mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-05-18 13:07:28 +08:00
Merge branch 'advanced_save' into advanced_save
This commit is contained in:
Yousef R. Gamaleldin
GitHub
commit
595683653a
@ -1,10 +1,8 @@
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import torch
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from comfy_api.latest import IO
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from typing_extensions import override
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from comfy_api.latest import ComfyExtension
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# Rec.709 to Rec.2020 Gamut Conversion Matrix
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M_709_to_2020 = torch.tensor([[0.6274, 0.3293, 0.0433],[0.0691, 0.9195, 0.0114],[0.0164, 0.0880, 0.8956]
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])
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@ -69,7 +67,7 @@ class ConvertColorSpace(IO.ComfyNode):
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# assume Grayscale has sRGB gamma
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luma = 0.2126 * rgb[..., 0] + 0.7152 * rgb[..., 1] + 0.0722 * rgb[..., 2]
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rgb = luma.unsqueeze(-1).repeat(1, 1, 1, 3)
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rgb = linear_to_srgb(rgb)
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rgb = srgb_to_linear(rgb)
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elif source_color_space == "HDR (Rec.2020)":
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# assuming Linear Rec.2020 input. Convert to Linear Rec.709
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@ -6,20 +6,28 @@ import folder_paths
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import av
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import io
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import json
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import logging
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import av
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import os
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import re
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import math
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import numpy as np
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import struct
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import torch
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import struct
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import zlib
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import tempfile
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import logging
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import comfy.utils
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import numpy as np
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from fractions import Fraction
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from server import PromptServer
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from comfy_api.latest import ComfyExtension, Input, IO, UI
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from comfy.cli_args import args
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from typing_extensions import override
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from comfy.cli_args import args
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SVG = IO.SVG.Type # TODO: temporary solution for backward compatibility, will be removed later.
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@ -830,14 +838,15 @@ class ImageMergeTileList(IO.ComfyNode):
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return IO.NodeOutput(merged_image)
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def _create_png_chunk(chunk_type: bytes, data: bytes) -> bytes:
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def create_png_chunk(chunk_type: bytes, data: bytes) -> bytes:
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"""Creates a valid PNG chunk with Length, Type, Data, and CRC32."""
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chunk = struct.pack('>I', len(data)) + chunk_type + data
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crc = zlib.crc32(chunk_type + data) & 0xffffffff
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return chunk + struct.pack('>I', crc)
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def _inject_metadata_png(png_bytes, prompt=None, extra_pnginfo=None):
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def inject_comfy_metadata_png(png_bytes, prompt=None, extra_pnginfo=None):
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# IEND chunk is the last 12 bytes of png files
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content = png_bytes[:-12]
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iend = png_bytes[-12:]
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@ -846,16 +855,16 @@ def _inject_metadata_png(png_bytes, prompt=None, extra_pnginfo=None):
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if prompt is not None:
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payload = b'prompt\x00' + json.dumps(prompt).encode('utf-8')
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metadata_chunks += _create_png_chunk(b'tEXt', payload)
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metadata_chunks += create_png_chunk(b'tEXt', payload)
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if extra_pnginfo is not None:
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for k, v in extra_pnginfo.items():
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payload = k.encode('utf-8') + b'\x00' + json.dumps(v).encode('utf-8')
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metadata_chunks += _create_png_chunk(b'tEXt', payload)
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metadata_chunks += create_png_chunk(b'tEXt', payload)
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return content + metadata_chunks + iend
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def _inject_metadata_exr(exr_bytes: bytes, prompt, extra_pnginfo) -> bytes:
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def inject_comfy_metadata_exr(exr_bytes: bytes, prompt, extra_pnginfo) -> bytes:
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# skip magic and version
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idx = 8
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@ -913,7 +922,7 @@ def _inject_metadata_exr(exr_bytes: bytes, prompt, extra_pnginfo) -> bytes:
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# stitch the file back together with the new header and updated table
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return exr_bytes[:table_start - 1] + payload + b'\x00' + new_table + exr_bytes[table_start + num_entries*8:]
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def _inject_metadata_avif(avif_bytes: bytes, prompt, extra_pnginfo) -> bytes:
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def inject_comfy_metadata_avif(avif_bytes: bytes, prompt, extra_pnginfo) -> bytes:
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metadata = {}
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if prompt is not None:
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metadata["prompt"] = prompt
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@ -934,7 +943,6 @@ def _inject_metadata_avif(avif_bytes: bytes, prompt, extra_pnginfo) -> bytes:
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# isobmff allows top-level boxes at the end of the file.
