Removed resampling from Pillow access.

Update PyProject Toml - License

.github/workflows/publish.yml

@@ -0,0 +1,21 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - master
+    paths:
+      - "pyproject.toml"
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@main
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

.gitignore

@@ -1,3 +1,4 @@
 .idea
 __pycache__
-config.json
+config.json
+*.cmd

__init__.py

@@ -1,5 +1,8 @@
 # -*- coding: utf-8 -*-
 from typing import Type
+import sys,os
+
+sys.path.append(str(os.path.dirname(os.path.abspath(__file__))))
 
 from .base import *
 from .colors import *
@@ -14,6 +17,8 @@ from .seq_processing import *
 from .switches import *
 from .utility import *
 from .calculate import *
+from .laboratory import *
+#from .lazyswitches import *
 
 _NODE_CLASSES: List[Type] = [DreamSineWave, DreamLinear, DreamCSVCurve, DreamBeatCurve, DreamFrameDimensions,
                              DreamImageMotion, DreamNoiseFromPalette, DreamAnalyzePalette, DreamColorShift,
@@ -21,18 +26,20 @@ _NODE_CLASSES: List[Type] = [DreamSineWave, DreamLinear, DreamCSVCurve, DreamBea
                              DreamSimpleFrameCounter, DreamImageSequenceInputWithDefaultFallback,
                              DreamImageSequenceOutput, DreamCSVGenerator, DreamImageAreaSampler,
                              DreamVideoEncoder, DreamSequenceTweening, DreamSequenceBlend, DreamColorAlign,
-                             DreamImageSampler, DreamNoiseFromAreaPalettes, DreamVideoEncoderMpegCoder,
+                             DreamImageSampler, DreamNoiseFromAreaPalettes,
                              DreamInputString, DreamInputFloat, DreamInputInt, DreamInputText, DreamBigLatentSwitch,
                              DreamFrameCountCalculator, DreamBigImageSwitch, DreamBigTextSwitch, DreamBigFloatSwitch,
                              DreamBigIntSwitch, DreamBigPaletteSwitch, DreamWeightedPromptBuilder, DreamPromptFinalizer,
                              DreamFrameCounterInfo, DreamBoolToFloat, DreamBoolToInt, DreamSawWave, DreamTriangleWave,
                              DreamTriangleEvent, DreamSmoothEvent, DreamCalculation, DreamImageColorShift,
-                             DreamComparePalette, DreamImageContrast, DreamImageBrightness]
+                             DreamComparePalette, DreamImageContrast, DreamImageBrightness, DreamLogFile,
+                             DreamLaboratory, DreamStringToLog, DreamIntToLog, DreamFloatToLog, DreamJoinLog,
+                             DreamStringTokenizer, DreamWavCurve, DreamFrameCounterTimeOffset, DreamRandomPromptWords]
 _SIGNATURE_SUFFIX = " [Dream]"
 
 MANIFEST = {
     "name": "Dream Project Animation",
-    "version": (3, 2, 0),
+    "version": (5, 1, 2),
     "author": "Dream Project",
     "project": "https://github.com/alt-key-project/comfyui-dream-project",
     "description": "Various utility nodes for creating animations with ComfyUI",

base.py

@@ -3,7 +3,7 @@ import glob
 
 from .categories import NodeCategories
 from .shared import *
-from .types import *
+from .dreamtypes import *
 
 
 class DreamFrameCounterInfo:
@@ -22,10 +22,6 @@ class DreamFrameCounterInfo:
                     "elapsed_seconds", "remaining_seconds", "total_seconds", "completion")
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *v):
-        return ALWAYS_CHANGED_FLAG
-
     def result(self, frame_counter: FrameCounter):
         return (frame_counter.current_frame,
                 frame_counter.total_frames,
@@ -56,8 +52,14 @@ class DreamDirectoryFileCount:
     FUNCTION = "result"
 
     @classmethod
-    def IS_CHANGED(cls, *v):
-        return ALWAYS_CHANGED_FLAG
+    def IS_CHANGED(cls, directory_path, patterns):
+        if not os.path.isdir(directory_path):
+            return ""
+        total = 0
+        for pattern in patterns.split("|"):
+            files = list(glob.glob(pattern, root_dir=directory_path))
+            total += len(files)
+        return total
 
     def result(self, directory_path, patterns):
         if not os.path.isdir(directory_path):
@@ -66,7 +68,6 @@ class DreamDirectoryFileCount:
         for pattern in patterns.split("|"):
             files = list(glob.glob(pattern, root_dir=directory_path))
             total += len(files)
-        print("total " + str(total))
         return (total,)
 
 
@@ -88,13 +89,31 @@ class DreamFrameCounterOffset:
     RETURN_NAMES = ("frame_counter",)
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, frame_counter, offset):
-        return hashed_as_strings(frame_counter, offset)
-
     def result(self, frame_counter: FrameCounter, offset):
         return (frame_counter.incremented(offset),)
 
+class DreamFrameCounterTimeOffset:
+    NODE_NAME = "Frame Counter Time Offset"
+
+    ICON = "±"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": SharedTypes.frame_counter | {
+                "offset_seconds": ("FLOAT", {"default": 0.0}),
+            },
+        }
+
+    CATEGORY = NodeCategories.ANIMATION
+    RETURN_TYPES = (FrameCounter.ID,)
+    RETURN_NAMES = ("frame_counter",)
+    FUNCTION = "result"
+
+    def result(self, frame_counter: FrameCounter, offset_seconds):
+        offset = offset_seconds * frame_counter.frames_per_second
+        return (frame_counter.incremented(offset),)
+
 
 class DreamSimpleFrameCounter:
     NODE_NAME = "Frame Counter (Simple)"
@@ -115,10 +134,6 @@ class DreamSimpleFrameCounter:
     RETURN_NAMES = ("frame_counter",)
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def result(self, frame_index, total_frames, frames_per_second):
         n = frame_index
         return (FrameCounter(n, total_frames, frames_per_second),)
@@ -146,8 +161,14 @@ class DreamDirectoryBackedFrameCounter:
     FUNCTION = "result"
 
     @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
+    def IS_CHANGED(cls, directory_path, patterns, indexing, total_frames, frames_per_second):
+        if not os.path.isdir(directory_path):
+            return ""
+        total = 0
+        for pattern in patterns.split("|"):
+            files = list(glob.glob(pattern, root_dir=directory_path))
+            total += len(files)
+        return (total, indexing, total_frames, frames_per_second)
 
     def result(self, directory_path, pattern, indexing, total_frames, frames_per_second):
         results = list_images_in_directory(directory_path, pattern, indexing == "alphabetic order")
@@ -178,10 +199,6 @@ class DreamFrameCountCalculator:
     RETURN_NAMES = ("TOTAL",)
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *v):
-        return ALWAYS_CHANGED_FLAG
-
     def result(self, hours, minutes, seconds, milliseconds, frames_per_second):
         total_s = seconds + 0.001 * milliseconds + minutes * 60 + hours * 3600
         return (round(total_s * frames_per_second),)

calculate.py

@@ -33,10 +33,6 @@ class DreamCalculation:
     RETURN_NAMES = ("FLOAT", "INT")
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def _make_model(self):
         funcs = self._make_functions()
         m = base_eval_model.clone()

colors.py

@@ -2,7 +2,7 @@
 
 from .categories import NodeCategories
 from .shared import *
-from .types import *
+from .dreamtypes import *
 
 
 class DreamImageAreaSampler:
@@ -26,10 +26,6 @@ class DreamImageAreaSampler:
     RETURN_NAMES = ("palette",)
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def _get_pixel_area(self, img: DreamImage, area):
         w = img.width
         h = img.height
@@ -102,10 +98,6 @@ class DreamImageSampler:
     RETURN_NAMES = ("palette",)
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def result(self, image, samples, seed):
         result = list()
         r = random.Random()
@@ -139,10 +131,6 @@ class DreamColorAlign:
     RETURN_NAMES = ("palette",)
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def result(self, palette: Tuple[RGBPalette], target_align: Tuple[RGBPalette], alignment_factor: float):
         results = list()
 
@@ -187,10 +175,6 @@ class DreamColorShift:
     RETURN_NAMES = ("palette",)
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def result(self, palette, red_multiplier, green_multiplier, blue_multiplier, fixed_brightness):
         results = list()
 
@@ -234,10 +218,6 @@ class DreamImageColorShift:
     RETURN_NAMES = ("image",)
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def result(self, image, red_multiplier, green_multiplier, blue_multiplier):
         proc = DreamImageProcessor(inputs=image)
 
@@ -265,10 +245,6 @@ class DreamImageBrightness:
     RETURN_NAMES = ("image",)
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def result(self, image, factor):
         proc = DreamImageProcessor(inputs=image)
 
@@ -296,10 +272,6 @@ class DreamImageContrast:
     RETURN_NAMES = ("image",)
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def result(self, image, factor):
         proc = DreamImageProcessor(inputs=image)
 
@@ -328,10 +300,6 @@ class DreamComparePalette:
         "brightness_multiplier", "contrast_multiplier", "red_multiplier", "green_multiplier", "blue_multiplier")
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def result(self, a, b):
         MIN_VALUE = 1 / 255.0
 
@@ -372,10 +340,6 @@ class DreamAnalyzePalette:
     RETURN_NAMES = ("brightness", "contrast", "redness", "greenness", "blueness")
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def result(self, palette):
         f = 1.0 / len(palette)
         (w, c, r, g, b) = (0, 0, 0, 0, 0)

curves.py

@@ -1,10 +1,14 @@
 # -*- coding: utf-8 -*-
 import csv
+import functools
 import math
+import os
+
+from scipy.io.wavfile import read as wav_read
 
 from .categories import NodeCategories
 from .shared import hashed_as_strings
-from .types import SharedTypes, FrameCounter
+from .dreamtypes import SharedTypes, FrameCounter
 
 
 def _linear_value_calc(x, x_start, x_end, y_start, y_end):
@@ -40,10 +44,6 @@ class DreamSineWave:
     RETURN_NAMES = ("FLOAT", "INT")
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def result(self, frame_counter: FrameCounter, max_value, min_value, periodicity_seconds, phase):
         x = frame_counter.current_time_in_seconds
         a = (max_value - min_value) * 0.5
@@ -73,10 +73,6 @@ class DreamSawWave:
     RETURN_NAMES = ("FLOAT", "INT")
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def result(self, frame_counter: FrameCounter, max_value, min_value, periodicity_seconds, phase):
         x = frame_counter.current_time_in_seconds
         x = ((x + periodicity_seconds * phase) % periodicity_seconds) / periodicity_seconds
@@ -103,10 +99,6 @@ class DreamTriangleWave:
     RETURN_NAMES = ("FLOAT", "INT")
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def result(self, frame_counter: FrameCounter, max_value, min_value, periodicity_seconds, phase):
         x = frame_counter.current_time_in_seconds
         x = ((x + periodicity_seconds * phase) % periodicity_seconds) / periodicity_seconds
@@ -119,6 +111,66 @@ class DreamTriangleWave:
         return _curve_result(y)
 
