feat: ✨ WIP batch from history

Barely tested, and not much safe guards (I need to analyze more
history sessions first, but the basic idea is there)

nodes/image_interpolation.py

@@ -11,9 +11,94 @@
 import numpy as np
 import subprocess
 import comfy
-
+from PIL import Image
+import urllib.request
+import urllib.parse
+import json
 import tensorflow as tf
 import comfy.model_management as model_management
+import io
+
+from comfy.cli_args import args
+from ..utils import pil2tensor
+
+
+def get_image(filename, subfolder, folder_type):
+    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+    url_values = urllib.parse.urlencode(data)
+    with urllib.request.urlopen(
+        "http://{}:{}/view?{}".format(args.listen, args.port, url_values)
+    ) as response:
+        return io.BytesIO(response.read())
+
+
+class GetBatchFromHistory:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "enable": ("BOOL", {"default": True}),
+                "count": ("INT", {"default": 1, "min": 0}),
+                "offset": ("INT", {"default": 0, "min": -1e9, "max": 1e9}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = "images"
+    CATEGORY = "animation"
+    FUNCTION = "load_from_history"
+
+    def load_from_history(
+        self,
+        enable=True,
+        count=0,
+        offset=0,
+    ):
+        if not enable or count == 0:
+            return (torch.zeros(0),)
+        frames = []
+        server_address = "localhost:3000"
+
+        with urllib.request.urlopen(
+            "http://{}/history".format(server_address)
+        ) as response:
+            history = json.loads(response.read())
+
+            output_images = []
+            for k, run in history.items():
+                for o in run["outputs"]:
+                    for node_id in run["outputs"]:
+                        node_output = run["outputs"][node_id]
+                        if "images" in node_output:
+                            images_output = []
+                            for image in node_output["images"]:
+                                image_data = get_image(
+                                    image["filename"], image["subfolder"], image["type"]
+                                )
+                                images_output.append(image_data)
+                            output_images.extend(images_output)
+            if len(output_images) == 0:
+                return (torch.zeros(0),)
+            for i, image in enumerate(list(reversed(output_images))):
+                if i < offset:
+                    continue
+                if i >= offset + count:
+                    break
+                # Decode image as tensor
+                img = Image.open(image)
+                log.debug(f"Image from history {i} of shape {img.size}")
+                frames.append(img)
+
+                # Display the shape of the tensor
+                # print("Tensor shape:", image_tensor.shape)
+
+            # return (output_images,)
+
+            output = pil2tensor(
+                list(reversed(frames)),
+            )
+
+            return (output,)
 
 
 class LoadFilmModel:
@@ -160,7 +245,7 @@ def concat_images(self, imageA: torch.Tensor, imageB: torch.Tensor):
         return (self.concatenate_tensors(imageA, imageB),)
 
 
-class ExportToProRes:
+class ExportToProres:
     """Export to ProRes 4444 (Experimental)"""
 
     def __init__(self):
@@ -196,7 +281,7 @@ def export_prores(
         log.debug(f"Exporting to {output_dir / id}")
 
         frames = tensor2np(images)
-        log.debug(f"Frames type {type(frames)}")
+        log.debug(f"Frames type {type(frames[0])}")
         log.debug(f"Exporting {len(frames)} frames")
 
         frames = [frame.astype(np.uint16) * 257 for frame in frames]
@@ -245,4 +330,10 @@ def export_prores(
         return (out_path,)
 
 
-__nodes__ = [LoadFilmModel, FilmInterpolation, ExportToProRes, ConcatImages]
+__nodes__ = [
+    LoadFilmModel,
+    FilmInterpolation,
+    ExportToProres,
+    ConcatImages,
+    GetBatchFromHistory,
+]