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return avif_bytes + uuid_box
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class SaveImageAdvanced(IO.ComfyNode):
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@classmethod
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def define_schema(cls):
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@ -1039,84 +1047,133 @@ class SaveImageAdvanced(IO.ComfyNode):
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color_space = format["color_space"]
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img_tensor = image.clone()
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height, width, num_channels = img_tensor.shape
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has_alpha = (num_channels == 4)
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# file pathing
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filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
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file = f"{filename_with_batch_num}_{counter:05}.{extension}"
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file = f"{filename_with_batch_num}_{counter:05}.{file_format}"
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file_path = os.path.join(full_output_folder, file)
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if bit_depth == "32-bit":
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img_np = img_tensor.cpu().numpy()
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# rgba128le handles 4x32f, gbrpf32le handles 3x32f planar
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av_fmt = 'rgba128le' if has_alpha else 'gbrpf32le'
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elif bit_depth == "16-bit":
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img_np = (img_tensor * 65535.0).clamp(0, 65535).to(torch.int32).cpu().numpy().astype(np.uint16)
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if extension == "png":
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# png requires Big-Endian (be) for 16-bit
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av_fmt = 'rgba64be' if has_alpha else 'rgb48be'
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img_np = img_np.byteswap().view(img_np.dtype.newbyteorder('>'))
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if file_format in ["png", "exr", "avif"]:
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# safe bit downcasting
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if (file_format == "png" or file_format == "avif") and bit_depth == "32-bit":
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bit_depth = "16-bit"
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if file_format == "exr" and bit_depth == "8-bit":
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bit_depth = "16-bit"
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if bit_depth == "32-bit":
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img_np = img_tensor.cpu().numpy().astype(np.float32)
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av_fmt = 'gbrapf32le' if has_alpha else 'gbrpf32le'
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elif bit_depth == "16-bit":
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if file_format == "exr":
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# default pyav build doesn't come with a codec for float16 exr format
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img_np = img_tensor.cpu().numpy().astype(np.float32)
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av_fmt = 'gbrapf32le' if has_alpha else 'gbrpf32le'
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else:
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img_np = (img_tensor * 65535.0).clamp(0, 65535).to(torch.int32).cpu().numpy().astype(np.uint16)
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av_fmt = 'rgba64le' if has_alpha else 'rgb48le'
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else:
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av_fmt = 'rgba64le' if has_alpha else 'rgb48le'
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else:
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img_np = (img_tensor * 255.0).clamp(0, 255).to(torch.int32).cpu().numpy().astype(np.uint8)
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av_fmt = 'rgba' if has_alpha else 'rgb24'
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img_np = (img_tensor * 255.0).clamp(0, 255).to(torch.int32).cpu().numpy().astype(np.uint8)
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av_fmt = 'rgba' if has_alpha else 'rgb24'
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memory_buffer = io.BytesIO()
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container_format = "image2" if extension in ["png", "exr"] else "avif"
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container = av.open(memory_buffer, mode='w', format=container_format)
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fd, tmp_path = tempfile.mkstemp(suffix=f".{file_format}")
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os.close(fd)
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container_format = "image2" if file_format in ["png", "exr"] else "avif"
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container = av.open(tmp_path, mode='w', format=container_format)
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if extension == "exr":
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stream = container.add_stream('exr', rate=1)
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stream.pix_fmt = av_fmt
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elif extension == "avif":
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stream = container.add_stream('av1', rate=1)
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# YUV color spaces
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stream.pix_fmt = 'yuv444p12le' if bit_depth in ["16-bit", "32-bit"] else 'yuv444p'
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elif extension == "png":
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stream = container.add_stream('png', rate=1)
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stream.pix_fmt = av_fmt
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stream.width = width
