diff --git a/comfy_extras/nodes_convert_color_space.py b/comfy_extras/nodes_convert_color_space.py
new file mode 100644
index 000000000..d9d346c71
--- /dev/null
+++ b/comfy_extras/nodes_convert_color_space.py
@@ -0,0 +1,108 @@
+import torch
+from comfy_api.latest import IO
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension
+
+# 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="ConvertColorSpace",
+            display_name="Convert Color Space",
+            category="image/color",
+            inputs=[
+                IO.Image.Input("images"),
+                IO.Combo.Input("source_color_space", options=["sRGB", "Linear", "HDR Display (PQ/Rec.2020)", "Grayscale"], default="sRGB"),
+                IO.Combo.Input("target_color_space", options=["sRGB", "Linear", "HDR Display (PQ/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 = srgb_to_linear(rgb)
+
+        elif source_color_space == "HDR Display (PQ/Rec.2020)":
+            # assuming Linear Rec.2020 input. Convert to Linear Rec.709
+            matrix = M_2020_to_709.to(device=device, dtype=rgb.dtype)
+            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 Display (PQ/Rec.2020)":
+            # convert Gamut from Linear Rec.709 to Linear Rec.2020
+            rgb = torch.matmul(rgb, M_709_to_2020.to(device=device, dtype=rgb.dtype).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(img_tensor)
+
+
+class ConvertColorSpaceExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            ConvertColorSpace
+        ]
+
+
+async def comfy_entrypoint() -> ConvertColorSpaceExtension:
+    return ConvertColorSpaceExtension()
diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py
index a77f0641f..ba7b94cbd 100644
--- a/comfy_extras/nodes_images.py
+++ b/comfy_extras/nodes_images.py
@@ -3,15 +3,25 @@ from __future__ import annotations
 import nodes
 import folder_paths
 
+import av
 import json
+
 import os
 import re
 import math
+import numpy as np
+import struct
 import torch
+
+import zlib
+import tempfile
+import logging
 import comfy.utils
+from fractions import Fraction
 
 from server import PromptServer
 from comfy_api.latest import ComfyExtension, IO, UI
+from comfy.cli_args import args
 from typing_extensions import override
 
 SVG = IO.SVG.Type  # TODO: temporary solution for backward compatibility, will be removed later.
@@ -823,6 +833,344 @@ class ImageMergeTileList(IO.ComfyNode):
         return IO.NodeOutput(merged_image)
 