 
+class WavData:
+    def __init__(self, sampling_rate: float, single_channel_samples, fps: float):
+        self._length_in_seconds = len(single_channel_samples) / sampling_rate
+        self._num_buckets = round(self._length_in_seconds * fps * 3)
+        self._bucket_size = len(single_channel_samples) / float(self._num_buckets)
+        self._buckets = list()
+        self._rate = sampling_rate
+        self._max_bucket_value = 0
+        for i in range(self._num_buckets):
+            start_index = round(i * self._bucket_size)
+            end_index = round((i + 1) * self._bucket_size) - 1
+            samples = list(map(lambda n: abs(n), single_channel_samples[start_index:end_index]))
+            bucket_total = sum(samples)
+            self._buckets.append(bucket_total)
+            self._max_bucket_value=max(bucket_total, self._max_bucket_value)
+
+        for i in range(self._num_buckets):
+            self._buckets[i] = float(self._buckets[i]) / self._max_bucket_value
+
+    def value_at_time(self, second: float) -> float:
+        if second < 0.0 or second > self._length_in_seconds:
+            return 0.0
+        nsample = second * self._rate
+        nbucket = min(max(0, round(nsample / self._bucket_size)), self._num_buckets - 1)
+        return self._buckets[nbucket]
+
+
+@functools.lru_cache(4)
+def _wav_loader(filepath, fps):
+    sampling_rate, samples = wav_read(filepath)
+    single_channel = samples[:, 0]
+    return WavData(sampling_rate, single_channel, fps)
+
+
+class DreamWavCurve:
+    NODE_NAME = "WAV Curve"
+    CATEGORY = NodeCategories.ANIMATION_CURVES
+    RETURN_TYPES = ("FLOAT", "INT")
+    RETURN_NAMES = ("FLOAT", "INT")
+    FUNCTION = "result"
+    ICON = "∿"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": SharedTypes.frame_counter | {
+                "wav_path": ("STRING", {"default": "audio.wav"}),
+                "scale": ("FLOAT", {"default": 1.0, "multiline": False})
+            },
+        }
+
+    def result(self, frame_counter: FrameCounter, wav_path, scale):
+        if not os.path.isfile(wav_path):
+            return (0.0, 0)
+        data = _wav_loader(wav_path, frame_counter.frames_per_second)
+        frame_counter.current_time_in_seconds
+        v = data.value_at_time(frame_counter.current_time_in_seconds)
+        return (v * scale, round(v * scale))
+
+
 class DreamTriangleEvent:
     NODE_NAME = "Triangle Event Curve"
 
@@ -138,10 +190,6 @@ class DreamTriangleEvent:
     RETURN_NAMES = ("FLOAT", "INT")
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def result(self, frame_counter: FrameCounter, max_value, min_value, width_seconds, center_seconds):
         x = frame_counter.current_time_in_seconds
         start = center_seconds - width_seconds * 0.5
@@ -174,10 +222,6 @@ class DreamSmoothEvent:
     RETURN_NAMES = ("FLOAT", "INT")
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def result(self, frame_counter: FrameCounter, max_value, min_value, width_seconds, center_seconds):
         x = frame_counter.current_time_in_seconds
         start = center_seconds - width_seconds * 0.5
@@ -213,9 +257,9 @@ class DreamBeatCurve:
                 "accent_1": ("INT", {"default": 1, "min": 1, "max": 24}),
             },
             "optional": {
-                "accent_2": ("INT", {"default": 3, "min": 1, "max": 24}),
-                "accent_3": ("INT", {"default": 0}),
-                "accent_4": ("INT", {"default": 0}),
+                "accent_2": ("INT", {"default": 0, "min": 0, "max": 24}),
+                "accent_3": ("INT", {"default": 0, "min": 0, "max": 24}),
+                "accent_4": ("INT", {"default": 0, "min": 0, "max": 24}),
             }
         }
 
@@ -224,10 +268,6 @@ class DreamBeatCurve:
     RETURN_NAMES = ("FLOAT", "INT")
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def _get_value_for_accent(self, accent, measure_length, bpm, frame_counter: FrameCounter, frame_offset):
         current_frame = frame_counter.current_frame + frame_offset
         frames_per_minute = frame_counter.frames_per_second * 60.0
@@ -272,10 +312,6 @@ class DreamLinear:
     RETURN_NAMES = ("FLOAT", "INT")
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def result(self, initial_value, final_value, frame_counter: FrameCounter):
         d = final_value - initial_value
         v = initial_value + frame_counter.progress * d
@@ -310,10 +346,6 @@ class DreamCSVGenerator:
     FUNCTION = "write"
     OUTPUT_NODE = True
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def write(self, csvfile, frame_counter: FrameCounter, value, csv_dialect):
         if frame_counter.is_first_frame and csvfile:
             with open(csvfile, 'w', newline='') as csvfile:
@@ -346,10 +378,6 @@ class DreamCSVCurve:
     RETURN_NAMES = ("FLOAT", "INT")
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def _row_yield(self, file, csv_dialect):
         prev_row = None
         for row in csv.reader(file, dialect=csv_dialect):

dreamtypes.py

@@ -2,7 +2,7 @@
 import random
 import time
 
-from typing import List, Dict
+from typing import List, Dict, Tuple
 
 from .shared import DreamImage
 
@@ -133,6 +133,35 @@ class PartialPrompt:
         return ", ".join(pos), ", ".join(neg)
 
 
+class LogEntry:
+    ID = "LOG_ENTRY"
+
+    @classmethod
+    def new(cls, text):
+        return LogEntry([(time.time(), text)])
+
+    def __init__(self, data: List[Tuple[float, str]] = None):
+        if data is None:
+            self._data = list()
+        else:
+            self._data = list(data)
+
+    def add(self, text: str):
+        new_data = list(self._data)
+        new_data.append((time.time(), text))
+        return LogEntry(new_data)
+
+    def merge(self, log_entry):
+        new_data = list(self._data)
+        new_data.extend(log_entry._data)
+        return LogEntry(new_data)
+
+    def get_filtered_entries(self, t: float):
+        for d in sorted(self._data):
+            if d[0] > t:
+                yield d
+
+
 class FrameCounter:
     ID = "FRAME_COUNTER"
 

embedded_config.py

@@ -3,7 +3,7 @@ EMBEDDED_CONFIGURATION = {
         "file_extension": "mp4",
         "path": "ffmpeg",
         "arguments": ["-r", "%FPS%", "-f", "concat", "-safe", "0", "-vsync",
-                      "cfr", "-i", "%FRAMES%", "-c:v", "libx265", "-pix_fmt",
+                      "cfr", "-i", "%FRAMES%", "-c:v", "libx264", "-pix_fmt",
                       "yuv420p", "%OUTPUT%"]
     },
     "mpeg_coder": {
@@ -11,7 +11,7 @@ EMBEDDED_CONFIGURATION = {
         "bitrate_factor": 1.0,
         "max_b_frame": 2,
         "file_extension": "mp4",
-        "codec_name": "libx265"
+        "codec_name": "libx264"
     },
     "encoding": {
         "jpeg_quality": 95

examples/motion-workflow-example.json

@@ -1,6 +1,6 @@
 {
-  "last_node_id": 422,
-  "last_link_id": 5466,
+  "last_node_id": 423,
+  "last_link_id": 5467,
   "nodes": [
     {
       "id": 77,
@@ -24,13 +24,7 @@
             2312
           ],
           "widget": {
-            "name": "text_2",
-            "config": [
-              "STRING",
-              {
-                "multiline": true
-              }
-            ]
+            "name": "text"
           }
         }
       ],
@@ -65,13 +59,7 @@
           ],
           "slot_index": 0,
           "widget": {
-            "name": "text",
-            "config": [
-              "STRING",
-              {
-                "multiline": true
-              }
-            ]
+            "name": "text"
           }
         }
       ],
@@ -1101,15 +1089,7 @@
           "type": "INT",
           "link": 5351,
           "widget": {
-            "name": "total_frames",
-            "config": [
-              "INT",
-              {
-                "default": 100,
-                "min": 2,
-                "max": 5184000
-              }
-            ]
+            "name": "total_frames"
           },
           "slot_index": 0
         },
@@ -1118,14 +1098,7 @@
           "type": "STRING",
           "link": 5347,
           "widget": {
-            "name": "directory_path",
-            "config": [
-              "STRING",
-              {
-                "default": "",
-                "multiline": false
-              }
-            ]
+            "name": "directory_path"
           }
         },
         {
@@ -1133,14 +1106,7 @@
           "type": "INT",
           "link": 5350,
           "widget": {
-            "name": "frames_per_second",
-            "config": [
-              "INT",
-              {
-                "min": 1,
-                "default": 25
-              }
-            ]
+            "name": "frames_per_second"
           }
         }
       ],
@@ -1503,13 +1469,7 @@
           "type": "STRING",
           "link": 2312,
           "widget": {
-            "name": "text",
-            "config": [
-              "STRING",
-              {
-                "multiline": true
-              }
-            ]
+            "name": "text"
           },
           "slot_index": 1
         }
@@ -1560,13 +1520,7 @@
           "type": "STRING",
           "link": 2309,
           "widget": {
-            "name": "text",
-            "config": [
-              "STRING",
-              {
-                "multiline": true
-              }
-            ]
+            "name": "text"
           }
         }
       ],
@@ -1995,16 +1949,7 @@
           "type": "INT",
           "link": 5074,
           "widget": {
-            "name": "width",
-            "config": [
-              "INT",
-              {
-                "default": 512,
-                "min": 1,
-                "max": 8192,
-                "step": 1
-              }
-            ]
+            "name": "width"
           }
         },
         {
@@ -2012,16 +1957,7 @@
           "type": "INT",
           "link": 5075,
           "widget": {
-            "name": "height",
-            "config": [
-              "INT",
-              {
-                "default": 512,
-                "min": 1,
-                "max": 8192,
-                "step": 1
-              }
-            ]
+            "name": "height"
           }
         }
       ],
@@ -2462,16 +2398,7 @@
           "type": "FLOAT",
           "link": 5222,
           "widget": {
-            "name": "x_translation",
-            "config": [
-              "FLOAT",
-              {
-                "default": 0,
-                "min": -10,
-                "max": 10,
-                "step": 0.01
-              }
-            ]
+            "name": "x_translation"
           }
         },
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@@ -3227,6 +3128,12 @@
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-      "type": "Video Encoder (mpegCoder) [Dream]",
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+      "color": "#b58b2a",
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@@ -4979,7 +4896,8 @@
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+      "color": "#929054",
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@@ -4989,7 +4907,8 @@
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+      "color": "#3f789e",
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@@ -4999,7 +4918,8 @@
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@@ -5009,7 +4929,8 @@
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+      "color": "#b06634",
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@@ -5019,7 +4940,8 @@
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+      "color": "#3f789e",
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@@ -5029,7 +4951,8 @@
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+      "color": "#ef75ff",
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@@ -5049,7 +4973,8 @@
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+      "color": "#444",
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     }
   ],
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examples/motion-workflow-with-color-coherence.json

