mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-05-19 05:27:24 +08:00
Merge fb8726244c into 3e3ed8cc2a
This commit is contained in:
Yousef R. Gamaleldin
GitHub
commit
3afc420ce5
108
comfy_extras/nodes_convert_color_space.py
Normal file
108
comfy_extras/nodes_convert_color_space.py
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@ -0,0 +1,108 @@
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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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# Rec.2020 to Rec.709 Gamut Conversion Matrix
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M_2020_to_709 = torch.tensor([[ 1.6605, -0.5876, -0.0728],[-0.1246, 1.1329, -0.0083],[-0.0182, -0.1006, 1.1187]
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])
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def srgb_to_linear(tensor):
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mask = tensor <= 0.04045
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return torch.where(mask, tensor / 12.92, torch.pow((tensor + 0.055) / 1.055, 2.4))
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def linear_to_srgb(tensor):
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mask = tensor <= 0.0031308
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return torch.where(mask, tensor * 12.92, 1.055 * torch.pow(tensor.clamp(min=1e-8), 1.0 / 2.4) - 0.055)
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def linear_to_pq(linear_tensor):
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"""SMPTE ST 2084 (PQ) encoding"""
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m1, m2 = (2610 / 4096 / 4), (2523 / 4096 * 128)
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c1, c2, c3 = (3424 / 4096), (2413 / 4096 * 32), (2392 / 4096 * 32)
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l_norm = torch.clamp(linear_tensor, 0.0, 1.0)
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l_m1 = torch.pow(l_norm, m1)
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return torch.pow((c1 + c2 * l_m1) / (1 + c3 * l_m1), m2)
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def pq_to_linear(pq_tensor):
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"""Inverse SMPTE ST 2084 (PQ) decoding"""
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m1, m2 = (2610 / 4096 / 4), (2523 / 4096 * 128)
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c1, c2, c3 = (3424 / 4096), (2413 / 4096 * 32), (2392 / 4096 * 32)
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n = torch.pow(torch.clamp(pq_tensor, 0.0, 1.0), 1/m2)
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return torch.pow(torch.clamp((n - c1) / (c2 - c3 * n), min=0.0), 1/m1)
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class ConvertColorSpace(IO.ComfyNode):
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@classmethod
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def define_schema(cls):
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return IO.Schema(
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node_id="ConvertColorSpace",
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display_name="Convert Color Space",
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category="image/color",
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inputs=[
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IO.Image.Input("images"),
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IO.Combo.Input("source_color_space", options=["sRGB", "Linear", "HDR Display (PQ/Rec.2020)", "Grayscale"], default="sRGB"),
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IO.Combo.Input("target_color_space", options=["sRGB", "Linear", "HDR Display (PQ/Rec.2020)", "Grayscale"], default="Linear"),
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],
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outputs=[
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IO.Image.Output("images"),
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]
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)
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@classmethod
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def execute(cls, images, source_color_space, target_color_space) -> IO.NodeOutput:
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img_tensor = images.clone()
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device = img_tensor.device
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has_alpha = img_tensor.shape[-1] == 4
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alpha = img_tensor[..., 3:4] if has_alpha else None
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rgb = img_tensor[..., :3]
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# turn source into linear
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if source_color_space == "sRGB":
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rgb = srgb_to_linear(rgb)
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elif source_color_space == "Grayscale":
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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 = srgb_to_linear(rgb)
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elif source_color_space == "HDR Display (PQ/Rec.2020)":
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# assuming Linear Rec.2020 input. Convert to Linear Rec.709
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matrix = M_2020_to_709.to(device=device, dtype=rgb.dtype)
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rgb = pq_to_linear(rgb)
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rgb = torch.matmul(rgb, matrix.T)
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# turn source into target space
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if target_color_space == "sRGB":
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rgb = linear_to_srgb(rgb)
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elif target_color_space == "Grayscale":
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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) # reapply srgb gamma
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elif target_color_space == "HDR Display (PQ/Rec.2020)":
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# convert Gamut from Linear Rec.709 to Linear Rec.2020
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rgb = torch.matmul(rgb, M_709_to_2020.to(device=device, dtype=rgb.dtype).T).clamp(min=0)
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rgb = linear_to_pq(rgb)
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img_tensor = torch.cat([rgb, alpha], dim=-1) if has_alpha else rgb
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return IO.NodeOutput(img_tensor)
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class ConvertColorSpaceExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[IO.ComfyNode]]:
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return [
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ConvertColorSpace
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]
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async def comfy_entrypoint() -> ConvertColorSpaceExtension:
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return ConvertColorSpaceExtension()
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@ -3,15 +3,25 @@ from __future__ import annotations
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import nodes
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import folder_paths
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import av
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import json
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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 zlib
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import tempfile
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import logging
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import comfy.utils
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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, 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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SVG = IO.SVG.Type # TODO: temporary solution for backward compatibility, will be removed later.