 
+
+def create_png_chunk(chunk_type: bytes, data: bytes) -> bytes:
+    """Creates a valid PNG chunk with Length, Type, Data, and CRC32."""
+    chunk = struct.pack('>I', len(data)) + chunk_type + data
+    crc = zlib.crc32(chunk_type + data) & 0xffffffff
+    return chunk + struct.pack('>I', crc)
+
+
+def inject_comfy_metadata_png(png_bytes, prompt=None, extra_pnginfo=None):
+    # IEND chunk is the last 12 bytes of png files
+    content = png_bytes[:-12]
+    iend = png_bytes[-12:]
+
+    metadata_chunks = b""
+
+    if prompt is not None:
+        payload = b'prompt\x00' + json.dumps(prompt).encode('utf-8')
+        metadata_chunks += create_png_chunk(b'tEXt', payload)
+
+    if extra_pnginfo is not None:
+        for k, v in extra_pnginfo.items():
+            payload = k.encode('utf-8') + b'\x00' + json.dumps(v).encode('utf-8')
+            metadata_chunks += create_png_chunk(b'tEXt', payload)
+
+    return content + metadata_chunks + iend
+
+def inject_comfy_metadata_exr(exr_bytes: bytes, prompt, extra_pnginfo) -> bytes:
+    # skip magic and version
+    idx = 8
+
+    # parse through existing attributes to find the end of the header
+    while True:
+        name_start = idx
+        while exr_bytes[idx] != 0:
+            idx += 1
+        name = exr_bytes[name_start:idx]
+        idx += 1
+
+        # empty name means we hit the header terminator
+        if len(name) == 0:
+            break
+
+        # 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
+    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
+    if extra_pnginfo is not None:
+        for k, v in extra_pnginfo.items():
+            k_enc = k.encode('utf-8')[:254]
+            v_enc = json.dumps(v).encode('utf-8')
+            payload += k_enc + b"\x00string\x00" + struct.pack('<I', len(v_enc)) + v_enc
+
+    # 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
+    while table_start + num_entries * 8 < min_offset:
+        offset = struct.unpack('<Q', exr_bytes[table_start + num_entries*8 : table_start + num_entries*8 + 8])[0]
+        if offset < min_offset:
+            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",
+            search_aliases=["save", "save image", "export image", "output image", "write image"],
+            display_name="Save Image",
+            description="Saves the input images to your ComfyUI output directory.",
+            category="image",
+            essentials_category="Basics",
+            inputs=[
+                IO.Image.Input(
+                    "images",
+                    tooltip="The images to save."
+                ),
+                IO.String.Input(
+                    "filename_prefix",
+                    default="ComfyUI",
+                    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.",
+                ),
+                IO.DynamicCombo.Input(
+                    "format",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "png",
+                            [
+                                IO.Combo.Input(
+                                    "bit_depth",
+                                    options=["8-bit", "16-bit"],
+                                    default="8-bit",
+                                    advanced=True,
+                                ),
+                                IO.Combo.Input(
+                                    "interpret_as",
+                                    options=["sRGB", "Raw/Data"],
+                                    default="sRGB",
+                                    advanced=True,
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "avif",
+                            [
+                                IO.Combo.Input(
+                                    "bit_depth",
+                                    options=["8-bit", "10-bit"],
+                                    default="8-bit",
+                                    advanced=True,
+                                ),
+                                IO.Combo.Input(
+                                    "interpret_as",
+                                    options=["sRGB"],
+                                    default="sRGB",
+                                    advanced=True,
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "exr",
+                            [
+                                IO.Combo.Input(
+                                    "bit_depth",
+                                    options=["32-bit"],
+                                    default="32-bit",
+                                    advanced=True,
+                                ),
+                                IO.Combo.Input(
+                                    "interpret_as",
+                                    options=["Linear", "Raw/Data"],
+                                    default="Linear",
+                                    advanced=True,
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="The file format in which to save the image.",
+                ),
+                IO.Boolean.Input("embed_workflow", default=True, advanced=True),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, images, filename_prefix: str, format: dict, embed_workflow: bool) -> 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()
+
+        prompt = cls.hidden.prompt
+        extra_pnginfo = cls.hidden.extra_pnginfo
+
+        for batch_number, image in enumerate(images):
+            # get widget values from dynamic combo
+            file_format = format["format"]
+            bit_depth = format["bit_depth"]
+            interpret_as = format["interpret_as"]
+
+            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"]:
+
+                if bit_depth == "32-bit":
+                    img_np = img_tensor.cpu().numpy().astype(np.float32)
+                    img_np = img_np[:, :, [1, 2, 0, 3]] if has_alpha else img_np[:, :,[1, 2, 0]]
+                    av_fmt = 'gbrapf32le' if has_alpha else 'gbrpf32le'
+                elif bit_depth in ["10-bit", "12-bit", "16-bit"]:
+                    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'
+                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'
+
+                fd, tmp_path = tempfile.mkstemp(suffix=f".{file_format}")
+                os.close(fd)
+                container_format = "image2" if file_format in ["png", "exr"] else "avif"
+                container = av.open(tmp_path, 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('libsvtav1', rate=1)
+
+                    stream.time_base = Fraction(1, 1)
+
+                    if bit_depth in ["10-bit", "16-bit", "32-bit"]:
+                        stream.pix_fmt = 'yuv420p10le'
+                    else:
+                        stream.pix_fmt = 'yuv420p'
+
+                    stream.codec_context.color_range = 2
+                    if interpret_as == "Raw/Data": # 2 == unspecified
+                        stream.codec_context.colorspace = 2
+                        stream.codec_context.color_primaries = 2
+                        stream.codec_context.color_trc = 2
+                    elif interpret_as == "Linear":
+                        stream.codec_context.colorspace = 1
+                        stream.codec_context.color_primaries = 1
+                        stream.codec_context.color_trc = 8
+                    else: # sRGB
+                        stream.codec_context.colorspace = 1
+                        stream.codec_context.color_primaries = 1
+                        stream.codec_context.color_trc = 13
+
+                    stream.options = {
+                        'preset': '10',
+                        'svtav1-params': 'rc=0:qp=20:color-range=1:color-matrix=1:enable-overlays=1',
+                        'g': '1'
+                    }
+
+                elif file_format == "png":
+                    stream = container.add_stream('png', rate=1)
+                    if bit_depth == "16-bit":
+                        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,
diff --git a/nodes.py b/nodes.py
index 8f8f90cf6..4c76b27ad 100644
--- a/nodes.py
+++ b/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
@@ -2121,7 +2122,7 @@ 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)",
@@ -2428,6 +2429,7 @@ async def init_builtin_extra_nodes():
         "nodes_rtdetr.py",
         "nodes_frame_interpolation.py",
         "nodes_sam3.py",
+        "nodes_convert_color_space.py",
     ]
 
     import_failed = []