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@@ -2112,7 +1986,7 @@
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@@ -3580,14 +3403,7 @@
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@@ -3601,6 +3417,12 @@
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@@ -3664,7 +3486,7 @@
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@@ -4100,6 +3915,12 @@
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           "slot_index": 0
+        },
+        {
+          "name": "frame_name",
+          "type": "STRING",
+          "links": null,
+          "shape": 3
         }
       ],
       "properties": {
@@ -4123,7 +3944,7 @@
         26
       ],
       "flags": {},
-      "order": 31,
+      "order": 36,
       "mode": 0,
       "inputs": [
         {
@@ -4306,7 +4127,7 @@
         256,
         0.11818237304687501,
         0.5,
-        340337213218643,
+        855083604429355,
         "randomize"
       ],
       "color": "#2a363b",
@@ -4443,7 +4264,7 @@
       "flags": {
         "collapsed": false
       },
-      "order": 17,
+      "order": 16,
       "mode": 0,
       "inputs": [],
       "outputs": [
@@ -4493,7 +4314,7 @@
         26
       ],
       "flags": {},
-      "order": 27,
+      "order": 26,
       "mode": 0,
       "inputs": [
         {
@@ -4529,7 +4350,7 @@
         26
       ],
       "flags": {},
-      "order": 22,
+      "order": 27,
       "mode": 0,
       "inputs": [
         {
@@ -4607,7 +4428,7 @@
         "1": 90
       },
       "flags": {},
-      "order": 18,
+      "order": 17,
       "mode": 0,
       "title": "Note on motion",
       "properties": {
@@ -4705,7 +4526,7 @@
         26
       ],
       "flags": {},
-      "order": 36,
+      "order": 34,
       "mode": 0,
       "inputs": [
         {
@@ -5031,7 +4852,7 @@
           "name": "sequence",
           "type": "ANIMATION_SEQUENCE",
           "links": [
-            5462
+            5463
           ],
           "shape": 3,
           "slot_index": 0
@@ -5047,11 +4868,11 @@
       "bgcolor": "#593930"
     },
     {
-      "id": 421,
-      "type": "Video Encoder (mpegCoder) [Dream]",
+      "id": 422,
+      "type": "FFMPEG Video Encoder [Dream]",
       "pos": [
-        4778,
-        559
+        4781,
+        562
       ],
       "size": {
         "0": 315,
@@ -5064,17 +4885,66 @@
         {
           "name": "sequence",
           "type": "ANIMATION_SEQUENCE",
-          "link": 5462
+          "link": 5463
+        }
+      ],
+      "outputs": [
+        {
+          "name": "log_entry",
+          "type": "LOG_ENTRY",
+          "links": null,
+          "shape": 3
         }
       ],
       "properties": {
-        "Node name for S&R": "Video Encoder (mpegCoder) [Dream]"
+        "Node name for S&R": "FFMPEG Video Encoder [Dream]"
       },
       "widgets_values": [
         "video",
         1,
         true
       ]
+    },
+    {
+      "id": 236,
+      "type": "LoadImage",
+      "pos": [
+        -2106,
+        679
+      ],
+      "size": {
+        "0": 430,
+        "1": 340
+      },
+      "flags": {},
+      "order": 18,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            5313
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "7159726-HSC00002-7 (1).jpg",
+        "image"
+      ],
+      "color": "#2a363b",
+      "bgcolor": "#3f5159"
     }
   ],
   "links": [
@@ -6223,10 +6093,10 @@
       "IMAGE"
     ],
     [
-      5462,
+      5463,
       281,
       0,
-      421,
+      422,
       0,
       "ANIMATION_SEQUENCE"
     ]
@@ -6240,7 +6110,8 @@
         474,
         885
       ],
-      "color": "#a1309b"
+      "color": "#a1309b",
+      "font_size": 24
     },
     {
       "title": "Settings",
@@ -6250,7 +6121,8 @@
         482,
         742
       ],
-      "color": "#b58b2a"
+      "color": "#b58b2a",
+      "font_size": 24
     },
     {
       "title": "Inpainting/Outpainting",
@@ -6260,7 +6132,8 @@
         1711,
         528
       ],
-      "color": "#929054"
+      "color": "#929054",
+      "font_size": 24
     },
     {
       "title": "Prev Frame Move",
@@ -6270,7 +6143,8 @@
         1450,
         456
       ],
-      "color": "#3f789e"
+      "color": "#3f789e",
+      "font_size": 24
     },
     {
       "title": "Full frame sampler",
@@ -6280,7 +6154,8 @@
         691,
         700
       ],
-      "color": "#88A"
+      "color": "#88A",
+      "font_size": 24
     },
     {
       "title": "Output",
@@ -6290,7 +6165,8 @@
         1458,
         381
       ],
-      "color": "#b06634"
+      "color": "#b06634",
+      "font_size": 24
     },
     {
       "title": "Animation Driver",
@@ -6300,7 +6176,8 @@
         336,
         373
       ],
-      "color": "#3f789e"
+      "color": "#3f789e",
+      "font_size": 24
     },
     {
       "title": "Motion Control",
@@ -6310,7 +6187,8 @@
         1451,
         392
       ],
-      "color": "#ef75ff"
+      "color": "#ef75ff",
+      "font_size": 24
     },
     {
       "title": "Model selection",
@@ -6320,7 +6198,8 @@
         476,
         204
       ],
-      "color": "#3f789e"
+      "color": "#3f789e",
+      "font_size": 24
     },
     {
       "title": "Noise",
@@ -6330,7 +6209,8 @@
         1840,
         765
       ],
-      "color": "#3f789e"
+      "color": "#3f789e",
+      "font_size": 24
     },
     {
       "title": "Previews",
@@ -6340,7 +6220,8 @@
         6473,
         329
       ],
-      "color": "#444"
+      "color": "#444",
+      "font_size": 24
     }
   ],
   "config": {},

image_processing.py

@@ -4,13 +4,11 @@ import math
 import numpy
 import torch
 from PIL import Image, ImageDraw
-from PIL.Image import Resampling
 
 from .categories import *
-from .shared import ALWAYS_CHANGED_FLAG, convertTensorImageToPIL, DreamImageProcessor, \
+from .shared import convertTensorImageToPIL, DreamImageProcessor, \
     DreamImage, DreamMask
-from .types import SharedTypes, FrameCounter
-
+from .dreamtypes import SharedTypes, FrameCounter
 
 class DreamImageMotion:
     NODE_NAME = "Image Motion"
@@ -42,10 +40,6 @@ class DreamImageMotion:
     RETURN_NAMES = ("image", "mask1", "mask2", "mask3")
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def _mk_PIL_image(self, size, color=None, mode="RGB") -> Image:
         im = Image.new(mode=mode, size=size)
         if color:
@@ -77,7 +71,7 @@ class DreamImageMotion:
             return min(max(i, 1), 32767)
 
         if output_resize_height and output_resize_width:
-            return lambda img: img.resize((bound(output_resize_width), bound(output_resize_height)), Resampling.NEAREST)
+            return lambda img: img.resize((bound(output_resize_width), bound(output_resize_height)))
         else:
             return lambda img: img
 
@@ -98,12 +92,12 @@ class DreamImageMotion:
             noise = other.get("noise", None)
             multiplier = math.pow(2, zoom)
             resized_image = pil_image.resize((round(pil_image.width * multiplier),
-                                              round(pil_image.height * multiplier)), Resampling.BILINEAR)
+                                              round(pil_image.height * multiplier)))
 
             if noise is None:
                 base_image = self._mk_PIL_image(pil_image.size, "black")
             else:
-                base_image = convertTensorImageToPIL(noise).resize(pil_image.size, Resampling.BILINEAR)
+                base_image = convertTensorImageToPIL(noise).resize(pil_image.size)
 
             selection_offset = (round(x_translation * pil_image.width), round(y_translation * pil_image.height))
             selection = ((pil_image.width - resized_image.width) // 2 + selection_offset[0],

inputfields.py

@@ -18,10 +18,6 @@ class DreamInputText:
     RETURN_NAMES = ("STRING",)
     FUNCTION = "noop"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def noop(self, value):
         return (value,)
 
@@ -42,10 +38,6 @@ class DreamInputString:
     RETURN_NAMES = ("STRING",)
     FUNCTION = "noop"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def noop(self, value):
         return (value,)
 
@@ -67,10 +59,6 @@ class DreamInputFloat:
     RETURN_NAMES = ("FLOAT",)
     FUNCTION = "noop"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def noop(self, value):
         return (value,)
 
