" and "workflow_id" in first_workflow_config:
|
||||
workflow_input = first_workflow_config["workflow_id"]
|
||||
else:
|
||||
workflow_input = str(first_workflow_path)
|
||||
|
||||
logger.info(f"执行第一步工作流: {workflow_input}")
|
||||
first_result = await kit.execute(workflow_input, first_workflow_params)
|
||||
|
||||
if first_result.status != "completed":
|
||||
raise Exception(f"第一步工作流执行失败: {first_result.msg}")
|
||||
|
||||
# Debug: Log the actual result structure
|
||||
logger.info(f"🔍 第一步工作流返回结果调试:")
|
||||
logger.info(f"- result type: {type(first_result)}")
|
||||
logger.info(f"- result.__dict__: {first_result.__dict__}")
|
||||
|
||||
if hasattr(first_result, 'images'):
|
||||
logger.info(f"- images: {first_result.images}")
|
||||
if hasattr(first_result, 'texts'):
|
||||
logger.info(f"- texts: {first_result.texts}")
|
||||
if hasattr(first_result, 'videos'):
|
||||
logger.info(f"- videos: {first_result.videos}")
|
||||
if hasattr(first_result, 'outputs'):
|
||||
logger.info(f"- outputs keys: {list(first_result.outputs.keys()) if first_result.outputs else 'None'}")
|
||||
if first_result.outputs:
|
||||
for node_id, node_output in first_result.outputs.items():
|
||||
logger.info(f" - 节点 {node_id}: {node_output}")
|
||||
|
||||
# 尝试获取所有可能的属性
|
||||
for attr in dir(first_result):
|
||||
if not attr.startswith('_'):
|
||||
try:
|
||||
value = getattr(first_result, attr)
|
||||
if not callable(value):
|
||||
logger.info(f"- {attr}: {value}")
|
||||
except:
|
||||
pass
|
||||
|
||||
# Extract results from first workflow
|
||||
generated_image_url = None
|
||||
generated_text = None
|
||||
generated_video_url = None
|
||||
|
||||
# 检查是否直接返回了视频（一步到位的工作流）
|
||||
if hasattr(first_result, 'videos') and first_result.videos:
|
||||
generated_video_url = first_result.videos[0]
|
||||
logger.info(f"✅ 第一步工作流直接生成了视频: {generated_video_url}")
|
||||
|
||||
# 如果直接生成了视频，跳过后续步骤
|
||||
progress_bar.progress(100)
|
||||
status_text.text(tr("status.success"))
|
||||
|
||||
# Download video to local
|
||||
final_video_path = os.path.join(task_dir, "final.mp4")
|
||||
timeout = httpx.Timeout(300.0) # 简化timeout设置
|
||||
async with httpx.AsyncClient(timeout=timeout) as client:
|
||||
response = await client.get(generated_video_url)
|
||||
response.raise_for_status()
|
||||
with open(final_video_path, 'wb') as f:
|
||||
f.write(response.content)
|
||||
|
||||
return final_video_path
|
||||
|
||||
# 如果没有直接返回视频，尝试提取图片和文本（原来的逻辑）
|
||||
# Extract image - try direct access first
|
||||
if hasattr(first_result, 'images') and first_result.images:
|
||||
generated_image_url = first_result.images[0]
|
||||
logger.info(f"✅ 从 result.images 获取图片: {generated_image_url}")
|
||||
elif hasattr(first_result, 'outputs') and first_result.outputs:
|
||||
# Try to find image in outputs
|
||||
for node_id, node_output in first_result.outputs.items():
|
||||
logger.info(f"- 检查节点 {node_id}: {list(node_output.keys()) if isinstance(node_output, dict) else type(node_output)}")
|
||||
if isinstance(node_output, dict) and 'images' in node_output:
|
||||
images = node_output['images']
|
||||
if images and len(images) > 0:
|
||||
generated_image_url = images[0]
|
||||
logger.info(f"✅ 从 outputs[{node_id}].images 获取图片: {generated_image_url}")
|
||||
break
|
||||
|
||||
# Extract text - try direct access first
|
||||
if hasattr(first_result, 'texts') and first_result.texts:
|
||||
generated_text = first_result.texts[0]
|
||||
logger.info(f"✅ 从 result.texts 获取文本: {generated_text[:50]}...")