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@ -823,6 +833,344 @@ 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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"""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_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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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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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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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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# parse through existing attributes to find the end of the header
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while True:
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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
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# empty name means we hit the header terminator
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if len(name) == 0:
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break
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# skip attribute type string
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while exr_bytes[idx] != 0:
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idx += 1
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idx += 1
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# read attribute size and skip the value
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attr_size = struct.unpack('<I', exr_bytes[idx:idx+4])[0]
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idx += 4 + attr_size
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# offset table starts right after the header terminator
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table_start = idx
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# build comfyui metadata payload
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payload = b""
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if prompt is not None:
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prompt_str = json.dumps(prompt).encode('utf-8')
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payload += b"prompt\x00string\x00" + struct.pack('<I', len(prompt_str)) + prompt_str
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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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k_enc = k.encode('utf-8')[:254]
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v_enc = json.dumps(v).encode('utf-8')
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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
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min_offset = struct.unpack('<Q', exr_bytes[table_start:table_start+8])[0]
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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]
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if offset < min_offset:
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min_offset = offset
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num_entries += 1
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# shift table pointers by the payload size
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shift_amount = len(payload)
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new_table = bytearray()
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for i in range(num_entries):
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offset = struct.unpack('<Q', exr_bytes[table_start + i*8 : table_start + i*8 + 8])[0]
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new_table.extend(struct.pack('<Q', offset + shift_amount))
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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_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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if extra_pnginfo is not None:
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for k, v in extra_pnginfo.items():
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metadata[k] = v
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payload = json.dumps(metadata).encode('utf-8')
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# 16-byte uuid required by isobmff spec
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# 'comfyui_workflow' is exactly 16 bytes long!
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comfy_uuid = b'comfyui_workflow'
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# box size: 4 (size) + 4 (type) + 16 (uuid) + payload length
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box_size = 4 + 4 + 16 + len(payload)
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uuid_box = struct.pack('>I', box_size) + b'uuid' + comfy_uuid + payload
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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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return IO.Schema(
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node_id="SaveImageAdvanced",
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search_aliases=["save", "save image", "export image", "output image", "write image"],
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display_name="Save Image",
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description="Saves the input images to your ComfyUI output directory.",
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category="image",
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essentials_category="Basics",
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inputs=[
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IO.Image.Input(
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"images",
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tooltip="The images to save."
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),
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IO.String.Input(
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"filename_prefix",
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default="ComfyUI",
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tooltip="The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.",
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),
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IO.DynamicCombo.Input(
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"format",
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options=[
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IO.DynamicCombo.Option(
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"png",
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[
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IO.Combo.Input(
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"bit_depth",
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options=["8-bit", "16-bit"],
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default="8-bit",
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advanced=True,
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),
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IO.Combo.Input(
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"interpret_as",
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options=["sRGB", "Raw/Data"],
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default="sRGB",
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advanced=True,
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),
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],
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),
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IO.DynamicCombo.Option(
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"avif",
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[
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IO.Combo.Input(
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"bit_depth",
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options=["8-bit", "10-bit"],
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default="8-bit",
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advanced=True,
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),
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IO.Combo.Input(
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"interpret_as",
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options=["sRGB"],
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default="sRGB",
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advanced=True,
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),
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],
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),
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IO.DynamicCombo.Option(
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"exr",