@@ -92,9 +80,5 @@ class DreamInputInt:
     RETURN_NAMES = ("INT",)
     FUNCTION = "noop"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def noop(self, value):
         return (value,)

install.py

@@ -1,10 +1,14 @@
 # -*- coding: utf-8 -*-
-from .shared import DreamConfig
+import sys, os
+
+#sys.path.append(str(os.path.dirname(os.path.abspath(__file__))))
+
+#from . import shared
 
 
 def setup_default_config():
-    DreamConfig()
-
+    #shared.DreamConfig()
+    pass
 
 def run_install():
     setup_default_config()

laboratory.py

@@ -0,0 +1,96 @@
+# -*- coding: utf-8 -*-
+
+# -*- coding: utf-8 -*-
+
+import json
+
+from .categories import *
+from .shared import DreamStateFile
+from .dreamtypes import *
+
+_laboratory_state = DreamStateFile("laboratory")
+
+
+class DreamLaboratory:
+    NODE_NAME = "Laboratory"
+    ICON = "🧪"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": SharedTypes.frame_counter | {
+                "key": ("STRING", {"default": "Random value " + str(random.randint(0, 1000000))}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "renew_policy": (["every frame", "first frame"],),
+                "min_value": ("FLOAT", {"default": 0.0}),
+                "max_value": ("FLOAT", {"default": 1.0}),
+                "mode": (["random uniform", "random bell", "ladder", "random walk"],),
+            },
+            "optional": {
+                "step_size": ("FLOAT", {"default": 0.1}),
+            },
+        }
+
+    CATEGORY = NodeCategories.UTILS
+    RETURN_TYPES = ("FLOAT", "INT", LogEntry.ID)
+    RETURN_NAMES = ("FLOAT", "INT", "log_entry")
+    FUNCTION = "result"
+
+    def _generate(self, seed, last_value, min_value, max_value, mode, step_size):
+        rnd = random.Random()
+        rnd.seed(seed)
+
+        def jsonify(v: float):
+            return json.loads(json.dumps(v))
+
+        if mode == "random uniform":
+            return jsonify(self._mode_uniform(rnd, last_value, min_value, max_value, step_size))
+        elif mode == "random bell":
+            return jsonify(self._mode_bell(rnd, last_value, min_value, max_value, step_size))
+        elif mode == "ladder":
+            return jsonify(self._mode_ladder(rnd, last_value, min_value, max_value, step_size))
+        else:
+            return jsonify(self._mode_walk(rnd, last_value, min_value, max_value, step_size))
+
+    def _mode_uniform(self, rnd: random.Random, last_value: float, min_value: float, max_value: float, step_size):
+        return rnd.random() * (max_value - min_value) + min_value
+
+    def _mode_bell(self, rnd: random.Random, last_value: float, min_value: float, max_value: float, step_size):
+        s = 0.0
+        for i in range(3):
+            s += rnd.random() * (max_value - min_value) + min_value
+        return s / 3.0
+
+    def _mode_ladder(self, rnd: random.Random, last_value: float, min_value: float, max_value: float, step_size):
+        if last_value is None:
+            last_value = min_value - step_size
+        next_value = last_value + step_size
+        if next_value > max_value:
+            d = abs(max_value - min_value)
+            next_value = (next_value - min_value) % d + min_value
+        return next_value
+
+    def _mode_walk(self, rnd: random.Random, last_value: float, min_value: float, max_value: float, step_size):
+        if last_value is None:
+            last_value = (max_value - min_value) * 0.5
+        if rnd.random() >= 0.5:
+            return min(max_value, last_value + step_size)
+        else:
+            return max(min_value, last_value - step_size)
+
+    def result(self, key, frame_counter: FrameCounter, seed, renew_policy, min_value, max_value, mode, **values):
+        if min_value > max_value:
+            t = max_value
+            max_value = min_value
+            min_value = t
+        step_size = values.get("step_size", abs(max_value - min_value) * 0.1)
+        last_value = _laboratory_state.get_section("values").get(key, None)
+
+        if (last_value is None) or (renew_policy == "every frame") or frame_counter.is_first_frame:
+            v = _laboratory_state.get_section("values") \
+                .update(key, 0, lambda old: self._generate(seed, last_value, min_value, max_value, mode, step_size))
+            return v, round(v), LogEntry.new(
+                "Laboratory generated new value for '{}': {} ({})".format(key, v, round(v)))
+        else:
+            return last_value, round(last_value), LogEntry.new("Laboratory reused value for '{}': {} ({})"
+                                                               .format(key, last_value, round(last_value)))

lazyswitches.py

@@ -0,0 +1,151 @@
+from .categories import NodeCategories
+from .dreamtypes import RGBPalette
+
+_NOT_A_VALUE_I = 9223372036854775807
+_NOT_A_VALUE_F = float(_NOT_A_VALUE_I)
+_NOT_A_VALUE_S = "⭆"
+
+def _generate_switch_input(type_nm: str, default_value=None):
+    d = dict()
+    for i in range(10):
+        if default_value is None:
+            d["input_" + str(i)] = (type_nm, {"lazy": True})
+        else:
+            d["input_" + str(i)] = (type_nm, {"default": default_value, "forceInput": True, "lazy": True})
+
+    return {
+        "required": {
+            "select": ("INT", {"default": 0, "min": 0, "max": 9})
+        },
+        "optional": d
+    }
+
+
+def _check_big_switch_lazy_status(*args, **kwargs):
+    n = int(kwargs['select'])
+    input_name = f"input_{n}"
+    print(f"SELECTED: {input_name}")
+    if input_name in kwargs:
+        return [input_name]
+    else:
+        return []
+
+
+class DreamLazyImageSwitch:
+    _switch_type = "IMAGE"
+    NODE_NAME = "Lazy Image Switch"
+    ICON = "⭆"
+    CATEGORY = NodeCategories.UTILS_SWITCHES
+    RETURN_TYPES = (_switch_type,)
+    RETURN_NAMES = ("selected",)
+    FUNCTION = "pick"
+        
+    @classmethod
+    def INPUT_TYPES(cls):
+        return _generate_switch_input(cls._switch_type)
+
+    def check_lazy_status(self, *args, **kwargs):
+        return _check_big_switch_lazy_status(*args, **kwargs)
+
+    def pick(self, select, **args):
+        return (args.get("input_"+str(select), None),)
+
+
+class DreamLazyLatentSwitch:
+    _switch_type = "LATENT"
+    NODE_NAME = "Lazy Latent Switch"
+    ICON = "⭆"
+    CATEGORY = NodeCategories.UTILS_SWITCHES
+    RETURN_TYPES = (_switch_type,)
+    RETURN_NAMES = ("selected",)
+    FUNCTION = "pick"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return _generate_switch_input(cls._switch_type)
+
+    def check_lazy_status(self, *args, **kwargs):
+        return _check_big_switch_lazy_status(*args, **kwargs)
+
+    def pick(self, select, **args):
+        return (args.get("input_" + str(select), None),)
+
+
+class DreamLazyTextSwitch:
+    _switch_type = "STRING"
+    NODE_NAME = "Lazy Text Switch"
+    ICON = "⭆"
+    CATEGORY = NodeCategories.UTILS_SWITCHES
+    RETURN_TYPES = (_switch_type,)
+    RETURN_NAMES = ("selected",)
+    FUNCTION = "pick"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return _generate_switch_input(cls._switch_type, _NOT_A_VALUE_S)
+
+    def check_lazy_status(self, *args, **kwargs):
+        return _check_big_switch_lazy_status(*args, **kwargs)
+
+    def pick(self, select, **args):
+        return (args.get("input_" + str(select), None),)
+
+
+class DreamLazyPaletteSwitch:
+    _switch_type = RGBPalette.ID
+    NODE_NAME = "Lazy Palette Switch"
+    ICON = "⭆"
+    CATEGORY = NodeCategories.UTILS_SWITCHES
+    RETURN_TYPES = (_switch_type,)
+    RETURN_NAMES = ("selected",)
+    FUNCTION = "pick"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return _generate_switch_input(cls._switch_type)
+
+    def check_lazy_status(self, *args, **kwargs):
+        return _check_big_switch_lazy_status(*args, **kwargs)
+
+    def pick(self, select, **args):
+        return (args.get("input_" + str(select), None),)
+
+
+class DreamLazyFloatSwitch:
+    _switch_type = "FLOAT"
+    NODE_NAME = "Lazy Float Switch"
+    ICON = "⭆"
+    CATEGORY = NodeCategories.UTILS_SWITCHES
+    RETURN_TYPES = (_switch_type,)
+    RETURN_NAMES = ("selected",)
+    FUNCTION = "pick"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return _generate_switch_input(cls._switch_type, _NOT_A_VALUE_F)
+
+    def check_lazy_status(self, *args, **kwargs):
+        return _check_big_switch_lazy_status(*args, **kwargs)
+
+    def pick(self, select, **args):
+        return (args.get("input_" + str(select), None),)
+
+
+class DreamLazyIntSwitch:
+    _switch_type = "INT"
+    NODE_NAME = "Lazy Int Switch"
+    ICON = "⭆"
+    CATEGORY = NodeCategories.UTILS_SWITCHES
+    RETURN_TYPES = (_switch_type,)
+    RETURN_NAMES = ("selected",)
+    FUNCTION = "pick"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return _generate_switch_input(cls._switch_type, _NOT_A_VALUE_I)
+
+    def check_lazy_status(self, *args, **kwargs):
+        return _check_big_switch_lazy_status(*args, **kwargs)
+
+    def pick(self, select, **args):
+        return (args.get("input_" + str(select), None),)

loaders.py

@@ -1,7 +1,8 @@
 # -*- coding: utf-8 -*-
 from .categories import NodeCategories
-from .shared import ALWAYS_CHANGED_FLAG, list_images_in_directory, DreamImage
-from .types import SharedTypes, FrameCounter
+from .shared import list_images_in_directory, DreamImage
+from .dreamtypes import SharedTypes, FrameCounter
+import os
 
 
 class DreamImageSequenceInputWithDefaultFallback:
@@ -22,13 +23,13 @@ class DreamImageSequenceInputWithDefaultFallback:
         }
 
     CATEGORY = NodeCategories.IMAGE_ANIMATION
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("image",)
+    RETURN_TYPES = ("IMAGE","STRING")
+    RETURN_NAMES = ("image","frame_name")
     FUNCTION = "result"
 