|
||||
elif hasattr(first_result, 'outputs') and first_result.outputs:
|
||||
# Try to find text in outputs
|
||||
for node_id, node_output in first_result.outputs.items():
|
||||
if isinstance(node_output, dict) and 'text' in node_output:
|
||||
text_list = node_output['text']
|
||||
if text_list and len(text_list) > 0:
|
||||
generated_text = text_list[0]
|
||||
logger.info(f"✅ 从 outputs[{node_id}].text 获取文本: {generated_text[:50]}...")
|
||||
break
|
||||
|
||||
if not generated_image_url:
|
||||
# 如果没有找到图片，尝试查看是否有其他类型的输出
|
||||
logger.error("❌ 第一步工作流未返回图片，尝试查找其他输出...")
|
||||
if hasattr(first_result, 'outputs') and first_result.outputs:
|
||||
for node_id, node_output in first_result.outputs.items():
|
||||
logger.info(f"- 节点 {node_id} 输出: {node_output}")
|
||||
|
||||
# 尝试使用第一个可用的图片输出（如果有的话）
|
||||
if hasattr(first_result, 'outputs') and first_result.outputs:
|
||||
for node_id, node_output in first_result.outputs.items():
|
||||
if isinstance(node_output, dict):
|
||||
# 检查所有可能的图片字段
|
||||
for key in ['images', 'image', 'output_image', 'result_image']:
|
||||
if key in node_output and node_output[key]:
|
||||
if isinstance(node_output[key], list) and len(node_output[key]) > 0:
|
||||
generated_image_url = node_output[key][0]
|
||||
logger.info(f"✅ 从 outputs[{node_id}].{key} 获取图片: {generated_image_url}")
|
||||
break
|
||||
elif isinstance(node_output[key], str):
|
||||
generated_image_url = node_output[key]
|
||||
logger.info(f"✅ 从 outputs[{node_id}].{key} 获取图片: {generated_image_url}")
|
||||
break
|
||||
if generated_image_url:
|
||||
break
|
||||
|
||||
if not generated_image_url:
|
||||
raise Exception("第一步工作流未返回图片，请检查工作流配置")
|
||||
|
||||
if not generated_text:
|
||||
# 如果没有找到文本，尝试查找其他文本输出
|
||||
logger.warning("⚠️ 第一步工作流未返回文本，尝试查找其他文本输出...")
|
||||
if hasattr(first_result, 'outputs') and first_result.outputs:
|
||||
for node_id, node_output in first_result.outputs.items():
|
||||
if isinstance(node_output, dict):
|
||||
# 检查所有可能的文本字段
|
||||
for key in ['text', 'texts', 'output_text', 'result_text', 'description', 'caption']:
|
||||
if key in node_output and node_output[key]:
|
||||
if isinstance(node_output[key], list) and len(node_output[key]) > 0:
|
||||
generated_text = node_output[key][0]
|
||||
logger.info(f"✅ 从 outputs[{node_id}].{key} 获取文本: {generated_text[:50]}...")
|
||||
break
|
||||
elif isinstance(node_output[key], str):
|
||||
generated_text = node_output[key]
|
||||
logger.info(f"✅ 从 outputs[{node_id}].{key} 获取文本: {generated_text[:50]}...")
|
||||
break
|
||||
if generated_text:
|
||||
break
|
||||
|
||||
# 如果还是没有文本，使用默认文本
|
||||
if not generated_text:
|
||||
generated_text = f"为您推荐这款{goods_title}，品质优良，值得拥有！"
|
||||
logger.warning(f"⚠️ 使用默认文本: {generated_text}")
|
||||
|
||||
logger.info(f"✅ 第一步完成: 图片={generated_image_url}, 文本={generated_text[:50]}...")
|
||||
|
||||
# Download generated image to local
|
||||
generated_image_path = os.path.join(task_dir, "generated_image.png")
|
||||
timeout = httpx.Timeout(10.0) # 简化timeout设置
|
||||
async with httpx.AsyncClient(timeout=timeout) as client:
|
||||
response = await client.get(generated_image_url)
|
||||
response.raise_for_status()
|
||||
with open(generated_image_path, 'wb') as f:
|
||||
f.write(response.content)
|
||||
|
||||
progress_bar.progress(40)
|
||||
status_text.text("步骤 2/3: 将文案转换为语音...")