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stream.height = height
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# planar: all red, all blue, all green instead of r, g, b, r, g, b
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is_planar = av_fmt.startswith('gbrp') or 'p' in av_fmt.split('rgba')[-1]
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if is_planar:
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img_np = img_np.transpose(2, 0, 1)
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try:
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frame = av.VideoFrame.from_ndarray(img_np, format=av_fmt)
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except ValueError:
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# FFMPEG Float32 Fallback: not all ffmpeg versions are able to handle float32 format for images
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# float16 fallback conversion
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logging.warning("[WARNING] Current FFMPEG Binary can't save float32 images. Fallbacking to float16")
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img_np = (img_tensor * 65535.0).clamp(0, 65535).to(torch.int32).cpu().numpy().astype(np.uint16)
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av_fmt = 'rgba64le' if has_alpha else 'rgb48le'
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frame = av.VideoFrame.from_ndarray(img_np, format=av_fmt)
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if extension == "exr" or extension == "png":
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if file_format == "exr":
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stream = container.add_stream('exr', rate=1)
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stream.pix_fmt = av_fmt
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for packet in stream.encode(frame):
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container.mux(packet)
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for packet in stream.encode():
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container.mux(packet)
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elif file_format == "avif":
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try:
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stream = container.add_stream('libsvtav1', rate=1)
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except Exception:
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stream = container.add_stream('av1', rate=1)
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container.close()
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stream.time_base = Fraction(1, 1)
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final_bytes = memory_buffer.getvalue()
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if bit_depth in ["16-bit", "32-bit"]:
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stream.pix_fmt = 'yuv420p10le'
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else:
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stream.pix_fmt = 'yuv420p'
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if embed_workflow and not args.disable_metadata:
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if extension == "png":
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final_bytes = _inject_metadata_png(final_bytes, prompt, extra_pnginfo)
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elif extension == "exr":
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final_bytes = _inject_metadata_exr(final_bytes, prompt, extra_pnginfo)
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else:
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final_bytes = _inject_metadata_avif(final_bytes, prompt, extra_pnginfo)
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stream.codec_context.color_range = 2
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stream.codec_context.colorspace = 1
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stream.codec_context.color_primaries = 1
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stream.codec_context.color_trc = 1
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with open(file_path, "wb") as f:
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f.write(final_bytes)
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stream.options = {
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'preset': '10',
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'svtav1-params': 'rc=0:qp=20:color-range=1:color-matrix=1:enable-overlays=1',
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'g': '1'
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}
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elif file_format == "png":
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stream = container.add_stream('png', rate=1)
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if bit_depth == "16-bit":
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stream.pix_fmt = 'rgba64be' if has_alpha else 'rgb48be'
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else:
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stream.pix_fmt = av_fmt
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stream.width = width
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stream.height = height
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stream.time_base = Fraction(1, 1)
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is_planar = av_fmt.startswith('gbrp') or 'p' in av_fmt.split('rgba')[-1]
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if is_planar:
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if av_fmt.startswith('gbrp'):
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img_np = img_np[:, :, [1, 2, 0, 3]] if has_alpha else img_np[:, :, [1, 2, 0]]
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img_np = img_np.transpose(2, 0, 1)
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try:
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frame = av.VideoFrame.from_ndarray(img_np, format=av_fmt)
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except ValueError:
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logging.warning("[WARNING] Current FFMPEG Binary can't save natively. Fallbacking.")