     @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
+    def IS_CHANGED(cls, *values, **kwargs):
+        return float("NaN")
 
     def result(self, frame_counter: FrameCounter, directory_path, pattern, indexing, **other):
         default_image = other.get("default_image", None)
@@ -37,5 +38,6 @@ class DreamImageSequenceInputWithDefaultFallback:
         if not entry:
             return (default_image, "")
         else:
+            image_names = [os.path.basename(file_path) for file_path in entry]
             images = map(lambda f: DreamImage(file_path=f), entry)
-            return (DreamImage.join_to_tensor_data(images),)
+            return (DreamImage.join_to_tensor_data(images), image_names[0]) 

node_list.json

@@ -19,11 +19,13 @@
   "File Count [Dream]": "Finds the number of files in a directory matching specified patterns",
   "Finalize Prompt [Dream]": "Used in conjunction with 'Build Prompt'",
   "Float Input [Dream]": "Float input (until primitive routing issues are solved)",
+  "Float to Log Entry [Dream]": "Logging for float values",
   "Frame Count Calculator [Dream]": "Simple utility to calculate number of frames based on duration and framerate",
   "Frame Counter (Directory) [Dream]": "Directory backed frame counter, for output directories",
   "Frame Counter (Simple) [Dream]": "Integer value used as frame counter",
   "Frame Counter Info [Dream]": "Extracts information from the frame counter",
   "Frame Counter Offset [Dream]": "Adds an offset to a frame counter",
+  "Frame Counter Time Offset [Dream]": "Adds an offset to a frame counter in seconds",
   "Image Brightness Adjustment [Dream]": "Adjusts the brightness of an image by a factor",
   "Image Color Shift [Dream]": "Adjust the colors (or brightness) of an image",
   "Image Contrast Adjustment [Dream]": "Adjusts the contrast of an image by a factor",
@@ -33,19 +35,26 @@
   "Image Sequence Saver [Dream]": "Saves a frame to a directory",
   "Image Sequence Tweening [Dream]": "Post processing for animation sequences generating blended in-between frames",
   "Int Input [Dream]": "Integer input (until primitive routing issues are solved)",
+  "Int to Log Entry [Dream]": "Logging for int values",
+  "Laboratory [Dream]": "Super-charged number generator for experimenting with ComfyUI",
   "Linear Curve [Dream]": "Linear interpolation between two value over the full animation",
+  "Log Entry Joiner [Dream]": "Merges multiple log entries (reduces noodling)",
+  "Log File [Dream]": "Logging node for output to file",
   "Noise from Area Palettes [Dream]": "Generates noise based on the colors of up to nine different palettes",
   "Noise from Palette [Dream]": "Generates noise based on the colors in a palette",
   "Palette Color Align [Dream]": "Shifts the colors of one palette towards another target palette",
   "Palette Color Shift [Dream]": "Multiplies the color values in a palette",
+  "Random Prompt Words [Dream]": "Picks random words from input",
   "Sample Image Area as Palette [Dream]": "Samples a palette from an image based on pre-defined areas",
   "Sample Image as Palette [Dream]": "Randomly samples pixel values to build a palette from an image",
   "Saw Curve [Dream]": "Saw wave curve",
   "Sine Curve [Dream]": "Simple sine wave curve",
   "Smooth Event Curve [Dream]": "Single event/peak curve with a slight bell-shape",
   "String Input [Dream]": "String input (until primitive routing issues are solved)",
+  "String Tokenizer [Dream]": "Extract individual words or phrases from a text as tokens",
+  "String to Log Entry [Dream]": "Use any string as a log entry",
   "Text Input [Dream]": "Multiline string input (until primitive routing issues are solved)",
   "Triangle Curve [Dream]": "Triangle wave curve",
   "Triangle Event Curve [Dream]": "Single event/peak curve with triangular shape",
-  "Video Encoder (mpegCoder) [Dream]": "Post processing for animation sequences using mpegCoder module to generate video file"
+  "WAV Curve [Dream]": "WAV audio file as a curve"
 }

noise.py

@@ -3,7 +3,7 @@ import math
 
 from .categories import NodeCategories
 from .shared import *
-from .types import *
+from .dreamtypes import *
 
 
 def _generate_noise(image: DreamImage, color_function, rng: random.Random, block_size, blur_amount,
@@ -44,10 +44,6 @@ class DreamNoiseFromPalette:
     RETURN_NAMES = ("image",)
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def result(self, palette: Tuple[RGBPalette], width, height, seed, blur_amount, density):
         outputs = list()
         rng = random.Random()
@@ -95,10 +91,6 @@ class DreamNoiseFromAreaPalettes:
     RETURN_NAMES = ("image",)
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def _area_coordinates(self, width, height):
         dx = width / 6
         dy = height / 6

output.py

@@ -6,9 +6,9 @@ import folder_paths as comfy_paths
 from PIL.PngImagePlugin import PngInfo
 
 from .categories import NodeCategories
-from .shared import hashed_as_strings, DreamImageProcessor, DreamImage, \
+from .shared import DreamImageProcessor, DreamImage, \
     list_images_in_directory, DreamConfig
-from .types import SharedTypes, FrameCounter, AnimationSequence
+from .dreamtypes import SharedTypes, FrameCounter, AnimationSequence, LogEntry
 
 CONFIG = DreamConfig()
 
@@ -42,7 +42,7 @@ class DreamImageSequenceOutput:
                 "directory_path": ("STRING", {"default": comfy_paths.output_directory, "multiline": False}),
                 "prefix": ("STRING", {"default": 'frame', "multiline": False}),
                 "digits": ("INT", {"default": 5}),
-                "at_end": (["stop output", "keep going"],),
+                "at_end": (["stop output", "raise error", "keep going"],),
                 "filetype": (['png with embedded workflow', "png", 'jpg'],),
             },
             "hidden": {
@@ -52,23 +52,24 @@ class DreamImageSequenceOutput:
         }
 
     CATEGORY = NodeCategories.IMAGE_ANIMATION
-    RETURN_TYPES = (AnimationSequence.ID,)
+    RETURN_TYPES = (AnimationSequence.ID, LogEntry.ID)
     OUTPUT_NODE = True
-    RETURN_NAMES = ("sequence",)
+    RETURN_NAMES = ("sequence", "log_entry")
     FUNCTION = "save"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def _get_new_filename(self, current_frame, prefix, digits, filetype):
         return prefix + "_" + str(current_frame).zfill(digits) + "." + filetype.split(" ")[0]
 
-    def _save_single_image(self, dream_image: DreamImage, batch_counter, frame_counter: FrameCounter, directory_path,
-                           prefix, digits, filetype, prompt, extra_pnginfo, at_end):
+    def _save_single_image(self, dream_image: DreamImage, batch_counter, frame_counter: FrameCounter,
+                           directory_path,
+                           prefix, digits, filetype, prompt, extra_pnginfo, at_end, logger):
+
         if at_end == "stop output" and frame_counter.is_after_last_frame:
-            print("Reached end of animation - not saving output!")
+            logger("Reached end of animation - not saving output!")
             return ()
+        if at_end == "raise error" and frame_counter.is_after_last_frame:
+            logger("Reached end of animation - raising error to stop processing!")
+            raise Exception("Reached end of animation!")
         filename = self._get_new_filename(frame_counter.current_frame, prefix, digits, filetype)
         if batch_counter >= 0:
             filepath = os.path.join(directory_path, "batch_" + (str(batch_counter).zfill(4)), filename)
@@ -81,7 +82,7 @@ class DreamImageSequenceOutput:
             dream_image.save_png(filepath, filetype == 'png with embedded workflow', prompt, extra_pnginfo)
         elif filetype == "jpg":
             dream_image.save_jpg(filepath, int(CONFIG.get("encoding.jpeg_quality", 95)))
-        print("Saved {} in {}".format(filename, os.path.abspath(save_dir)))
+        logger("Saved {} in {}".format(filename, os.path.abspath(save_dir)))
         return ()
 
     def _generate_animation_sequence(self, filetype, directory_path, frame_counter):
@@ -93,13 +94,19 @@ class DreamImageSequenceOutput:
         return AnimationSequence(frame_counter, frames)
 
     def save(self, image, **args):
+        log_texts = list()
+        logger = lambda s: log_texts.append(s)
         if not args.get("directory_path", ""):
             args["directory_path"] = comfy_paths.output_directory
+        args["logger"] = logger
         proc = DreamImageProcessor(image, **args)
         proc.process(self._save_single_image)
-        frame_counter: FrameCounter = args["frame_counter"]
+        frame_counter = args["frame_counter"]
+        log_entry = LogEntry([])
+        for text in log_texts:
+            log_entry = log_entry.add(text)
         if frame_counter.is_final_frame:
             return (self._generate_animation_sequence(args["filetype"], args["directory_path"],
-                                                      frame_counter),)
+                                                      frame_counter), log_entry)
         else:
-            return (AnimationSequence(frame_counter),)
+            return (AnimationSequence(frame_counter), log_entry)

prompting.py

@@ -1,6 +1,44 @@
 from .categories import NodeCategories
-from .shared import hashed_as_strings
-from .types import PartialPrompt
+from .dreamtypes import PartialPrompt
+import random
+
+class DreamRandomPromptWords:
+    NODE_NAME = "Random Prompt Words"
+    ICON = "⚅"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "partial_prompt": (PartialPrompt.ID,)
+            },
+            "required": {
+                "words": ("STRING", {"default": "", "multiline": True}),
+                "separator": ("STRING", {"default": ",", "multiline": False}),
+                "samples": ("INT", {"default": 1, "min": 1, "max": 100}),
+                "min_weight": ("FLOAT", {"default": 1.0, "min": -10, "max": 10}),
+                "max_weight": ("FLOAT", {"default": 1.0, "min": -10, "max": 10}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+        }
+
+    CATEGORY = NodeCategories.CONDITIONING
+    RETURN_TYPES = (PartialPrompt.ID,)
+    RETURN_NAMES = ("partial_prompt",)
+    FUNCTION = "result"
+
+    def result(self, words: str, separator, samples, min_weight, max_weight, seed, **args):
+        p = args.get("partial_prompt", PartialPrompt())
+        rnd = random.Random()
+        rnd.seed(seed)
+        words = list(set(map(lambda s: s.strip(), filter(lambda s: s.strip() != "", words.split(separator)))))
+        samples = min(samples, len(words))
+        for i in range(samples):
+            picked_word = words[rnd.randint(0, len(words)-1)]
+            words = list(filter(lambda s: s!=picked_word, words))
+            weight = rnd.uniform(min_weight, max_weight)
+            p = p.add(picked_word, weight)
+        return (p,)
 
 
 class DreamWeightedPromptBuilder:
@@ -24,10 +62,6 @@ class DreamWeightedPromptBuilder:
     RETURN_NAMES = ("partial_prompt",)
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def result(self, added_prompt, weight, **args):
         input = args.get("partial_prompt", PartialPrompt())
         p = input.add(added_prompt, weight)
@@ -54,9 +88,6 @@ class DreamPromptFinalizer:
     RETURN_NAMES = ("positive", "negative")
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
 
     def result(self, partial_prompt: PartialPrompt, adjustment, adjustment_reference, clamp):
         if adjustment == "raw" or partial_prompt.is_empty():

pyproject.toml

@@ -0,0 +1,15 @@
+[project]
+name = "comfyui-dream-project"
+description = "This extension offers various nodes that are useful for Deforum-like animations in ComfyUI."
+version = "5.1.2"
+license = { text = "MIT License" }
+dependencies = ["imageio", "pilgram", "scipy", "numpy<2.0,>=1.18", "torchvision", "evalidate"]
+
+[project.urls]
+Repository = "https://github.com/alt-key-project/comfyui-dream-project"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "altkeyproject"
+DisplayName = "comfyui-dream-project"
+Icon = ""

readme.md

@@ -7,6 +7,17 @@ and useful to many ComfyUI users.
 
