436 lines
17 KiB
Python
436 lines
17 KiB
Python
# 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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Frame processor - Process single frame through complete pipeline
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Orchestrates: TTS → Image Generation → Frame Composition → Video Segment
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Key Feature:
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- TTS-driven video duration: Audio duration from TTS is passed to video generation workflows
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to ensure perfect sync between audio and video (no padding, no trimming needed)
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"""
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from typing import Callable, Optional
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import httpx
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from loguru import logger
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from pixelle_video.models.progress import ProgressEvent
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from pixelle_video.models.storyboard import Storyboard, StoryboardFrame, StoryboardConfig
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class FrameProcessor:
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"""Frame processor"""
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def __init__(self, pixelle_video_core):
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"""
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Initialize
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Args:
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pixelle_video_core: PixelleVideoCore instance
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"""
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self.core = pixelle_video_core
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async def __call__(
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self,
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frame: StoryboardFrame,
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storyboard: 'Storyboard',
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config: StoryboardConfig,
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total_frames: int = 1,
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progress_callback: Optional[Callable[[ProgressEvent], None]] = None
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) -> StoryboardFrame:
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"""
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Process single frame through complete pipeline
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Steps:
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1. Generate audio (TTS)
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2. Generate image (ComfyKit)
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3. Compose frame (add subtitle)
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4. Create video segment (image + audio)
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Args:
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frame: Storyboard frame to process
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storyboard: Storyboard instance
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config: Storyboard configuration
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total_frames: Total number of frames in storyboard
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progress_callback: Optional callback for progress updates (receives ProgressEvent)
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Returns:
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Processed frame with all paths filled
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"""
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logger.info(f"Processing frame {frame.index}...")
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frame_num = frame.index + 1
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# Determine if this frame needs image generation
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needs_image = frame.image_prompt is not None
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try:
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# Step 1: Generate audio (TTS)
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if progress_callback:
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progress_callback(ProgressEvent(
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event_type="frame_step",
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progress=0.0,
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frame_current=frame_num,
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frame_total=total_frames,
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step=1,
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action="audio"
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))
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await self._step_generate_audio(frame, config)
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# Step 2: Generate media (image or video, conditional)
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if needs_image:
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if progress_callback:
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progress_callback(ProgressEvent(
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event_type="frame_step",
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progress=0.25,
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frame_current=frame_num,
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frame_total=total_frames,
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step=2,
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action="media"
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))
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await self._step_generate_media(frame, config)
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else:
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frame.image_path = None
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frame.media_type = None
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logger.debug(f" 2/4: Skipped media generation (not required by template)")
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# Step 3: Compose frame (add subtitle)
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if progress_callback:
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progress_callback(ProgressEvent(
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event_type="frame_step",
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progress=0.50 if needs_image else 0.33,
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frame_current=frame_num,
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frame_total=total_frames,
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step=3,
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action="compose"
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))
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await self._step_compose_frame(frame, storyboard, config)
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# Step 4: Create video segment
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if progress_callback:
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progress_callback(ProgressEvent(
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event_type="frame_step",
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progress=0.75 if needs_image else 0.67,
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frame_current=frame_num,
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frame_total=total_frames,
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step=4,
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action="video"
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))
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await self._step_create_video_segment(frame, config)
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logger.info(f"✅ Frame {frame.index} completed")
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return frame
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except Exception as e:
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logger.error(f"❌ Failed to process frame {frame.index}: {e}")
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raise
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async def _step_generate_audio(
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self,
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frame: StoryboardFrame,
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config: StoryboardConfig
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):
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"""Step 1: Generate audio using TTS"""
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logger.debug(f" 1/4: Generating audio for frame {frame.index}...")