|
||||
|
||||
# ============================================================
|
||||
# Step 2: TTS conversion (text -> audio)
|
||||
# ============================================================
|
||||
audio_path = os.path.join(task_dir, "narration.mp3")
|
||||
await pixelle_video.tts(
|
||||
text=generated_text,
|
||||
output_path=audio_path,
|
||||
inference_mode="local", # 明确指定推理模式
|
||||
voice=tts_voice, # 使用voice而不是voice_id
|
||||
speed=tts_speed
|
||||
)
|
||||
|
||||
logger.info(f"✅ 第二步完成: 语音={audio_path}")
|
||||
progress_bar.progress(70)
|
||||
status_text.text("步骤 3/3: 调用第二步工作流生成最终视频...")
|
||||
|
||||
# ============================================================
|
||||
# Step 3: Call second workflow (generated_image + audio)
|
||||
# ============================================================
|
||||
# Directly load second workflow file
|
||||
second_workflow_path = Path("workflows/runninghub/digital_combination.json")
|
||||
if not second_workflow_path.exists():
|
||||
raise Exception(f"第二步工作流文件不存在: {second_workflow_path}")
|
||||
|
||||
with open(second_workflow_path, 'r', encoding='utf-8') as f:
|
||||
second_workflow_config = json.load(f)
|
||||
|
||||
# Build workflow parameters for second workflow
|
||||
# 注意：参数名需要根据你的 workflow 实际参数来调整
|
||||
# 尝试多种可能的参数名
|
||||
second_workflow_params = {
|
||||
# 尝试常见的参数名
|
||||
"videoimage": generated_image_path, # 生成的图片路径
|
||||
"audio": audio_path, # 音频路径
|
||||
}
|
||||
|
||||
logger.info(f"🔧 传递给第二步工作流的参数:")
|
||||
for key, value in second_workflow_params.items():
|
||||
logger.info(f" - {key}: {value}")
|
||||
|
||||
# Execute second workflow
|
||||
if second_workflow_config.get("source") == "runninghub" and "workflow_id" in second_workflow_config:
|
||||
workflow_input = second_workflow_config["workflow_id"]
|
||||
else:
|
||||
workflow_input = str(second_workflow_path)
|
||||
|
||||
logger.info(f"执行第二步工作流: {workflow_input}")
|
||||
second_result = await kit.execute(workflow_input, second_workflow_params)
|
||||
|
||||
if second_result.status != "completed":
|
||||
raise Exception(f"第二步工作流执行失败: {second_result.msg}")
|
||||
|
||||
# Debug: Log the second workflow result structure
|
||||
logger.info(f"🔍 第二步工作流返回结果调试:")
|
||||
logger.info(f"- result type: {type(second_result)}")
|
||||
logger.info(f"- result.__dict__: {second_result.__dict__}")
|
||||
|
||||
if hasattr(second_result, 'videos'):
|
||||
logger.info(f"- videos: {second_result.videos}")
|
||||
if hasattr(second_result, 'images'):
|
||||
logger.info(f"- images: {second_result.images}")
|
||||
if hasattr(second_result, 'texts'):
|
||||
logger.info(f"- texts: {second_result.texts}")
|
||||
if hasattr(second_result, 'outputs'):
|
||||
logger.info(f"- outputs keys: {list(second_result.outputs.keys()) if second_result.outputs else 'None'}")
|
||||
if second_result.outputs:
|
||||
for node_id, node_output in second_result.outputs.items():
|
||||
logger.info(f" - 节点 {node_id}: {node_output}")
|
||||
|
||||
# 尝试获取所有可能的属性
|
||||
for attr in dir(second_result):
|
||||
if not attr.startswith('_'):
|
||||
try:
|
||||
value = getattr(second_result, attr)
|
||||
if not callable(value):
|
||||
logger.info(f"- {attr}: {value}")
|
||||
except:
|
||||
pass
|
||||
|
||||
# Extract video from second workflow result
|
||||
generated_video_url = None
|
||||
|
||||
if hasattr(second_result, 'videos') and second_result.videos:
|
||||
generated_video_url = second_result.videos[0]
|
||||
logger.info(f"✅ 从 result.videos 获取视频: {generated_video_url}")
|
||||
elif hasattr(second_result, 'outputs') and second_result.outputs:
|
||||
for node_id, node_output in second_result.outputs.items():
|
||||
logger.info(f"- 检查节点 {node_id}: {list(node_output.keys()) if isinstance(node_output, dict) else type(node_output)}")
|
||||
if isinstance(node_output, dict) and 'videos' in node_output:
|
||||
videos = node_output['videos']
|
||||
if videos and len(videos) > 0:
|
||||
generated_video_url = videos[0]
|
||||
logger.info(f"✅ 从 outputs[{node_id}].videos 获取视频: {generated_video_url}")
|
||||
break
|
||||
|
||||
if not generated_video_url:
|
||||
# 如果没有找到视频，尝试查看是否有其他类型的输出
|
||||
logger.error("❌ 第二步工作流未返回视频，尝试查找其他输出...")