 I have demonstrated the use of these custom nodes in this [youtube video](https://youtu.be/pZ6Li3qF-Kk).
 
+# Notice!
+
+This custom node pack is currently not being updated. Stable Diffusion video generation is moving towards a different 
+workflow with AnimateDiff and Stable Video Diffusion. I decided to not try to update this node pack, but I am instead 
+creating a separate custom node pack here:
+
+[github](https://github.com/alt-key-project/comfyui-dream-video-batches)
+
+This new node pack will be getting my attention from now on (at least as long as stable diffusion video generation is done mostly 
+in batches).
+
 ## Installation
 
 ### Simple option
@@ -163,12 +174,12 @@ Utility for calculating good width/height based on common video dimensions.
 ### Video Encoder (FFMPEG) [Dream]
 Post processing for animation sequences calling FFMPEG to generate video files.
 
-### Video Encoder (mpegCoder) [Dream]
-Post processing for animation sequences using the python module mpegCoder with ffmpeg library to generate video files.
-
 ### File Count [Dream]
 Finds the number of files in a directory matching specified patterns.
 
+### Float/Int/string to Log Entry [Dream]
+Logging for float/int/string values.
+
 ### Frame Count Calculator [Dream]
 Simple utility to calculate number of frames based on time and framerate.
 
@@ -183,7 +194,10 @@ counter.
 Extracts information from the frame counter.
 
 ### Frame Counter Offset [Dream]
-Adds an offset to a frame counter.
+Adds an offset (in frames) to a frame counter.
+
+### Frame Counter Time Offset [Dream]
+Adds an offset in seconds to a frame counter.
 
 ### Image Brightness Adjustment [Dream]
 Adjusts the brightness of an image by a factor.
@@ -209,6 +223,19 @@ Saves a frame to a directory.
 ### Image Sequence Tweening [Dream]
 Post processing for animation sequences generating blended in-between frames.
 
+### Laboratory [Dream]
+Super-charged number generator for experimenting with ComfyUI.
+
+### Lazy *** Switch [Dream]
+Switch nodes for different type for up to ten inputs. The lazy version only evaluates the 
+selected input, but first/next of the big switch is unsupported.
+
+### Log Entry Joiner [Dream]
+Merges multiple log entries (reduces noodling).
+
+### Log File [Dream]
+The text logging facility for the Dream Project Animation nodes.
+
 ### Linear Curve [Dream]
 Linear interpolation between two values over the full animation.
 
@@ -243,12 +270,21 @@ Simple sine wave curve.
 ### Smooth Event Curve [Dream]
 Single event/peak curve with a slight bell-shape. 
 
+### String Tokenizer [Dream]
+Splits a text into tokens by a separator and returns one of the tokens based on a given index.
+
+### Random Prompt Words [Dream]
+Randomly picks words/tokens/phrases from an input text.
+
 ### Triangle Curve [Dream]
 Triangle wave curve. 
 
 ### Triangle Event Curve [Dream]
 Single event/peak curve with triangular shape.
 
+### WAV Curve [Dream]
+Use an uncompressed WAV audio file as a curve.
+
 ### Other custom nodes
 
 Many of the nodes found in 'WAS Node Suite' are useful the Dream Project Animation nodes - I suggest you install those 
@@ -274,6 +310,18 @@ This flow demonstrates sampling image areas into palettes and generating noise f
 
 [area-sampled-noise](examples/area-sampled-noise.json)
 
+### Prompt Morphing
+
+This flow demonstrates prompt building with weights based on curves and brightness and contrast control.
+
+[prompt-morphing](examples/prompt-morphing.json)
+
+### Laboratory
+
+This flow demonstrates use of the Laboratory and Logging nodes.
+
+[laboratory](examples/laboratory.json)
+
 ## Known issues
 
 ### FFMPEG

requirements.txt

@@ -1,7 +1,6 @@
 imageio
 pilgram
 scipy
-numpy<1.24>=1.18
+numpy<2.0,>=1.18
 torchvision
-mpegCoder
 evalidate

seq_processing.py

@@ -4,11 +4,14 @@ import shutil
 import subprocess
 import tempfile
 from functools import lru_cache
+
 from PIL import Image as PilImage
+
 from .categories import NodeCategories
 from .err import on_error
-from .shared import DreamConfig, MpegEncoderUtility
-from .types import *
+from .shared import DreamConfig
+#from .shared import MpegEncoderUtility
+from .dreamtypes import *
 
 CONFIG = DreamConfig()
 
@@ -129,65 +132,67 @@ def _make_video_filename(name, file_ext):
     (b, _) = os.path.splitext(name)
     return b + "." + file_ext.strip(".")
 
-
-class DreamVideoEncoderMpegCoder:
-    NODE_NAME = "Video Encoder (mpegCoder)"
-    ICON = "🎬"
-    CATEGORY = NodeCategories.ANIMATION_POSTPROCESSING
-    RETURN_TYPES = ()
-    RETURN_NAMES = ()
-    OUTPUT_NODE = True
-    FUNCTION = "encode"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": SharedTypes.sequence | {
-                "name": ("STRING", {"default": 'video', "multiline": False}),
-                "framerate_factor": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0}),
-                "remove_images": ("BOOLEAN", {"default": True})
-            },
-        }
-
-    def _find_free_filename(self, filename, defaultdir):
-        if os.path.basename(filename) == filename:
-            filename = os.path.join(defaultdir, filename)
-        n = 1
-        tested = filename
-        while os.path.exists(tested):
-            n += 1
-            (b, ext) = os.path.splitext(filename)
-            tested = b + "_" + str(n) + ext
-        return tested
-
-    def encode(self, sequence, name, framerate_factor, remove_images):
-        if not sequence.is_defined:
-            return ()
-        config = DreamConfig()
-        filename = _make_video_filename(name, config.get("mpeg_coder.file_extension", "mp4"))
-        for batch_num in sequence.batches:
-            try:
-                images = list(sequence.get_image_files_of_batch(batch_num))
-                filename = self._find_free_filename(filename, os.path.dirname(images[0]))
-                first_image = DreamImage.from_file(images[0])
-                enc = MpegEncoderUtility(video_path=filename,
-                                         bit_rate_factor=float(config.get("mpeg_coder.bitrate_factor", 1.0)),
-                                         encoding_threads=int(config.get("mpeg_coder.encoding_threads", 4)),
-                                         max_b_frame=int(config.get("mpeg_coder.max_b_frame", 2)),
-                                         width=first_image.width,
-                                         height=first_image.height,
-                                         files=images,
-                                         fps=sequence.fps * framerate_factor,
-                                         codec_name=config.get("mpeg_coder.codec_name", "libx265"))
-                enc.encode()
-                if remove_images:
-                    for imagepath in images:
-                        if os.path.isfile(imagepath):
-                            os.unlink(imagepath)
-            except Exception as e:
-                on_error(self.__class__, str(e))
-        return ()
-
+#
+# class DreamVideoEncoderMpegCoder:
+#     NODE_NAME = "Video Encoder (mpegCoder)"
+#     ICON = "🎬"
+#     CATEGORY = NodeCategories.ANIMATION_POSTPROCESSING
+#     RETURN_TYPES = (LogEntry.ID,)
+#     RETURN_NAMES = ("log_entry",)
+#     OUTPUT_NODE = True
+#     FUNCTION = "encode"
+#
+#     @classmethod
+#     def INPUT_TYPES(cls):
+#         return {
+#             "required": SharedTypes.sequence | {
+#                 "name": ("STRING", {"default": 'video', "multiline": False}),
+#                 "framerate_factor": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0}),
+#                 "remove_images": ("BOOLEAN", {"default": True})
+#             },
+#         }
+#
+#     def _find_free_filename(self, filename, defaultdir):
+#         if os.path.basename(filename) == filename:
+#             filename = os.path.join(defaultdir, filename)
+#         n = 1
+#         tested = filename
+#         while os.path.exists(tested):
+#             n += 1
+#             (b, ext) = os.path.splitext(filename)
+#             tested = b + "_" + str(n) + ext
+#         return tested
+#
+#     def encode(self, sequence, name, framerate_factor, remove_images):
+#         if not sequence.is_defined:
+#             return (LogEntry([]),)
+#         config = DreamConfig()
+#         filename = _make_video_filename(name, config.get("mpeg_coder.file_extension", "mp4"))
+#         log_entry = LogEntry([])
+#         for batch_num in sequence.batches:
+#             try:
+#                 images = list(sequence.get_image_files_of_batch(batch_num))
+#                 filename = self._find_free_filename(filename, os.path.dirname(images[0]))
+#                 first_image = DreamImage.from_file(images[0])
+#                 enc = MpegEncoderUtility(video_path=filename,
+#                                          bit_rate_factor=float(config.get("mpeg_coder.bitrate_factor", 1.0)),
+#                                          encoding_threads=int(config.get("mpeg_coder.encoding_threads", 4)),
+#                                          max_b_frame=int(config.get("mpeg_coder.max_b_frame", 2)),
+#                                          width=first_image.width,
+#                                          height=first_image.height,
+#                                          files=images,
+#                                          fps=sequence.fps * framerate_factor,
+#                                          codec_name=config.get("mpeg_coder.codec_name", "libx265"))
+#                 enc.encode()
+#                 log_entry = log_entry.add("Generated video '{}'".format(filename))
+#                 if remove_images:
+#                     for imagepath in images:
+#                         if os.path.isfile(imagepath):
+#                             os.unlink(imagepath)
+#             except Exception as e:
+#                 on_error(self.__class__, str(e))
+#         return (log_entry,)
+#
 
 class DreamVideoEncoder:
     NODE_NAME = "FFMPEG Video Encoder"
@@ -205,8 +210,8 @@ class DreamVideoEncoder:
         }
 