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[
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IO.Combo.Input(
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"bit_depth",
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options=["32-bit"],
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default="32-bit",
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advanced=True,
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),
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IO.Combo.Input(
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"interpret_as",
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options=["Linear", "Raw/Data"],
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default="Linear",
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advanced=True,
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),
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],
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),
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],
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tooltip="The file format in which to save the image.",
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),
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IO.Boolean.Input("embed_workflow", default=True, advanced=True),
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],
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hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
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is_output_node=True,
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)
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@classmethod
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def execute(cls, images, filename_prefix: str, format: dict, embed_workflow: bool) -> IO.NodeOutput:
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output_dir = folder_paths.get_output_directory()
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full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.\
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get_save_image_path(filename_prefix, output_dir, images[0].shape[1], images[0].shape[0])
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results = list()
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prompt = cls.hidden.prompt
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extra_pnginfo = cls.hidden.extra_pnginfo
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for batch_number, image in enumerate(images):
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# get widget values from dynamic combo
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file_format = format["format"]
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bit_depth = format["bit_depth"]
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interpret_as = format["interpret_as"]
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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}_.{file_format}"
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file_path = os.path.join(full_output_folder, file)
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if file_format in ["png", "exr", "avif"]:
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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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img_np = img_np[:, :, [1, 2, 0, 3]] if has_alpha else img_np[:, :,[1, 2, 0]]
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av_fmt = 'gbrapf32le' if has_alpha else 'gbrpf32le'
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elif bit_depth in ["10-bit", "12-bit", "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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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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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 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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elif file_format == "avif":
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stream = container.add_stream('libsvtav1', rate=1)
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stream.time_base = Fraction(1, 1)
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|
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if bit_depth in ["10-bit", "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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stream.codec_context.color_range = 2
|
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if interpret_as == "Raw/Data": # 2 == unspecified
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stream.codec_context.colorspace = 2
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stream.codec_context.color_primaries = 2
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stream.codec_context.color_trc = 2
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elif interpret_as == "Linear":
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stream.codec_context.colorspace = 1
|
||||
stream.codec_context.color_primaries = 1
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||||
stream.codec_context.color_trc = 8
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else: # sRGB
|
||||
stream.codec_context.colorspace = 1
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||||
stream.codec_context.color_primaries = 1
|
||||
stream.codec_context.color_trc = 13
|
||||
|
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stream.options = {
|
||||
'preset': '10',
|
||||
'svtav1-params': 'rc=0:qp=20:color-range=1:color-matrix=1:enable-overlays=1',
|
||||
'g': '1'
|
||||
}
|
||||
|
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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'
|
||||
else:
|
||||
stream.pix_fmt = av_fmt
|
||||
|
||||
stream.width = width
|
||||
stream.height = height
|
||||
stream.time_base = Fraction(1, 1)
|
||||
|
||||
is_planar = av_fmt.startswith('gbrp') or 'p' in av_fmt.split('rgba')[-1]
|
||||
if is_planar:
|
||||
if av_fmt.startswith('gbr'):
|
||||
img_np = img_np[:, :, [1, 2, 0, 3]] if has_alpha else img_np[:, :, [1, 2, 0]]
|
||||
img_np = img_np.transpose(2, 0, 1)
|
||||
|
||||
try:
|
||||
frame = av.VideoFrame.from_ndarray(img_np, format=av_fmt)
|
||||
except ValueError:
|
||||
logging.warning("[WARNING] Current FFMPEG Binary can't save natively. Fallbacking.")
|
||||
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)
|
||||
|
||||
# reformat for both avif and exr to ensure correct internal conversion
|
||||
if file_format in ["avif", "exr"] or (file_format == "png" and bit_depth == "16-bit"):
|
||||
reformat_kwargs = {"format": stream.pix_fmt}
|
||||
if file_format == "avif":
|
||||
reformat_kwargs.update({
|
||||
"src_colorspace": 1, "dst_colorspace": 1,
|
||||
"src_color_range": 2, "dst_color_range": 2
|
||||
})
|
||||
frame = frame.reformat(**reformat_kwargs)
|
||||
frame.pts = 0
|
||||
frame.time_base = stream.time_base
|
||||
if file_format == "avif":
|
||||
frame.color_range = 2
|
||||
frame.colorspace = stream.codec_context.colorspace
|
||||
|
||||
for packet in stream.encode(frame):
|
||||
container.mux(packet)
|
||||
for packet in stream.encode():
|
||||
container.mux(packet)
|
||||
|
||||
container.close()
|
||||
|
||||
with open(tmp_path, "rb") as f:
|
||||
final_bytes = f.read()
|
||||
os.remove(tmp_path)
|
||||
|
||||
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})
|
||||
|
||||
class ImagesExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
|
||||
@ -835,6 +1183,7 @@ class ImagesExtension(ComfyExtension):
|
||||
ImageAddNoise,
|
||||
SaveAnimatedWEBP,
|
||||
SaveAnimatedPNG,
|
||||
SaveImageAdvanced,
|
||||
SaveSVGNode,
|
||||
ImageStitch,
|
||||
ResizeAndPadImage,
|
||||
|
||||
4
nodes.py
4
nodes.py
@ -1633,6 +1633,7 @@ class SaveImage:
|
||||
ESSENTIALS_CATEGORY = "Basics"
|
||||
DESCRIPTION = "Saves the input images to your ComfyUI output directory."
|
||||
SEARCH_ALIASES = ["save", "save image", "export image", "output image", "write image", "download"]
|
||||
DEPRECATED = True
|
||||
|
||||
def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
|
||||
filename_prefix += self.prefix_append
|
||||
@ -2138,7 +2139,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"LatentFromBatch" : "Latent From Batch",
|
||||
"RepeatLatentBatch": "Repeat Latent Batch",
|
||||
# Image
|
||||
"SaveImage": "Save Image",
|
||||
"SaveImage": "Save Image (DEPRECATED)",
|
||||
"PreviewImage": "Preview Image",
|
||||
"LoadImage": "Load Image",
|
||||
"LoadImageMask": "Load Image (as Mask)",
|
||||
@ -2445,6 +2446,7 @@ async def init_builtin_extra_nodes():
|
||||
"nodes_rtdetr.py",
|
||||
"nodes_frame_interpolation.py",
|
||||
"nodes_sam3.py",
|
||||
"nodes_convert_color_space.py",
|
||||
]
|
||||
|
||||
import_failed = []
|
||||
|
||||
Loading…
Reference in New Issue
Block a user