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img_np = (img_tensor * 65535.0).clamp(0, 65535).to(torch.int32).cpu().numpy().astype(np.uint16)
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av_fmt = 'rgba64le' if has_alpha else 'rgb48le'
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frame = av.VideoFrame.from_ndarray(img_np, format=av_fmt)
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# reformat for both avif and exr to ensure correct internal conversion
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if file_format in ["avif", "exr"] or (file_format == "png" and bit_depth == "16-bit"):
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reformat_kwargs = {"format": stream.pix_fmt}
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if file_format == "avif":
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reformat_kwargs.update({
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"src_colorspace": 1, "dst_colorspace": 1,
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"src_color_range": 2, "dst_color_range": 2
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})
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frame = frame.reformat(**reformat_kwargs)
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frame.pts = 0
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frame.time_base = stream.time_base
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if file_format == "avif":
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frame.color_range = 2
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frame.colorspace = 1
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for packet in stream.encode(frame):
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container.mux(packet)
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for packet in stream.encode():
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container.mux(packet)
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container.close()
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with open(tmp_path, "rb") as f:
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final_bytes = f.read()
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os.remove(tmp_path)
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if embed_workflow and not args.disable_metadata:
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if file_format == "png":
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final_bytes = inject_comfy_metadata_png(final_bytes, prompt, extra_pnginfo)
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elif file_format == "exr":
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final_bytes = inject_comfy_metadata_exr(final_bytes, prompt, extra_pnginfo)
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else:
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final_bytes = inject_comfy_metadata_avif(final_bytes, prompt, extra_pnginfo)
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with open(file_path, "wb") as f:
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f.write(final_bytes)
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results.append({
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"filename": file,
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@ -1127,7 +1184,6 @@ class SaveImageAdvanced(IO.ComfyNode):
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return IO.NodeOutput(ui={"images": results})
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class ImagesExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[IO.ComfyNode]]:
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@ -1150,6 +1206,7 @@ class ImagesExtension(ComfyExtension):
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ImageScaleToMaxDimension,
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SplitImageToTileList,
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ImageMergeTileList,
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SaveImageAdvanced
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]
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@ -1,349 +0,0 @@
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import io
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import av
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import os
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import json
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import torch
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import struct
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import zlib
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import logging
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import numpy as np
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import folder_paths
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from comfy_api.latest import IO
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from typing_extensions import override
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from comfy_api.latest import ComfyExtension
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from comfy.cli_args import args
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def create_png_chunk(chunk_type: bytes, data: bytes) -> bytes:
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"""Creates a valid PNG chunk with Length, Type, Data, and CRC32."""
|
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chunk = struct.pack('>I', len(data)) + chunk_type + data
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crc = zlib.crc32(chunk_type + data) & 0xffffffff
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return chunk + struct.pack('>I', crc)
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|
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def inject_comfy_metadata_png(png_bytes, prompt=None, extra_pnginfo=None):
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# IEND chunk is the last 12 bytes of png files
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content = png_bytes[:-12]
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iend = png_bytes[-12:]
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metadata_chunks = b""
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|
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if prompt is not None:
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payload = b'prompt\x00' + json.dumps(prompt).encode('utf-8')
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metadata_chunks += create_png_chunk(b'tEXt', payload)
|
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|
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if extra_pnginfo is not None:
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for k, v in extra_pnginfo.items():
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payload = k.encode('utf-8') + b'\x00' + json.dumps(v).encode('utf-8')
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metadata_chunks += create_png_chunk(b'tEXt', payload)
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return content + metadata_chunks + iend
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|
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def inject_comfy_metadata_exr(exr_bytes: bytes, prompt, extra_pnginfo) -> bytes:
|
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# skip magic and version
|
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idx = 8
|
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|
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# parse through existing attributes to find the end of the header
|
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while True:
|
||||
name_start = idx
|
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while exr_bytes[idx] != 0:
|
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idx += 1
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||||
name = exr_bytes[name_start:idx]
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idx += 1
|
||||
|
||||
# empty name means we hit the header terminator
|
||||
if len(name) == 0:
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break
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||||
|
||||
# skip attribute type string
|
||||
while exr_bytes[idx] != 0:
|
||||
idx += 1
|
||||
idx += 1
|
||||
|
||||
# read attribute size and skip the value
|
||||
attr_size = struct.unpack('<I', exr_bytes[idx:idx+4])[0]
|
||||
idx += 4 + attr_size
|
||||
|
||||
# offset table starts right after the header terminator
|
||||
table_start = idx
|
||||
|
||||
# build comfyui metadata payload
|
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payload = b""
|
||||
if prompt is not None:
|
||||
prompt_str = json.dumps(prompt).encode('utf-8')
|
||||
payload += b"prompt\x00string\x00" + struct.pack('<I', len(prompt_str)) + prompt_str
|
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if extra_pnginfo is not None:
|
||||
for k, v in extra_pnginfo.items():
|
||||
k_enc = k.encode('utf-8')[:254]
|
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v_enc = json.dumps(v).encode('utf-8')
|
||||
payload += k_enc + b"\x00string\x00" + struct.pack('<I', len(v_enc)) + v_enc
|
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|
||||
# find the first pixel offset to calculate the table size
|
||||
min_offset = struct.unpack('<Q', exr_bytes[table_start:table_start+8])[0]
|
||||
num_entries = 1
|
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while table_start + num_entries * 8 < min_offset:
|
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offset = struct.unpack('<Q', exr_bytes[table_start + num_entries*8 : table_start + num_entries*8 + 8])[0]
|
||||
if offset < min_offset:
|
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min_offset = offset
|
||||
num_entries += 1
|
||||
|
||||
# shift table pointers by the payload size
|
||||
shift_amount = len(payload)
|
||||
new_table = bytearray()
|
||||
for i in range(num_entries):
|
||||
offset = struct.unpack('<Q', exr_bytes[table_start + i*8 : table_start + i*8 + 8])[0]
|
||||
new_table.extend(struct.pack('<Q', offset + shift_amount))
|
||||
|
||||
# stitch the file back together with the new header and updated table
|
||||
return exr_bytes[:table_start - 1] + payload + b'\x00' + new_table + exr_bytes[table_start + num_entries*8:]
|
||||
|
||||
def inject_comfy_metadata_avif(avif_bytes: bytes, prompt, extra_pnginfo) -> bytes:
|
||||
metadata = {}
|
||||
if prompt is not None:
|
||||
metadata["prompt"] = prompt
|
||||
if extra_pnginfo is not None:
|
||||
for k, v in extra_pnginfo.items():
|
||||
metadata[k] = v
|
||||
|
||||
payload = json.dumps(metadata).encode('utf-8')
|
||||
|
||||
# 16-byte uuid required by isobmff spec
|
||||
# 'comfyui_workflow' is exactly 16 bytes long!
|
||||
comfy_uuid = b'comfyui_workflow'
|
||||
|
||||
# box size: 4 (size) + 4 (type) + 16 (uuid) + payload length
|
||||
box_size = 4 + 4 + 16 + len(payload)
|
||||
uuid_box = struct.pack('>I', box_size) + b'uuid' + comfy_uuid + payload
|
||||
|
||||
# isobmff allows top-level boxes at the end of the file.
|
||||
return avif_bytes + uuid_box
|
||||
|
||||
class SaveImageAdvanced(IO.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="SaveImageAdvanced",
|
||||
category="image/advanced_io",
|
||||
output_node=True,
|
||||
inputs=[
|
||||
IO.Image.Input("images"),
|
||||
IO.String.Input("filename_prefix", default="ComfyUI"),
|
||||
IO.Combo.Input("file_format", options=["png", "exr", "avif"], default="png"),
|
||||
IO.Combo.Input("bit_depth", options=["8-bit", "16-bit", "32-bit"], default="8-bit"),
|
||||
IO.Boolean.Input("embed_workflow", default=True),
|
||||
IO.Hidden.Input("prompt", type="PROMPT"),
|
||||
IO.Hidden.Input("extra_pnginfo", type="EXTRA_PNGINFO"),
|
||||
],
|
||||
outputs=[]
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, images, filename_prefix="ComfyUI", file_format="png", bit_depth="8-bit",
|
||||
embed_workflow=True, prompt=None, extra_pnginfo=None) -> IO.NodeOutput:
|
||||
|
||||
output_dir = folder_paths.get_output_directory()
|
||||
|
||||
full_output_folder, filename, counter, subfolder, filename_prefix = \
|
||||
folder_paths.get_save_image_path(filename_prefix, output_dir, images[0].shape[1], images[0].shape[0])
|
||||
|
||||
results = list()
|
||||
|
||||
for batch_number, image in enumerate(images):
|
||||
img_tensor = image.clone()
|
||||
|
||||
height, width, num_channels = img_tensor.shape
|
||||
has_alpha = (num_channels == 4)
|
||||
|
||||
# file pathing
|
||||
filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
|
||||
file = f"{filename_with_batch_num}_{counter:05}.{file_format}"
|
||||
file_path = os.path.join(full_output_folder, file)
|
||||
|
||||
if file_format in ["png", "exr", "avif"]:
|
||||
|
||||
# safe bit downcasting
|
||||
if (file_format == "png" or file_format == "avif") and bit_depth == "32-bit":
|
||||
bit_depth = "16-bit"
|
||||
if file_format == "exr" and bit_depth == "8-bit":
|
||||
bit_depth = "16-bit"
|
||||
|
||||
if bit_depth == "32-bit":
|
||||
img_np = img_tensor.cpu().numpy()
|
||||
# rgba128le handles 4x32f, gbrpf32le handles 3x32f planar
|
||||
av_fmt = 'rgba128le' if has_alpha else 'gbrpf32le'
|
||||
elif bit_depth == "16-bit":
|
||||
img_np = (img_tensor * 65535.0).clamp(0, 65535).to(torch.int32).cpu().numpy().astype(np.uint16)
|
||||
if file_format == "png":
|
||||
# png requires Big-Endian (be) for 16-bit
|
||||
av_fmt = 'rgba64be' if has_alpha else 'rgb48be'
|
||||
img_np = img_np.byteswap().view(img_np.dtype.newbyteorder('>'))
|
||||
else:
|
||||
av_fmt = 'rgba64le' if has_alpha else 'rgb48le'
|
||||
else:
|
||||
img_np = (img_tensor * 255.0).clamp(0, 255).to(torch.int32).cpu().numpy().astype(np.uint8)
|
||||
av_fmt = 'rgba' if has_alpha else 'rgb24'
|
||||
|
||||
memory_buffer = io.BytesIO()
|
||||
container_format = "image2" if file_format in ["png", "exr"] else "avif"
|
||||
container = av.open(memory_buffer, mode='w', format=container_format)
|
||||
|
||||
if file_format == "exr":
|
||||
stream = container.add_stream('exr', rate=1)
|
||||
stream.pix_fmt = av_fmt
|
||||
elif file_format == "avif":
|
||||
stream = container.add_stream('av1', rate=1)
|
||||
# YUV color spaces
|
||||
stream.pix_fmt = 'yuv444p12le' if bit_depth in["16-bit", "32-bit"] else 'yuv444p'
|
||||
elif file_format == "png":
|
||||
stream = container.add_stream('png', rate=1)
|
||||
stream.pix_fmt = av_fmt
|
||||
|
||||
stream.width = width
|
||||
stream.height = height
|
||||
|
||||
# planar: all red, all blue, all green instead of r, g, b, r, g, b
|
||||
is_planar = av_fmt.startswith('gbrp') or 'p' in av_fmt.split('rgba')[-1]
|
||||
if is_planar:
|
||||
img_np = img_np.transpose(2, 0, 1)
|
||||
|
||||
try:
|
||||
frame = av.VideoFrame.from_ndarray(img_np, format=av_fmt)
|
||||
except ValueError:
|
||||
# FFMPEG Float32 Fallback: not all ffmpeg versions are able to handle float32 format for images
|
||||
# float16 fallback conversion
|
||||
logging.warning("[WARNING] Current FFMPEG Binary can't save float32 images. Fallbacking to float16")
|
||||
img_np = (img_tensor * 65535.0).clamp(0, 65535).to(torch.int32).cpu().numpy().astype(np.uint16)
|
||||
av_fmt = 'rgba64le' if has_alpha else 'rgb48le'
|
||||
frame = av.VideoFrame.from_ndarray(img_np, format=av_fmt)
|
||||
if file_format == "exr" or file_format == "png":
|
||||
stream.pix_fmt = av_fmt
|
||||
|
||||
for packet in stream.encode(frame):
|
||||
container.mux(packet)
|
||||
for packet in stream.encode():
|
||||
container.mux(packet)
|
||||
|
||||
container.close()
|
||||
|
||||
final_bytes = memory_buffer.getvalue()
|
||||
|
||||
if embed_workflow and not args.disable_metadata:
|
||||
if file_format == "png":
|
||||
final_bytes = inject_comfy_metadata_png(final_bytes, prompt, extra_pnginfo)
|
||||
elif file_format == "exr":
|
||||
final_bytes = inject_comfy_metadata_exr(final_bytes, prompt, extra_pnginfo)
|
||||
else:
|
||||
final_bytes = inject_comfy_metadata_avif(final_bytes, prompt, extra_pnginfo)
|
||||
|
||||
with open(file_path, "wb") as f:
|
||||
f.write(final_bytes)
|
||||
|
||||
results.append({
|
||||
"filename": file,
|
||||
"subfolder": subfolder,
|
||||
"type": "output"
|
||||
})
|
||||
counter += 1
|
||||
|
||||
return IO.NodeOutput(ui={"images": results})
|
||||
|
||||
# Rec.709 to Rec.2020 Gamut Conversion Matrix
|
||||
M_709_to_2020 = torch.tensor([[0.6274, 0.3293, 0.0433],[0.0691, 0.9195, 0.0114],[0.0164, 0.0880, 0.8956]
|
||||
])
|
||||
|
||||
# Rec.2020 to Rec.709 Gamut Conversion Matrix
|
||||
M_2020_to_709 = torch.tensor([[ 1.6605, -0.5876, -0.0728],[-0.1246, 1.1329, -0.0083],[-0.0182, -0.1006, 1.1187]
|
||||
])
|
||||
|
||||
def srgb_to_linear(tensor):
|
||||
mask = tensor <= 0.04045
|
||||
return torch.where(mask, tensor / 12.92, torch.pow((tensor + 0.055) / 1.055, 2.4))
|
||||
|
||||
def linear_to_srgb(tensor):
|
||||
mask = tensor <= 0.0031308
|
||||
return torch.where(mask, tensor * 12.92, 1.055 * torch.pow(tensor.clamp(min=1e-8), 1.0 / 2.4) - 0.055)
|
||||
|
||||
def linear_to_pq(linear_tensor):
|
||||
"""SMPTE ST 2084 (PQ) encoding"""
|
||||
m1, m2 = (2610 / 4096 / 4), (2523 / 4096 * 128)
|
||||
c1, c2, c3 = (3424 / 4096), (2413 / 4096 * 32), (2392 / 4096 * 32)
|
||||
l_norm = torch.clamp(linear_tensor, 0.0, 1.0)
|
||||
l_m1 = torch.pow(l_norm, m1)
|
||||
return torch.pow((c1 + c2 * l_m1) / (1 + c3 * l_m1), m2)
|
||||
|
||||
def pq_to_linear(pq_tensor):
|
||||
"""Inverse SMPTE ST 2084 (PQ) decoding"""
|
||||
m1, m2 = (2610 / 4096 / 4), (2523 / 4096 * 128)
|
||||
c1, c2, c3 = (3424 / 4096), (2413 / 4096 * 32), (2392 / 4096 * 32)
|
||||
n = torch.pow(torch.clamp(pq_tensor, 0.0, 1.0), 1/m2)
|
||||
return torch.pow(torch.clamp((n - c1) / (c2 - c3 * n), min=0.0), 1/m1)
|
||||
|
||||
class ConvertColorSpace(IO.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="Convert Color Space",
|
||||
category="image/color",
|
||||
inputs=[
|
||||
IO.Image.Input("images"),
|
||||
IO.Combo.Input("source_color_space", options=["sRGB", "Linear", "HDR (Rec.2020)", "Grayscale"], default="sRGB"),
|
||||
IO.Combo.Input("target_color_space", options=["sRGB", "Linear", "HDR (Rec.2020)", "Grayscale"], default="Linear"),
|
||||
],
|
||||
outputs=[
|
||||
IO.Image.Output("images"),
|
||||
]
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, images, source_color_space, target_color_space) -> IO.NodeOutput:
|
||||
img_tensor = images.clone()
|
||||
device = img_tensor.device
|
||||
|
||||
has_alpha = img_tensor.shape[-1] == 4
|
||||
alpha = img_tensor[..., 3:4] if has_alpha else None
|
||||
rgb = img_tensor[..., :3]
|
||||
|
||||
# turn source into linear
|
||||
if source_color_space == "sRGB":
|
||||
rgb = srgb_to_linear(rgb)
|
||||
|
||||
elif source_color_space == "Grayscale":
|
||||
# assume Grayscale has sRGB gamma
|
||||
luma = 0.2126 * rgb[..., 0] + 0.7152 * rgb[..., 1] + 0.0722 * rgb[..., 2]
|
||||
rgb = luma.unsqueeze(-1).repeat(1, 1, 1, 3)
|
||||
rgb = linear_to_srgb(rgb)
|
||||
|
||||
elif source_color_space == "HDR (Rec.2020)":
|
||||
# assuming Linear Rec.2020 input. Convert to Linear Rec.709
|
||||
matrix = M_2020_to_709.to(device)
|
||||
rgb = pq_to_linear(rgb)
|
||||
rgb = torch.matmul(rgb, matrix.T)
|
||||
|
||||
|
||||
# turn source into target space
|
||||
if target_color_space == "sRGB":
|
||||
rgb = linear_to_srgb(rgb)
|
||||
|
||||
elif target_color_space == "Grayscale":
|
||||
luma = 0.2126 * rgb[..., 0] + 0.7152 * rgb[..., 1] + 0.0722 * rgb[..., 2]
|
||||
rgb = luma.unsqueeze(-1).repeat(1, 1, 1, 3)
|
||||
rgb = linear_to_srgb(rgb) # reapply srgb gamma
|
||||
|
||||
elif target_color_space == "HDR (Rec.2020)":
|
||||
# convert Gamut from Linear Rec.709 to Linear Rec.2020
|
||||
rgb = torch.matmul(rgb, M_709_to_2020.to(device).T).clamp(min=0)
|
||||
rgb = linear_to_pq(rgb)
|
||||
|
||||
img_tensor = torch.cat([rgb, alpha], dim=-1) if has_alpha else rgb
|
||||
|
||||
return IO.NodeOutput(images=img_tensor)
|
||||
|
||||
class AdvancedImageSave(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
|
||||
return [
|
||||
SaveImageAdvanced,
|
||||
ConvertColorSpace
|
||||
]
|
||||
|
||||
|
||||
async def comfy_entrypoint() -> AdvancedImageSave:
|
||||
return AdvancedImageSave()
|
||||
Loading…
Reference in New Issue
Block a user