     CATEGORY = NodeCategories.ANIMATION_POSTPROCESSING
-    RETURN_TYPES = ()
-    RETURN_NAMES = ()
+    RETURN_TYPES = (LogEntry.ID,)
+    RETURN_NAMES = ("log_entry",)
     OUTPUT_NODE = True
     FUNCTION = "encode"
 
@@ -228,24 +233,28 @@ class DreamVideoEncoder:
     def generate_video(self, files, fps, filename, config):
         filename = self._find_free_filename(filename, os.path.dirname(files[0]))
         _ffmpeg(config, files, fps, filename)
+        return filename
 
     def encode(self, sequence: AnimationSequence, name: str, remove_images, framerate_factor):
         if not sequence.is_defined:
-            return ()
+            return (LogEntry([]),)
 
         config = DreamConfig()
         filename = _make_video_filename(name, config.get("ffmpeg.file_extension", "mp4"))
+        log_entry = LogEntry([])
         for batch_num in sequence.batches:
             try:
                 images = list(sequence.get_image_files_of_batch(batch_num))
-                self.generate_video(images, sequence.fps * framerate_factor, filename, config)
+                actual_filename = self.generate_video(images, sequence.fps * framerate_factor, filename, config)
+
+                log_entry = log_entry.add("Generated video '{}'".format(actual_filename))
                 if remove_images:
                     for imagepath in images:
                         if os.path.isfile(imagepath):
                             os.unlink(imagepath)
             except Exception as e:
                 on_error(self.__class__, str(e))
-        return ()
+        return (log_entry,)
 
 
 class DreamSequenceTweening:

shared.py

@@ -4,10 +4,10 @@ import hashlib
 import json
 import os
 import random
-import time
-
-import folder_paths as comfy_paths
+import tempfile
 import glob
+from io import BytesIO
+
 import numpy
 import torch
 from PIL import Image, ImageFilter, ImageEnhance
@@ -18,10 +18,11 @@ from typing import Dict, Tuple, List
 from .dreamlogger import DreamLog
 from .embedded_config import EMBEDDED_CONFIGURATION
 
+tmpDir = tempfile.TemporaryDirectory("Dream_Anim")
+
 NODE_FILE = os.path.abspath(__file__)
 DREAM_NODES_SOURCE_ROOT = os.path.dirname(NODE_FILE)
-TEMP_PATH = os.path.join(os.path.abspath(comfy_paths.temp_directory), "Dream_Anim")
-ALWAYS_CHANGED_FLAG = float("NaN")
+TEMP_PATH = os.path.join(os.path.abspath(tempfile.gettempdir()), "Dream_Anim")
 
 
 def convertTensorImageToPIL(tensor_image) -> Image:
@@ -274,7 +275,7 @@ def list_images_in_directory(directory_path: str, pattern: str, alphabetic_index
 
     def _num_from_filename(fn):
         (text, _) = os.path.splitext(fn)
-        token: str = text.split("_")[-1]
+        token = text.split("_")[-1]
         if token.isdigit():
             return int(token)
         else:
@@ -332,13 +333,13 @@ class DreamStateStore:
 
 
 class DreamStateFile:
-    def __init__(self, state_file_path=os.path.join(TEMP_PATH, "state.json")):
-        self._dirname = os.path.dirname(state_file_path)
-        self._filepath = state_file_path
+    def __init__(self, state_collection_name="state"):
+        self._filepath = os.path.join(TEMP_PATH, state_collection_name+".json")
+        self._dirname = os.path.dirname(self._filepath)
         if not os.path.isdir(self._dirname):
             os.makedirs(self._dirname)
         if not os.path.isfile(self._filepath):
-            self._data: dict = {}
+            self._data = {}
         else:
             with open(self._filepath, encoding="utf-8") as f:
                 self._data = json.load(f)
@@ -358,62 +359,33 @@ class DreamStateFile:
             self._data[key] = value
         with open(self._filepath, "w", encoding="utf-8") as f:
             json.dump(self._data, f)
-        print("* {} -> {}".format(key, value))
         return previous
 
 
-def hashed_as_strings(*items):
+def hash_tensor_data(image, hasher = None):
+    m = hashlib.sha256() if hasher is None else hasher
+    if isinstance(image, torch.Tensor):
+        buff = BytesIO()
+        torch.save(image, buff)
+        print("HASHING TENSOR")
+        m.update(buff.getvalue())
+    elif isinstance(image, bytes) or isinstance(image, bytearray):
+        print("HASHING BYTES")
+        m.update(image)
+    elif isinstance(image, list) or isinstance(image, tuple):
+        print("HASHING ITERABLE")
+        for item in image:
+            hash_tensor_data(item, m)
+    else:
+        print("HASING_AS_TEXT - "+str(type(image)))
+        m.update(str(image).encode(encoding="utf-8"))
+    return m.digest().hex()
+
+def hashed_as_strings(*items, **kwargs):
     tokens = "|".join(list(map(str, items)))
     m = hashlib.sha256()
     m.update(tokens.encode(encoding="utf-8"))
+    for pair in kwargs.items():
+        m.update(str(pair).encode(encoding="utf-8"))
     return m.digest().hex()
 
-
-class MpegEncoderUtility:
-    def __init__(self, video_path: str, bit_rate_factor: float, width: int, height: int, files: List[str],
-                 fps: float, encoding_threads: int, codec_name, max_b_frame):
-        import mpegCoder
-        self._files = files
-        self._logger = get_logger()
-        self._enc = mpegCoder.MpegEncoder()
-        bit_rate = self._calculate_bit_rate(width, height, fps, bit_rate_factor)
-        self._logger.info("Bitrate " + str(bit_rate))
-        self._enc.setParameter(
-            videoPath=video_path, codecName=codec_name,
-            nthread=encoding_threads, bitRate=bit_rate, width=width, height=height, widthSrc=width,
-            heightSrc=height,
-            GOPSize=len(files), maxBframe=max_b_frame, frameRate=self._fps_to_tuple(fps))
-
-    def _calculate_bit_rate(self, width: int, height: int, fps: float, bit_rate_factor: float):
-        bits_per_pixel_base = 0.125
-        return round(max(10, float(width * height * fps * bits_per_pixel_base * bit_rate_factor * 0.001)))
-
-    def encode(self):
-        if not self._enc.FFmpegSetup():
-            raise Exception("Failed to setup MPEG Encoder - check parameters!")
-        try:
-            t = time.time()
-
-            for filepath in self._files:
-                self._logger.debug("Encoding frame {}", filepath)
-                image = DreamImage.from_file(filepath).convert("RGB")
-                self._enc.EncodeFrame(image.numpy_array())
-            self._enc.FFmpegClose()
-            self._logger.info("Completed video encoding of {n} frames in {t} seconds", n=len(self._files),
-                              t=round(time.time() - t))
-        finally:
-            self._enc.clear()
-
-    def _fps_to_tuple(self, fps: float):
-        def _is_almost_int(f: float):
-            return abs(f - int(f)) < 0.001
-
-        a = fps
-        b = 1
-        while not _is_almost_int(a) and b < 100:
-            a /= 10
-            b *= 10
-        a = round(a)
-        b = round(b)
-        self._logger.info("Video specified as {fps} fps - encoder framerate {a}/{b}", fps=fps, a=a, b=b)
-        return (a, b)

switches.py

@@ -1,30 +1,40 @@
 from .categories import NodeCategories
+from .dreamtypes import RGBPalette
 from .err import *
-from .shared import ALWAYS_CHANGED_FLAG, hashed_as_strings
-from .types import RGBPalette
+from .shared import hashed_as_strings, hash_tensor_data
 
+_NOT_A_VALUE_I = 9223372036854775807
+_NOT_A_VALUE_F = float(_NOT_A_VALUE_I)
+_NOT_A_VALUE_S = "⭆"
 
-def _generate_switch_input(type: str):
+def _generate_switch_input(type_nm: str, default_value=None):
     d = dict()
     for i in range(10):
-        d["input_" + str(i)] = (type,)
+        if default_value is None:
+            d["input_" + str(i)] = (type_nm,)
+        else:
+            d["input_" + str(i)] = (type_nm, {"default": default_value, "forceInput": True})
+
     return {
         "required": {
-            "select": ("INT", {"defualt": 0, "min": 0, "max": 9}),
+            "select": ("INT", {"default": 0, "min": 0, "max": 9}),
             "on_missing": (["previous", "next"],)
         },
         "optional": d
     }
 
-
-def _do_pick(cls, select, on_missing, **args):
+def _do_pick(cls, select, test_val, on_missing, **args):
     direction = 1
     if on_missing == "previous":
         direction = -1
     if len(args) == 0:
         on_error(cls, "No inputs provided!")
-    while args.get("input_" + str(select), None) is None:
+    n = len(args)
+    while not test_val(args.get("input_" + str(select), None)):
+        if n<0:
+            return (None,)
         select = (select + direction) % 10
+        n = n - 1
     return args["input_" + str(select)],
 
 
@@ -41,12 +51,8 @@ class DreamBigImageSwitch:
     def INPUT_TYPES(cls):
         return _generate_switch_input(cls._switch_type)
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def pick(self, select, on_missing, **args):
-        return _do_pick(self.__class__, select, on_missing, **args)
+        return _do_pick(self.__class__, select, lambda n: n is not None, on_missing, **args)
 
 
 class DreamBigLatentSwitch:
@@ -62,12 +68,8 @@ class DreamBigLatentSwitch:
     def INPUT_TYPES(cls):
         return _generate_switch_input(cls._switch_type)
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def pick(self, select, on_missing, **args):
-        return _do_pick(self.__class__, select, on_missing, **args)
+        return _do_pick(self.__class__, select, lambda n: n is not None, on_missing, **args)
 
 
 class DreamBigTextSwitch:
@@ -81,14 +83,10 @@ class DreamBigTextSwitch:
 