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# Generate output path using task_id
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from pixelle_video.utils.os_util import get_task_frame_path
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output_path = get_task_frame_path(config.task_id, frame.index, "audio")
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# Build TTS params based on inference mode
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tts_params = {
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"text": frame.narration,
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"inference_mode": config.tts_inference_mode,
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"output_path": output_path,
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"index": frame.index + 1, # 1-based index for workflow
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}
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if config.tts_inference_mode == "local":
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# Local mode: pass voice and speed
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if config.voice_id:
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tts_params["voice"] = config.voice_id
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if config.tts_speed is not None:
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tts_params["speed"] = config.tts_speed
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else: # comfyui
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# ComfyUI mode: pass workflow, voice, speed, and ref_audio
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if config.tts_workflow:
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tts_params["workflow"] = config.tts_workflow
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if config.voice_id:
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tts_params["voice"] = config.voice_id
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if config.tts_speed is not None:
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tts_params["speed"] = config.tts_speed
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if config.ref_audio:
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tts_params["ref_audio"] = config.ref_audio
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audio_path = await self.core.tts(**tts_params)
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frame.audio_path = audio_path
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# Get audio duration
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frame.duration = await self._get_audio_duration(audio_path)
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logger.debug(f" ✓ Audio generated: {audio_path} ({frame.duration:.2f}s)")
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async def _step_generate_media(
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self,
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frame: StoryboardFrame,
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config: StoryboardConfig
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):
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"""Step 2: Generate media (image or video) using ComfyKit"""
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logger.debug(f" 2/4: Generating media for frame {frame.index}...")
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# Determine media type based on workflow
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# video_ prefix in workflow name indicates video generation
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workflow_name = config.media_workflow or ""
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is_video_workflow = "video_" in workflow_name.lower()
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media_type = "video" if is_video_workflow else "image"
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logger.debug(f" → Media type: {media_type} (workflow: {workflow_name})")
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# Build media generation parameters
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media_params = {
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"prompt": frame.image_prompt,
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"workflow": config.media_workflow, # Pass workflow from config (None = use default)
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"media_type": media_type,
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"width": config.media_width,
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"height": config.media_height,
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"index": frame.index + 1, # 1-based index for workflow
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}
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# For video workflows: pass audio duration as target video duration
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# This ensures video length matches audio length from the source
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if is_video_workflow and frame.duration:
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media_params["duration"] = frame.duration
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logger.info(f" → Generating video with target duration: {frame.duration:.2f}s (from TTS audio)")
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# Call Media generation
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media_result = await self.core.media(**media_params)
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# Store media type
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frame.media_type = media_result.media_type
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if media_result.is_image:
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# Download image to local (pass task_id)
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local_path = await self._download_media(
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media_result.url,
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frame.index,
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config.task_id,
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media_type="image"
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)
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frame.image_path = local_path
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logger.debug(f" ✓ Image generated: {local_path}")
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elif media_result.is_video:
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# Download video to local (pass task_id)
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local_path = await self._download_media(
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media_result.url,
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frame.index,
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config.task_id,
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media_type="video"
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)
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frame.video_path = local_path
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# Update duration from video if available
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if media_result.duration:
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frame.duration = media_result.duration
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logger.debug(f" ✓ Video generated: {local_path} (duration: {frame.duration:.2f}s)")
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else:
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# Get video duration from file
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frame.duration = await self._get_video_duration(local_path)
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logger.debug(f" ✓ Video generated: {local_path} (duration: {frame.duration:.2f}s)")
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else:
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raise ValueError(f"Unknown media type: {media_result.media_type}")
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async def _step_compose_frame(
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self,
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frame: StoryboardFrame,
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storyboard: 'Storyboard',
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config: StoryboardConfig
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):
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"""Step 3: Compose frame with subtitle using HTML template"""
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logger.debug(f" 3/4: Composing frame {frame.index}...")
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# Generate output path using task_id
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from pixelle_video.utils.os_util import get_task_frame_path
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output_path = get_task_frame_path(config.task_id, frame.index, "composed")
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# For video type: render HTML as transparent overlay image
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# For image type: render HTML with image background
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# In both cases, we need the composed image
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composed_path = await self._compose_frame_html(frame, storyboard, config, output_path)
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frame.composed_image_path = composed_path
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logger.debug(f" ✓ Frame composed: {composed_path}")
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async def _compose_frame_html(
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self,
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frame: StoryboardFrame,
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storyboard: 'Storyboard',
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config: StoryboardConfig,
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output_path: str
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) -> str:
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"""Compose frame using HTML template"""
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from pixelle_video.services.frame_html import HTMLFrameGenerator
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from pixelle_video.utils.template_util import resolve_template_path
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# Resolve template path (handles various input formats)
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template_path = resolve_template_path(config.frame_template)
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# Get content metadata from storyboard
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content_metadata = storyboard.content_metadata if storyboard else None
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# Build ext data
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ext = {}
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if content_metadata:
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ext["content_title"] = content_metadata.title or ""
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ext["content_author"] = content_metadata.author or ""
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ext["content_subtitle"] = content_metadata.subtitle or ""
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ext["content_genre"] = content_metadata.genre or ""
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# Add frame index (1-based)
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ext["index"] = frame.index + 1
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# Add custom template parameters
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if config.template_params:
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ext.update(config.template_params)
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# Generate frame using HTML (size is auto-parsed from template path)
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generator = HTMLFrameGenerator(template_path)
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composed_path = await generator.generate_frame(
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title=storyboard.title,
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text=frame.narration,
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image=frame.image_path,
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ext=ext,
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output_path=output_path
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)
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return composed_path
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async def _step_create_video_segment(
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self,
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frame: StoryboardFrame,
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config: StoryboardConfig
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):
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"""Step 4: Create video segment from media + audio"""
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logger.debug(f" 4/4: Creating video segment for frame {frame.index}...")
