From 7b0a0e281742f0969267d754b65df8b6d3139c16 Mon Sep 17 00:00:00 2001 From: bigcat88 Date: Sat, 18 Apr 2026 16:48:30 +0300 Subject: [PATCH] feat(api-nodes): add automatic downscaling of videos for ByteDance 2 nodes Signed-off-by: bigcat88 --- comfy_api_nodes/apis/bytedance.py | 13 +++- comfy_api_nodes/nodes_bytedance.py | 33 +++++++-- comfy_api_nodes/util/__init__.py | 2 + comfy_api_nodes/util/conversions.py | 104 +++++++++++++++++++++++++--- 4 files changed, 134 insertions(+), 18 deletions(-) diff --git a/comfy_api_nodes/apis/bytedance.py b/comfy_api_nodes/apis/bytedance.py index 3755323ac..dc3bc3213 100644 --- a/comfy_api_nodes/apis/bytedance.py +++ b/comfy_api_nodes/apis/bytedance.py @@ -158,10 +158,17 @@ RECOMMENDED_PRESETS_SEEDREAM_4 = [ ("Custom", None, None), ] -# Seedance 2.0 reference video pixel count limits per model. +# Seedance 2.0 reference video pixel count limits per model and output resolution. SEEDANCE2_REF_VIDEO_PIXEL_LIMITS = { - "dreamina-seedance-2-0-260128": {"min": 409_600, "max": 927_408}, - "dreamina-seedance-2-0-fast-260128": {"min": 409_600, "max": 927_408}, + "dreamina-seedance-2-0-260128": { + "480p": {"min": 409_600, "max": 927_408}, + "720p": {"min": 409_600, "max": 927_408}, + "1080p": {"min": 409_600, "max": 2_073_600}, + }, + "dreamina-seedance-2-0-fast-260128": { + "480p": {"min": 409_600, "max": 927_408}, + "720p": {"min": 409_600, "max": 927_408}, + }, } # The time in this dictionary are given for 10 seconds duration. diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py index 429c32444..bc564782d 100644 --- a/comfy_api_nodes/nodes_bytedance.py +++ b/comfy_api_nodes/nodes_bytedance.py @@ -35,6 +35,7 @@ from comfy_api_nodes.util import ( get_number_of_images, image_tensor_pair_to_batch, poll_op, + resize_video_to_pixel_budget, sync_op, upload_audio_to_comfyapi, upload_image_to_comfyapi, @@ -69,9 +70,12 @@ DEPRECATED_MODELS = {"seedance-1-0-lite-t2v-250428", "seedance-1-0-lite-i2v-2504 logger = logging.getLogger(__name__) -def _validate_ref_video_pixels(video: Input.Video, model_id: str, index: int) -> None: - """Validate reference video pixel count against Seedance 2.0 model limits.""" - limits = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id) +def _validate_ref_video_pixels(video: Input.Video, model_id: str, resolution: str, index: int) -> None: + """Validate reference video pixel count against Seedance 2.0 model limits for the selected resolution.""" + model_limits = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id) + if not model_limits: + return + limits = model_limits.get(resolution) if not limits: return try: @@ -1373,6 +1377,14 @@ def _seedance2_reference_inputs(resolutions: list[str]): min=0, ), ), + IO.Boolean.Input( + "auto_downscale", + default=False, + advanced=True, + optional=True, + tooltip="Automatically downscale reference videos that exceed the model's pixel budget " + "for the selected resolution. Aspect ratio is preserved; videos already within limits are untouched.", + ), ] @@ -1480,10 +1492,23 @@ class ByteDance2ReferenceNode(IO.ComfyNode): model_id = SEEDANCE_MODELS[model["model"]] has_video_input = len(reference_videos) > 0 + + if model.get("auto_downscale") and reference_videos: + max_px = ( + SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {}) + .get(model["resolution"], {}) + .get("max") + ) + if max_px: + for key in reference_videos: + reference_videos[key] = resize_video_to_pixel_budget( + reference_videos[key], max_px + ) + total_video_duration = 0.0 for i, key in enumerate(reference_videos, 1): video = reference_videos[key] - _validate_ref_video_pixels(video, model_id, i) + _validate_ref_video_pixels(video, model_id, model["resolution"], i) try: dur = video.get_duration() if dur < 1.8: diff --git a/comfy_api_nodes/util/__init__.py b/comfy_api_nodes/util/__init__.py index 0cb9a47c7..f3584aba9 100644 --- a/comfy_api_nodes/util/__init__.py +++ b/comfy_api_nodes/util/__init__.py @@ -19,6 +19,7 @@ from .conversions import ( image_tensor_pair_to_batch, pil_to_bytesio, resize_mask_to_image, + resize_video_to_pixel_budget, tensor_to_base64_string, tensor_to_bytesio, tensor_to_pil, @@ -90,6 +91,7 @@ __all__ = [ "image_tensor_pair_to_batch", "pil_to_bytesio", "resize_mask_to_image", + "resize_video_to_pixel_budget", "tensor_to_base64_string", "tensor_to_bytesio", "tensor_to_pil", diff --git a/comfy_api_nodes/util/conversions.py b/comfy_api_nodes/util/conversions.py index 82b6d22a5..be5d5719b 100644 --- a/comfy_api_nodes/util/conversions.py +++ b/comfy_api_nodes/util/conversions.py @@ -129,22 +129,38 @@ def pil_to_bytesio(img: Image.Image, mime_type: str = "image/png") -> BytesIO: return img_byte_arr +def _compute_downscale_dims(src_w: int, src_h: int, total_pixels: int) -> tuple[int, int] | None: + """Return downscaled (w, h) with even dims fitting ``total_pixels``, or None if already fits. + + Source aspect ratio is preserved; output may drift by a fraction of a percent because both dimensions + are rounded down to even values (many codecs require divisible-by-2). + """ + pixels = src_w * src_h + if pixels <= total_pixels: + return None + scale = math.sqrt(total_pixels / pixels) + new_w = max(2, int(src_w * scale)) + new_h = max(2, int(src_h * scale)) + new_w -= new_w % 2 + new_h -= new_h % 2 + return new_w, new_h + + def downscale_image_tensor(image: torch.Tensor, total_pixels: int = 1536 * 1024) -> torch.Tensor: - """Downscale input image tensor to roughly the specified total pixels.""" + """Downscale input image tensor to roughly the specified total pixels. + + Output dimensions are rounded down to even values so that the result is guaranteed to fit within ``total_pixels`` + and is compatible with codecs that require even dimensions (e.g. yuv420p). + """ samples = image.movedim(-1, 1) - total = int(total_pixels) - scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) - if scale_by >= 1: + dims = _compute_downscale_dims(samples.shape[3], samples.shape[2], int(total_pixels)) + if dims is None: return image - width = round(samples.shape[3] * scale_by) - height = round(samples.shape[2] * scale_by) - - s = common_upscale(samples, width, height, "lanczos", "disabled") - s = s.movedim(1, -1) - return s + new_w, new_h = dims + return common_upscale(samples, new_w, new_h, "lanczos", "disabled").movedim(1, -1) -def downscale_image_tensor_by_max_side(image: torch.Tensor, *, max_side: int) -> torch.Tensor: +def downscale_image_tensor_by_max_side(image: torch.Tensor, *, max_side: int) -> torch.Tensor: """Downscale input image tensor so the largest dimension is at most max_side pixels.""" samples = image.movedim(-1, 1) height, width = samples.shape[2], samples.shape[3] @@ -399,6 +415,72 @@ def trim_video(video: Input.Video, duration_sec: float) -> Input.Video: raise RuntimeError(f"Failed to trim video: {str(e)}") from e +def resize_video_to_pixel_budget(video: Input.Video, total_pixels: int) -> Input.Video: + """Downscale a video to fit within ``total_pixels`` (w * h), preserving aspect ratio. + + Returns the original video object untouched when it already fits. Preserves frame rate, duration, and audio. + Aspect ratio is preserved up to a fraction of a percent (even-dim rounding). + """ + src_w, src_h = video.get_dimensions() + scale_dims = _compute_downscale_dims(src_w, src_h, total_pixels) + if scale_dims is None: + return video + return _apply_video_scale(video, scale_dims) + + +def _apply_video_scale(video: Input.Video, scale_dims: tuple[int, int]) -> Input.Video: + """Re-encode ``video`` scaled to ``scale_dims`` with a single decode/encode pass.""" + out_w, out_h = scale_dims + output_buffer = BytesIO() + input_container = None + output_container = None + + try: + input_source = video.get_stream_source() + input_container = av.open(input_source, mode="r") + output_container = av.open(output_buffer, mode="w", format="mp4") + + video_stream = output_container.add_stream("h264", rate=video.get_frame_rate()) + video_stream.width = out_w + video_stream.height = out_h + video_stream.pix_fmt = "yuv420p" + + audio_stream = None + for stream in input_container.streams: + if isinstance(stream, av.AudioStream): + audio_stream = output_container.add_stream("aac", rate=stream.sample_rate) + audio_stream.sample_rate = stream.sample_rate + audio_stream.layout = stream.layout + break + + for frame in input_container.decode(video=0): + frame = frame.reformat(width=out_w, height=out_h, format="yuv420p") + for packet in video_stream.encode(frame): + output_container.mux(packet) + for packet in video_stream.encode(): + output_container.mux(packet) + + if audio_stream is not None: + input_container.seek(0) + for audio_frame in input_container.decode(audio=0): + for packet in audio_stream.encode(audio_frame): + output_container.mux(packet) + for packet in audio_stream.encode(): + output_container.mux(packet) + + output_container.close() + input_container.close() + output_buffer.seek(0) + return InputImpl.VideoFromFile(output_buffer) + + except Exception as e: + if input_container is not None: + input_container.close() + if output_container is not None: + output_container.close() + raise RuntimeError(f"Failed to resize video: {str(e)}") from e + + def _f32_pcm(wav: torch.Tensor) -> torch.Tensor: """Convert audio to float 32 bits PCM format. Copy-paste from nodes_audio.py file.""" if wav.dtype.is_floating_point: