feat: ✨ use PIL for gif saving

nodes/io.py

@@ -1,4 +1,4 @@
-from ..utils import tensor2np
+from ..utils import tensor2np, PIL_FILTER_MAP
 import uuid
 import folder_paths
 from ..log import log
@@ -7,6 +7,8 @@
 import torch
 from pathlib import Path
 import numpy as np
+from PIL import Image
+from typing import Optional, List
 
 
 class ExportToProres:
@@ -37,9 +39,9 @@ def export_prores(
         if images.size(0) == 0:
             return ("",)
         output_dir = Path(folder_paths.get_output_directory())
-        id = f"{prefix}_{uuid.uuid4()}.mov"
+        file_id = f"{prefix}_{uuid.uuid4()}.mov"
 
-        log.debug(f"Exporting to {output_dir / id}")
+        log.debug(f"Exporting to {output_dir / file_id}")
 
         frames = tensor2np(images)
         log.debug(f"Frames type {type(frames[0])}")
@@ -49,7 +51,7 @@ def export_prores(
 
         height, width, _ = frames[0].shape
 
-        out_path = (output_dir / id).as_posix()
+        out_path = (output_dir / file_id).as_posix()
 
         # Prepare the FFmpeg command
         command = [
@@ -91,6 +93,37 @@ def export_prores(
         return (out_path,)
 
 
+def prepare_animated_batch(
+    batch: torch.Tensor,
+    pingpong=False,
+    resize_by=1.0,
+    resample_filter: Optional[Image.Resampling] = None,
+    image_type=np.uint8,
+) -> List[Image.Image]:
+    images = tensor2np(batch)
+    images = [frame.astype(image_type) for frame in images]
+
+    height, width, _ = batch[0].shape
+
+    if pingpong:
+        reversed_frames = images[::-1]
+        images.extend(reversed_frames)
+    pil_images = [Image.fromarray(frame) for frame in images]
+
+    # Resize frames if necessary
+    if abs(resize_by - 1.0) > 1e-6:
+        new_width = int(width * resize_by)
+        new_height = int(height * resize_by)
+        pil_images_resized = [
+            frame.resize((new_width, new_height), resample=resample_filter)
+            for frame in pil_images
+        ]
+        pil_images = pil_images_resized
+
+    return pil_images
+
+
+# todo: deprecate for apng
 class SaveGif:
     """Save the images from the batch as a GIF"""
 
@@ -103,66 +136,54 @@ def INPUT_TYPES(cls):
                 "resize_by": ("FLOAT", {"default": 1.0, "min": 0.1}),
                 "optimize": ("BOOLEAN", {"default": False}),
                 "pingpong": ("BOOLEAN", {"default": False}),
-            }
+            },
+            "optional": {
+                "resample_filter": (list(PIL_FILTER_MAP.keys()),),
+            },
         }
 
     RETURN_TYPES = ()
     OUTPUT_NODE = True
     CATEGORY = "mtb/IO"
     FUNCTION = "save_gif"
 
-    def save_gif(self, image, fps=12, resize_by=1.0, pingpong=False):
+    def save_gif(
+        self,
+        image,
+        fps=12,
+        resize_by=1.0,
+        optimize=False,
+        pingpong=False,
+        resample_filter=None,
+    ):
         if image.size(0) == 0:
             return ("",)
 
-        images = tensor2np(image)
-        images = [frame.astype(np.uint8) for frame in images]
-        if pingpong:
-            reversed_frames = images[::-1]
-            images.extend(reversed_frames)
+        if resample_filter is not None:
+            resample_filter = PIL_FILTER_MAP.get(resample_filter)
 
-        height, width, _ = image[0].shape
+        pil_images = prepare_animated_batch(
+            image,
+            pingpong,
+            resize_by,
+            resample_filter,
+        )
 
         ruuid = uuid.uuid4()
-
         ruuid = ruuid.hex[:10]
-
         out_path = f"{folder_paths.output_directory}/{ruuid}.gif"
 
-        log.debug(f"Saving a gif file {width}x{height} as {ruuid}.gif")
-
-        # Prepare the FFmpeg command
-        command = [
-            "ffmpeg",
-            "-y",
-            "-f",
-            "rawvideo",
-            "-vcodec",
-            "rawvideo",
-            "-s",
-            f"{width}x{height}",
-            "-pix_fmt",
-            "rgb24",  # GIF only supports rgb24
-            "-r",
-            str(fps),
-            "-i",
-            "-",
-            "-vf",
-            f"fps={fps},scale={width * resize_by}:-1",  # Set frame rate and resize if necessary
-            "-y",
+        # Create the GIF from PIL images
+        pil_images[0].save(
             out_path,
-        ]
-
-        process = subprocess.Popen(command, stdin=subprocess.PIPE)
-
-        for frame in images:
-            model_management.throw_exception_if_processing_interrupted()
-            process.stdin.write(frame.tobytes())
-
-        process.stdin.close()
-        process.wait()
-        results = []
-        results.append({"filename": f"{ruuid}.gif", "subfolder": "", "type": "output"})
+            save_all=True,
+            append_images=pil_images[1:],
+            optimize=optimize,
+            duration=int(1000 / fps),
+            loop=0,
+        )
+
+        results = [{"filename": f"{ruuid}.gif", "subfolder": "", "type": "output"}]
         return {"ui": {"gif": results}}
 
 

utils.py

@@ -50,35 +50,42 @@ def import_install(package_name):
 # endregion
 
 # region GLOBAL VARIABLES
-# Get the absolute path of the parent directory of the current script
+# - Get the absolute path of the parent directory of the current script
 here = Path(__file__).parent.resolve()
 
-# Construct the absolute path to the ComfyUI directory
+# - Construct the absolute path to the ComfyUI directory
 comfy_dir = here.parent.parent
 
-# Construct the path to the font file
+# - Construct the path to the font file
 font_path = here / "font.ttf"
 
-# Add extern folder to path
+# - Add extern folder to path
 extern_root = here / "extern"
 add_path(extern_root)
 for pth in extern_root.iterdir():
     if pth.is_dir():
         add_path(pth)
 
-
-# Add the ComfyUI directory and custom nodes path to the sys.path list
+# - Add the ComfyUI directory and custom nodes path to the sys.path list
 add_path(comfy_dir)
 add_path((comfy_dir / "custom_nodes"))
+
+PIL_FILTER_MAP = {
+    "nearest": Image.Resampling.NEAREST,
+    "box": Image.Resampling.BOX,
+    "bilinear": Image.Resampling.BILINEAR,
+    "hamming": Image.Resampling.HAMMING,
+    "bicubic": Image.Resampling.BICUBIC,
+    "lanczos": Image.Resampling.LANCZOS,
+}
+
+
 # endregion
 
 
 # region TENSOR UTILITIES
 def tensor2pil(image: torch.Tensor) -> List[Image.Image]:
-    batch_count = 1
-    if len(image.shape) > 3:
-        batch_count = image.size(0)
-
+    batch_count = image.size(0) if len(image.shape) > 3 else 1
     if batch_count > 1:
         out = []
         for i in range(batch_count):
@@ -107,9 +114,7 @@ def np2tensor(img_np: np.ndarray | List[np.ndarray]) -> torch.Tensor:
 
 
 def tensor2np(tensor: torch.Tensor) -> List[np.ndarray]:
-    batch_count = 1
-    if len(tensor.shape) > 3:
-        batch_count = tensor.size(0)
+    batch_count = tensor.size(0) if len(tensor.shape) > 3 else 1
     if batch_count > 1:
         out = []
         for i in range(batch_count):