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# Generate output path using task_id
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from pixelle_video.utils.os_util import get_task_frame_path
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output_path = get_task_frame_path(config.task_id, frame.index, "segment")
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from pixelle_video.services.video import VideoService
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video_service = VideoService()
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# Branch based on media type
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if frame.media_type == "video":
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# Video workflow: overlay HTML template on video, then add audio
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logger.debug(f" → Using video-based composition with HTML overlay")
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# Step 1: Overlay transparent HTML image on video
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# The composed_image_path contains the rendered HTML with transparent background
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temp_video_with_overlay = get_task_frame_path(config.task_id, frame.index, "video") + "_overlay.mp4"
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video_service.overlay_image_on_video(
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video=frame.video_path,
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overlay_image=frame.composed_image_path,
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output=temp_video_with_overlay,
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scale_mode="contain" # Scale video to fit template size (contain mode)
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)
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# Step 2: Add narration audio to the overlaid video
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# Note: The video might have audio (replaced) or be silent (audio added)
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segment_path = video_service.merge_audio_video(
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video=temp_video_with_overlay,
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audio=frame.audio_path,
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output=output_path,
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replace_audio=True, # Replace video audio with narration
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audio_volume=1.0
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)
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# Clean up temp file
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import os
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if os.path.exists(temp_video_with_overlay):
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os.unlink(temp_video_with_overlay)
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elif frame.media_type == "image" or frame.media_type is None:
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# Image workflow: create video from image + audio
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logger.debug(f" → Using image-based composition")
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segment_path = video_service.create_video_from_image(
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image=frame.composed_image_path,
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audio=frame.audio_path,
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output=output_path,
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fps=config.video_fps
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)
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else:
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raise ValueError(f"Unknown media type: {frame.media_type}")
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frame.video_segment_path = segment_path
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logger.debug(f" ✓ Video segment created: {segment_path}")
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async def _get_audio_duration(self, audio_path: str) -> float:
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"""Get audio duration in seconds"""
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try:
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# Try using ffmpeg-python
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import ffmpeg
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probe = ffmpeg.probe(audio_path)
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duration = float(probe['format']['duration'])
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return duration
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except Exception as e:
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logger.warning(f"Failed to get audio duration: {e}, using estimate")
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# Fallback: estimate based on file size (very rough)
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import os
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file_size = os.path.getsize(audio_path)
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# Assume ~16kbps for MP3, so 2KB per second
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estimated_duration = file_size / 2000
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return max(1.0, estimated_duration) # At least 1 second
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async def _download_media(
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self,
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url: str,
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frame_index: int,
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task_id: str,
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media_type: str
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) -> str:
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"""Download media (image or video) from URL to local file"""
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from pixelle_video.utils.os_util import get_task_frame_path
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output_path = get_task_frame_path(task_id, frame_index, media_type)
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timeout = httpx.Timeout(connect=10.0, read=60, write=60, pool=60)
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async with httpx.AsyncClient(timeout=timeout) as client:
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response = await client.get(url)
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response.raise_for_status()
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with open(output_path, 'wb') as f:
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f.write(response.content)
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return output_path
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async def _get_video_duration(self, video_path: str) -> float:
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"""Get video duration in seconds"""
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try:
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import ffmpeg
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probe = ffmpeg.probe(video_path)
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duration = float(probe['format']['duration'])
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return duration
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except Exception as e:
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logger.warning(f"Failed to get video duration: {e}, using audio duration")
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# Fallback: use audio duration if available
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return 1.0 # Default to 1 second if unable to determine
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