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4 Commits
feat/api-n
...
colored-lo
| Author | SHA1 | Date | |
|---|---|---|---|
| 2e2502affe | |||
| b293f8cefd | |||
| 2ca1480f91 | |||
| 6ecf5eca7a |
@ -5,6 +5,24 @@ import logging
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import sys
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import threading
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ANSI_LEVEL_COLORS = {
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'DEBUG': '\033[36m', # cyan
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'INFO': '\033[32m', # green
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'WARNING': '\033[33m', # yellow
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'ERROR': '\033[31m', # red
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'CRITICAL': '\033[35m', # magenta
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}
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ANSI_RESET = '\033[0m'
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ANSI_BOLD = '\033[1m'
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class ColoredFormatter(logging.Formatter):
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def format(self, record):
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color = ANSI_LEVEL_COLORS.get(record.levelname, '')
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bold = ANSI_BOLD if record.levelno >= logging.WARNING else ''
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level_tag = f"{bold}{color}[{record.levelname}]{ANSI_RESET} "
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return level_tag + super().format(record)
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logs = None
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stdout_interceptor = None
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stderr_interceptor = None
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@ -51,7 +69,7 @@ def on_flush(callback):
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if stderr_interceptor is not None:
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stderr_interceptor.on_flush(callback)
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def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool = False):
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def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool = False, color_logs: bool = False):
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global logs
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if logs:
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return
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@ -68,8 +86,10 @@ def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool
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logger = logging.getLogger()
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logger.setLevel(log_level)
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formatter = ColoredFormatter("%(message)s") if color_logs else logging.Formatter("%(message)s")
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stream_handler = logging.StreamHandler()
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stream_handler.setFormatter(logging.Formatter("%(message)s"))
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stream_handler.setFormatter(formatter)
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if use_stdout:
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# Only errors and critical to stderr
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@ -77,7 +97,7 @@ def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool
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# Lesser to stdout
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stdout_handler = logging.StreamHandler(sys.stdout)
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stdout_handler.setFormatter(logging.Formatter("%(message)s"))
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stdout_handler.setFormatter(formatter)
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stdout_handler.addFilter(lambda record: record.levelno < logging.ERROR)
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logger.addHandler(stdout_handler)
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@ -183,6 +183,7 @@ parser.add_argument("--multi-user", action="store_true", help="Enables per-user
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parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level')
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parser.add_argument("--log-stdout", action="store_true", help="Send normal process output to stdout instead of stderr (default).")
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parser.add_argument("--color-logs", action="store_true", help="Enable ANSI colored log output in the terminal.")
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# The default built-in provider hosted under web/
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@ -43,15 +43,16 @@ from comfy_api_nodes.util import (
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ApiEndpoint,
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download_url_to_image_tensor,
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download_url_to_video_output,
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downscale_video_to_max_pixels,
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get_number_of_images,
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image_tensor_pair_to_batch,
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poll_op,
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resize_video_to_pixel_budget,
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sync_op,
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upload_audio_to_comfyapi,
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upload_image_to_comfyapi,
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upload_images_to_comfyapi,
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upload_video_to_comfyapi,
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upscale_video_to_min_pixels,
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validate_image_aspect_ratio,
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validate_image_dimensions,
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validate_string,
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@ -110,12 +111,13 @@ def _validate_ref_video_pixels(video: Input.Video, model_id: str, resolution: st
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max_px = limits.get("max")
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if min_px and pixels < min_px:
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raise ValueError(
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f"Reference video {index} is too small: {w}x{h} = {pixels:,}px. " f"Minimum is {min_px:,}px for this model."
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f"Reference video {index} is too small: {w}x{h} = {pixels:,} total pixels. "
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f"Minimum for this model is {min_px:,} total pixels."
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)
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if max_px and pixels > max_px:
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raise ValueError(
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f"Reference video {index} is too large: {w}x{h} = {pixels:,}px. "
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f"Maximum is {max_px:,}px for this model. Try downscaling the video."
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f"Reference video {index} is too large: {w}x{h} = {pixels:,} total pixels. "
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f"Maximum for this model is {max_px:,} total pixels. Try downscaling the video."