|
||||
if hasattr(second_result, 'outputs') and second_result.outputs:
|
||||
for node_id, node_output in second_result.outputs.items():
|
||||
logger.info(f"- 节点 {node_id} 输出: {node_output}")
|
||||
|
||||
# 尝试使用第一个可用的视频输出（如果有的话）
|
||||
if hasattr(second_result, 'outputs') and second_result.outputs:
|
||||
for node_id, node_output in second_result.outputs.items():
|
||||
if isinstance(node_output, dict):
|
||||
# 检查所有可能的视频字段
|
||||
for key in ['videos', 'video', 'output_video', 'result_video', 'mp4']:
|
||||
if key in node_output and node_output[key]:
|
||||
if isinstance(node_output[key], list) and len(node_output[key]) > 0:
|
||||
generated_video_url = node_output[key][0]
|
||||
logger.info(f"✅ 从 outputs[{node_id}].{key} 获取视频: {generated_video_url}")
|
||||
break
|
||||
elif isinstance(node_output[key], str):
|
||||
generated_video_url = node_output[key]
|
||||
logger.info(f"✅ 从 outputs[{node_id}].{key} 获取视频: {generated_video_url}")
|
||||
break
|
||||
if generated_video_url:
|
||||
break
|
||||
|
||||
if not generated_video_url:
|
||||
raise Exception("第二步工作流未返回视频，请检查工作流配置")
|
||||
|
||||
logger.info(f"✅ 第三步完成: 视频={generated_video_url}")
|
||||
|
||||
# Download video to local
|
||||
final_video_path = os.path.join(task_dir, "final.mp4")
|
||||
timeout = httpx.Timeout(300.0) # 简化timeout设置
|
||||
async with httpx.AsyncClient(timeout=timeout) as client:
|
||||
response = await client.get(generated_video_url)
|
||||
response.raise_for_status()
|
||||
with open(final_video_path, 'wb') as f:
|
||||
f.write(response.content)
|
||||
|
||||
return final_video_path
|
||||
|
||||
# Execute async generation
|
||||
final_video_path = run_async(generate_digital_human_video())
|
||||
|
||||
total_time = time.time() - start_time
|
||||
progress_bar.progress(100)
|
||||
status_text.text(tr("status.success"))
|
||||
|
||||
# Display result
|
||||
st.success(tr("status.video_generated", path=final_video_path))
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
# Video info
|
||||
if os.path.exists(final_video_path):
|
||||
file_size_mb = os.path.getsize(final_video_path) / (1024 * 1024)
|
||||
|
||||
info_text = (
|
||||
f"⏱️ {tr('info.generation_time')} {total_time:.1f}s "
|
||||
f"📦 {file_size_mb:.2f}MB"
|
||||
)
|
||||
st.caption(info_text)
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
# Video preview
|
||||
st.video(final_video_path)
|
||||
|
||||
# Download button
|
||||
with open(final_video_path, "rb") as video_file:
|
||||
video_bytes = video_file.read()
|
||||
video_filename = os.path.basename(final_video_path)
|
||||
st.download_button(
|
||||
label="⬇️ 下载视频" if get_language() == "zh_CN" else "⬇️ Download Video",
|
||||
data=video_bytes,
|
||||
file_name=video_filename,
|
||||
mime="video/mp4",
|
||||
use_container_width=True
|
||||
)
|
||||
else:
|
||||
st.error(tr("status.video_not_found", path=final_video_path))
|
||||
|
||||
except Exception as e:
|
||||
status_text.text("")
|
||||
progress_bar.empty()
|
||||
st.error(tr("status.error", error=str(e)))
|
||||
logger.exception(e)
|
||||
st.stop()
|
||||
|
||||
|
||||
# Register self
|
||||
register_pipeline_ui(DigitalHumanPipelineUI)
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
{
|
||||
"source": "runninghub",
|
||||
"workflow_id": "2003717471859294210"
|
||||
}
|
||||
@@ -0,0 +1,4 @@
|
||||
{
|
||||
"source": "runninghub",
|
||||
"workflow_id": "2004120336125861890"
|
||||
}
|
||||
Reference in new issue
Block a user