     @classmethod
     def INPUT_TYPES(cls):
-        return _generate_switch_input(cls._switch_type)
-
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(values)
+        return _generate_switch_input(cls._switch_type, _NOT_A_VALUE_S)
 
     def pick(self, select, on_missing, **args):
-        return _do_pick(self.__class__, select, on_missing, **args)
+        return _do_pick(self.__class__, select, lambda n: (n is not None) and (n != _NOT_A_VALUE_S), on_missing, **args)
 
 
 class DreamBigPaletteSwitch:
@@ -104,12 +102,8 @@ class DreamBigPaletteSwitch:
     def INPUT_TYPES(cls):
         return _generate_switch_input(cls._switch_type)
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return ALWAYS_CHANGED_FLAG
-
     def pick(self, select, on_missing, **args):
-        return _do_pick(self.__class__, select, on_missing, **args)
+        return _do_pick(self.__class__, select, lambda n: (n is not None), on_missing, **args)
 
 
 class DreamBigFloatSwitch:
@@ -123,14 +117,10 @@ class DreamBigFloatSwitch:
 
     @classmethod
     def INPUT_TYPES(cls):
-        return _generate_switch_input(cls._switch_type)
-
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(values)
+        return _generate_switch_input(cls._switch_type, _NOT_A_VALUE_F)
 
     def pick(self, select, on_missing, **args):
-        return _do_pick(self.__class__, select, on_missing, **args)
+        return _do_pick(self.__class__, select, lambda n: (n is not None) and (n != _NOT_A_VALUE_F), on_missing, **args)
 
 
 class DreamBigIntSwitch:
@@ -144,14 +134,10 @@ class DreamBigIntSwitch:
 
     @classmethod
     def INPUT_TYPES(cls):
-        return _generate_switch_input(cls._switch_type)
-
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(values)
+        return _generate_switch_input(cls._switch_type, _NOT_A_VALUE_I)
 
     def pick(self, select, on_missing, **args):
-        return _do_pick(self.__class__, select, on_missing, **args)
+        return _do_pick(self.__class__, select, lambda n: (n is not None) and (n != _NOT_A_VALUE_I), on_missing, **args)
 
 
 class DreamBoolToFloat:
@@ -172,10 +158,6 @@ class DreamBoolToFloat:
             }
         }
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(values)
-
     def pick(self, boolean, on_true, on_false):
         if boolean:
             return (on_true,)
@@ -201,10 +183,6 @@ class DreamBoolToInt:
             }
         }
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(values)
-
     def pick(self, boolean, on_true, on_false):
         if boolean:
             return (on_true,)

utility.py

@@ -1,8 +1,220 @@
 # -*- coding: utf-8 -*-
+import datetime
 import math
+import os
+
+import folder_paths as comfy_paths
 
 from .categories import NodeCategories
-from .shared import hashed_as_strings
+from .shared import hashed_as_strings, DreamStateFile
+from .dreamtypes import LogEntry, SharedTypes, FrameCounter
+
+_logfile_state = DreamStateFile("logging")
+
+
+class DreamJoinLog:
+    NODE_NAME = "Log Entry Joiner"
+    ICON = "🗎"
+    CATEGORY = NodeCategories.UTILS
+    RETURN_TYPES = (LogEntry.ID,)
+    RETURN_NAMES = ("log_entry",)
+    FUNCTION = "convert"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "entry_0": (LogEntry.ID,),
+                "entry_1": (LogEntry.ID,),
+                "entry_2": (LogEntry.ID,),
+                "entry_3": (LogEntry.ID,),
+            }
+        }
+
+    def convert(self, **values):
+        entry = LogEntry([])
+        for i in range(4):
+            txt = values.get("entry_" + str(i), None)
+            if txt:
+                entry = entry.merge(txt)
+        return (entry,)
+
+
+class DreamFloatToLog:
+    NODE_NAME = "Float to Log Entry"
+    ICON = "🗎"
+    CATEGORY = NodeCategories.UTILS
+    RETURN_TYPES = (LogEntry.ID,)
+    RETURN_NAMES = ("log_entry",)
+    FUNCTION = "convert"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("FLOAT", {"default": 0}),
+                "label": ("STRING", {"default": ""}),
+            },
+        }
+
+    def convert(self, label, value):
+        return (LogEntry.new(label + ": " + str(value)),)
+
+
+class DreamIntToLog:
+    NODE_NAME = "Int to Log Entry"
+    ICON = "🗎"
+    CATEGORY = NodeCategories.UTILS
+    RETURN_TYPES = (LogEntry.ID,)
+    RETURN_NAMES = ("log_entry",)
+    FUNCTION = "convert"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("INT", {"default": 0}),
+                "label": ("STRING", {"default": ""}),
+            },
+        }
+
+    def convert(self, label, value):
+        return (LogEntry.new(label + ": " + str(value)),)
+
+
+class DreamStringToLog:
+    NODE_NAME = "String to Log Entry"
+    ICON = "🗎"
+    OUTPUT_NODE = True
+    CATEGORY = NodeCategories.UTILS
+    RETURN_TYPES = (LogEntry.ID,)
+    RETURN_NAMES = ("log_entry",)
+    FUNCTION = "convert"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text": ("STRING", {"default": ""}),
+            },
+            "optional": {
+                "label": ("STRING", {"default": ""}),
+            }
+        }
+
+    def convert(self, text, **values):
+        label = values.get("label", "")
+        if label:
+            return (LogEntry.new(label + ": " + text),)
+        else:
+            return (LogEntry.new(text),)
+
+
+class DreamStringTokenizer:
+    NODE_NAME = "String Tokenizer"
+    ICON = "🪙"
+    OUTPUT_NODE = True
+    CATEGORY = NodeCategories.UTILS
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("token",)
+    FUNCTION = "exec"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text": ("STRING", {"default": "", "multiline": True}),
+                "separator": ("STRING", {"default": ","}),
+                "selected": ("INT", {"default": 0, "min": 0})
+            },
+        }
+
+    def exec(self, text: str, separator: str, selected: int):
+        if separator is None or separator == "":
+            separator = " "
+        parts = text.split(sep=separator)
+        return (parts[abs(selected) % len(parts)].strip(),)
+
+
+class DreamLogFile:
+    NODE_NAME = "Log File"
+    ICON = "🗎"
+    OUTPUT_NODE = True
+    CATEGORY = NodeCategories.UTILS
+    RETURN_TYPES = ()
+    RETURN_NAMES = ()
+    FUNCTION = "write"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": SharedTypes.frame_counter | {
+                "log_directory": ("STRING", {"default": comfy_paths.output_directory}),
+                "log_filename": ("STRING", {"default": "dreamlog.txt"}),
+                "stdout": ("BOOLEAN", {"default": True}),
+                "active": ("BOOLEAN", {"default": True}),
+                "clock_has_24_hours": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "entry_0": (LogEntry.ID,),
+                "entry_1": (LogEntry.ID,),
+                "entry_2": (LogEntry.ID,),
+                "entry_3": (LogEntry.ID,),
+                "entry_4": (LogEntry.ID,),
+                "entry_5": (LogEntry.ID,),
+                "entry_6": (LogEntry.ID,),
+                "entry_7": (LogEntry.ID,),
+            },
+        }
+
+    def _path_to_log_file(self, log_directory, logfile):
+        if os.path.isabs(logfile):
+            return os.path.normpath(os.path.abspath(logfile))
+        elif os.path.isabs(log_directory):
+            return os.path.normpath(os.path.abspath(os.path.join(log_directory, logfile)))
+        elif log_directory:
+            return os.path.normpath(os.path.abspath(os.path.join(comfy_paths.output_directory, log_directory, logfile)))
+        else:
+            return os.path.normpath(os.path.abspath(os.path.join(comfy_paths.output_directory, logfile)))
+
+    def _get_tm_format(self, clock_has_24_hours):
+        if clock_has_24_hours:
+            return "%a %H:%M:%S"
+        else:
+            return "%a %I:%M:%S %p"
+
+    def write(self, frame_counter: FrameCounter, log_directory, log_filename, stdout, active, clock_has_24_hours,
+              **entries):
+        if not active:
+            return ()
+        log_entry = None
+        for i in range(8):
+            e = entries.get("entry_" + str(i), None)
+            if e is not None:
+                if log_entry is None:
+                    log_entry = e
+                else:
+                    log_entry = log_entry.merge(e)
+        log_file_path = self._path_to_log_file(log_directory, log_filename)
+        ts = _logfile_state.get_section("timestamps").get(log_file_path, 0)
+        output_text = list()
+        last_t = 0
+        for (t, text) in log_entry.get_filtered_entries(ts):
+            dt = datetime.datetime.fromtimestamp(t)
+            output_text.append("[frame {}/{} (~{}%), timestamp {}]\n{}".format(frame_counter.current_frame + 1,
+                                                                               frame_counter.total_frames,
+                                                                               round(frame_counter.progress * 100),
+                                                                               dt.strftime(self._get_tm_format(
+                                                                                   clock_has_24_hours)), text.rstrip()))
+            output_text.append("---")
+            last_t = max(t, last_t)
+        output_text = "\n".join(output_text) + "\n"
+        if stdout:
+            print(output_text)
+        with open(log_file_path, "a", encoding="utf-8") as f:
+            f.write(output_text)
+        _logfile_state.get_section("timestamps").update(log_file_path, 0, lambda _: last_t)
+        return ()
 
 
 def _align_num(n: int, alignment: int, type: str):
@@ -38,10 +250,6 @@ class DreamFrameDimensions:
     RETURN_NAMES = ("width", "height", "final_width", "final_height")
     FUNCTION = "result"
 
-    @classmethod
-    def IS_CHANGED(cls, *values):
-        return hashed_as_strings(*values)
-
     def result(self, size, aspect_ratio, orientation, divisor, alignment, alignment_type):
         ratio = tuple(map(int, aspect_ratio.split(":")))
         final_width = int(size)
@@ -52,4 +260,3 @@ class DreamFrameDimensions:
             return (width, height, final_width, final_height)
         else:
             return (height, width, final_height, final_width)
-