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)
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@ -1676,14 +1678,14 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
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"first_frame_asset_id",
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default="",
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tooltip="Seedance asset_id to use as the first frame. "
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"Mutually exclusive with the first_frame image input.",
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"Mutually exclusive with the first_frame image input.",
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optional=True,
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),
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IO.String.Input(
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"last_frame_asset_id",
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default="",
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tooltip="Seedance asset_id to use as the last frame. "
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"Mutually exclusive with the last_frame image input.",
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"Mutually exclusive with the last_frame image input.",
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optional=True,
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),
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IO.Int.Input(
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@ -1865,11 +1867,20 @@ def _seedance2_reference_inputs(resolutions: list[str], default_ratio: str = "16
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IO.Boolean.Input(
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"auto_downscale",
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default=False,
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advanced=True,
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optional=True,
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tooltip="Automatically downscale reference videos that exceed the model's pixel budget "
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"for the selected resolution. Aspect ratio is preserved; videos already within limits are untouched.",
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),
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IO.Boolean.Input(
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"auto_upscale",
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default=False,
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advanced=True,
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optional=True,
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tooltip="Automatically upscale reference videos that are below the model's minimum pixel count "
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"for the selected resolution. Aspect ratio is preserved; videos already meeting the minimum are "
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"untouched. Note: upscaling a low-resolution source does not add real detail and may produce "
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"lower-quality generations.",
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),
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IO.Autogrow.Input(
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"reference_assets",
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template=IO.Autogrow.TemplateNames(
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@ -2030,7 +2041,13 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
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max_px = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {}).get(model["resolution"], {}).get("max")
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if max_px:
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for key in reference_videos:
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reference_videos[key] = resize_video_to_pixel_budget(reference_videos[key], max_px)
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reference_videos[key] = downscale_video_to_max_pixels(reference_videos[key], max_px)
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if model.get("auto_upscale") and reference_videos:
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min_px = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {}).get(model["resolution"], {}).get("min")
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if min_px:
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for key in reference_videos:
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reference_videos[key] = upscale_video_to_min_pixels(reference_videos[key], min_px)
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total_video_duration = 0.0
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for i, key in enumerate(reference_videos, 1):
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@ -16,16 +16,17 @@ from .conversions import (
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convert_mask_to_image,
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downscale_image_tensor,
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downscale_image_tensor_by_max_side,
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downscale_video_to_max_pixels,
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image_tensor_pair_to_batch,
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pil_to_bytesio,
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resize_mask_to_image,
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resize_video_to_pixel_budget,
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tensor_to_base64_string,
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tensor_to_bytesio,
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tensor_to_pil,
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text_filepath_to_base64_string,
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text_filepath_to_data_uri,
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trim_video,
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upscale_video_to_min_pixels,
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video_to_base64_string,
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)
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from .download_helpers import (
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@ -88,16 +89,17 @@ __all__ = [
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"convert_mask_to_image",
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"downscale_image_tensor",
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"downscale_image_tensor_by_max_side",
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"downscale_video_to_max_pixels",
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"image_tensor_pair_to_batch",
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"pil_to_bytesio",
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"resize_mask_to_image",
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"resize_video_to_pixel_budget",
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"tensor_to_base64_string",
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"tensor_to_bytesio",
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"tensor_to_pil",
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"text_filepath_to_base64_string",
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"text_filepath_to_data_uri",
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"trim_video",
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"upscale_video_to_min_pixels",
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"video_to_base64_string",
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# Validation utilities
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"get_image_dimensions",
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@ -415,14 +415,48 @@ def trim_video(video: Input.Video, duration_sec: float) -> Input.Video:
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raise RuntimeError(f"Failed to trim video: {str(e)}") from e
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def resize_video_to_pixel_budget(video: Input.Video, total_pixels: int) -> Input.Video:
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"""Downscale a video to fit within ``total_pixels`` (w * h), preserving aspect ratio.
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def downscale_video_to_max_pixels(video: Input.Video, max_pixels: int) -> Input.Video:
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"""Downscale a video to fit within ``max_pixels`` (w * h), preserving aspect ratio.
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Returns the original video object untouched when it already fits. Preserves frame rate, duration, and audio.
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Aspect ratio is preserved up to a fraction of a percent (even-dim rounding).
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"""
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src_w, src_h = video.get_dimensions()
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scale_dims = _compute_downscale_dims(src_w, src_h, total_pixels)
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scale_dims = _compute_downscale_dims(src_w, src_h, max_pixels)
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if scale_dims is None:
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return video
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return _apply_video_scale(video, scale_dims)
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def _compute_upscale_dims(src_w: int, src_h: int, total_pixels: int) -> tuple[int, int] | None:
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"""Return upscaled (w, h) with even dims meeting at least ``total_pixels``, or None if already large enough.
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Source aspect ratio is preserved; output may drift by a fraction of a percent because both dimensions
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are rounded up to even values (many codecs require divisible-by-2). The result is guaranteed to be at
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least ``total_pixels``.
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"""
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pixels = src_w * src_h
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if pixels >= total_pixels:
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return None
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scale = math.sqrt(total_pixels / pixels)
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new_w = math.ceil(src_w * scale)
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new_h = math.ceil(src_h * scale)
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if new_w % 2:
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new_w += 1
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if new_h % 2:
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new_h += 1
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return new_w, new_h
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def upscale_video_to_min_pixels(video: Input.Video, min_pixels: int) -> Input.Video:
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"""Upscale a video to meet at least ``min_pixels`` (w * h), preserving aspect ratio.
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Returns the original video object untouched when it already meets the minimum. Preserves frame rate,
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duration, and audio. Aspect ratio is preserved up to a fraction of a percent (even-dim rounding).
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Note: upscaling a low-resolution source does not add real detail; downstream model quality may suffer.
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"""
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src_w, src_h = video.get_dimensions()
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scale_dims = _compute_upscale_dims(src_w, src_h, min_pixels)
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if scale_dims is None:
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return video
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return _apply_video_scale(video, scale_dims)
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2
main.py
2
main.py
@ -17,7 +17,7 @@ import folder_paths
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import time
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from comfy.cli_args import enables_dynamic_vram
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from app.logger import setup_logger
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setup_logger(log_level=args.verbose, use_stdout=args.log_stdout)
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setup_logger(log_level=args.verbose, use_stdout=args.log_stdout, color_logs=args.color_logs)
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from app.assets.seeder import asset_seeder
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from app.assets.services import register_output_files
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@ -1,5 +1,5 @@
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comfyui-frontend-package==1.43.18
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comfyui-workflow-templates==0.9.79
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comfyui-workflow-templates==0.9.82
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comfyui-embedded-docs==0.5.0
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torch
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torchsde
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