3.1 - curves, calculations, prompt building and switches.

__init__.py

@@ -1,27 +1,37 @@
-from typing import List, Type
+# -*- coding: utf-8 -*-
+from typing import Type
 
-from .seq_processing import *
 from .base import *
-from .curves import *
-from .loaders import *
-from .output import *
-from .utility import *
 from .colors import *
-from .noise import *
+from .curves import *
 from .image_processing import *
+from .inputfields import *
+from .loaders import *
+from .noise import *
+from .output import *
+from .prompting import *
+from .seq_processing import *
+from .switches import *
+from .utility import *
+from .calculate import *
 
 _NODE_CLASSES: List[Type] = [DreamSineWave, DreamLinear, DreamCSVCurve, DreamBeatCurve, DreamFrameDimensions,
-                             DreamImageMotion,
+                             DreamImageMotion, DreamNoiseFromPalette, DreamAnalyzePalette, DreamColorShift,
                              DreamDirectoryFileCount, DreamFrameCounterOffset, DreamDirectoryBackedFrameCounter,
                              DreamSimpleFrameCounter, DreamImageSequenceInputWithDefaultFallback,
-                             DreamImageSequenceOutput, DreamCSVGenerator,
+                             DreamImageSequenceOutput, DreamCSVGenerator, DreamImageAreaSampler,
                              DreamVideoEncoder, DreamSequenceTweening, DreamSequenceBlend, DreamColorAlign,
-                             DreamImageSampler, DreamNoiseFromPalette, DreamAnalyzePalette, DreamColorShift]
+                             DreamImageSampler, DreamNoiseFromAreaPalettes, DreamVideoEncoderMpegCoder,
+                             DreamInputString, DreamInputFloat, DreamInputInt, DreamInputText, DreamBigLatentSwitch,
+                             DreamFrameCountCalculator, DreamBigImageSwitch, DreamBigTextSwitch, DreamBigFloatSwitch,
+                             DreamBigIntSwitch, DreamBigPaletteSwitch, DreamWeightedPromptBuilder, DreamPromptFinalizer,
+                             DreamFrameCounterInfo, DreamBoolToFloat, DreamBoolToInt, DreamSawWave, DreamTriangleWave,
+                             DreamTriangleEvent, DreamSmoothEvent, DreamCalculation]
 _SIGNATURE_SUFFIX = " [Dream]"
 
 MANIFEST = {
     "name": "Dream Project Animation",
-    "version": (1, 1, 0),
+    "version": (3, 1, 0),
     "author": "Dream Project",
     "project": "https://github.com/alt-key-project/comfyui-dream-project",
     "description": "Various utility nodes for creating animations with ComfyUI",
@@ -31,13 +41,47 @@ NODE_CLASS_MAPPINGS = {}
 
 NODE_DISPLAY_NAME_MAPPINGS = {}
 
+config = DreamConfig()
+
+
+def update_category(cls):
+    top = config.get("ui.top_category", "").strip().strip("/")
+    leaf_icon = ""
+    if top and "CATEGORY" in cls.__dict__:
+        cls.CATEGORY = top + "/" + cls.CATEGORY.lstrip("/")
+    if "CATEGORY" in cls.__dict__:
+        joined = []
+        for partial in cls.CATEGORY.split("/"):
+            icon = config.get("ui.category_icons." + partial, "")
+            if icon:
+                leaf_icon = icon
+            if config.get("ui.prepend_icon_to_category", False):
+                partial = icon.lstrip() + " " + partial
+            if config.get("ui.append_icon_to_category", False):
+                partial = partial + " " + icon.rstrip()
+            joined.append(partial)
+        cls.CATEGORY = "/".join(joined)
+    return leaf_icon
+
+
+def update_display_name(cls, category_icon, display_name):
+    icon = cls.__dict__.get("ICON", category_icon)
+    if config.get("ui.prepend_icon_to_node", False):
+        display_name = icon.lstrip() + " " + display_name
+    if config.get("ui.append_icon_to_node", False):
+        display_name = display_name + " " + icon.rstrip()
+    return display_name
+
+
 for cls in _NODE_CLASSES:
+    category_icon = update_category(cls)
     clsname = cls.__name__
     if "NODE_NAME" in cls.__dict__:
         node_name = cls.__dict__["NODE_NAME"] + _SIGNATURE_SUFFIX
         NODE_CLASS_MAPPINGS[node_name] = cls
-        display_name = cls.__dict__.get("DISPLAY_NAME", cls.__dict__["NODE_NAME"]) + _SIGNATURE_SUFFIX
-        NODE_DISPLAY_NAME_MAPPINGS[node_name] = display_name
+        NODE_DISPLAY_NAME_MAPPINGS[node_name] = update_display_name(cls, category_icon,
+                                                                    cls.__dict__.get("DISPLAY_NAME",
+                                                                                     cls.__dict__["NODE_NAME"]))
     else:
         raise Exception("Class {} is missing NODE_NAME!".format(str(cls)))
 

base.py

@@ -1,11 +1,45 @@
+# -*- coding: utf-8 -*-
+import glob
+
 from .categories import NodeCategories
 from .shared import *
 from .types import *
-import glob
+
+
+class DreamFrameCounterInfo:
+    NODE_NAME = "Frame Counter Info"
+    ICON = "⚋"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": SharedTypes.frame_counter
+        }
+
+    CATEGORY = NodeCategories.ANIMATION
+    RETURN_TYPES = ("INT", "INT", "BOOLEAN", "BOOLEAN", "FLOAT", "FLOAT", "FLOAT", "FLOAT")
+    RETURN_NAMES = ("frames_completed", "total_frames", "first_frame", "last_frame",
+                    "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,
+                frame_counter.is_first_frame,
+                frame_counter.is_final_frame,
+                frame_counter.current_time_in_seconds,
+                frame_counter.remaining_time_in_seconds,
+                frame_counter.total_time_in_seconds,
+                frame_counter.current_time_in_seconds / max(0.01, frame_counter.total_time_in_seconds))
 
 
 class DreamDirectoryFileCount:
     NODE_NAME = "File Count"
+    ICON = "📂"
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -25,18 +59,22 @@ class DreamDirectoryFileCount:
     def IS_CHANGED(cls, *v):
         return ALWAYS_CHANGED_FLAG
 
-    def result(self, directory_path, patterns, indexing):
+    def result(self, directory_path, patterns):
         if not os.path.isdir(directory_path):
             return (0,)
         total = 0
         for pattern in patterns.split("|"):
-            total += len(glob.glob(pattern))
+            files = list(glob.glob(pattern, root_dir=directory_path))
+            total += len(files)
+        print("total " + str(total))
         return (total,)
 
 
 class DreamFrameCounterOffset:
     NODE_NAME = "Frame Counter Offset"
 
+    ICON = "±"
+
     @classmethod
     def INPUT_TYPES(cls):
         return {
@@ -60,6 +98,7 @@ class DreamFrameCounterOffset:
 
 class DreamSimpleFrameCounter:
     NODE_NAME = "Frame Counter (Simple)"
+    ICON = "⚋"
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -87,6 +126,7 @@ class DreamSimpleFrameCounter:
 
 class DreamDirectoryBackedFrameCounter:
     NODE_NAME = "Frame Counter (Directory)"
+    ICON = "⚋"
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -96,7 +136,7 @@ class DreamDirectoryBackedFrameCounter:
                 "pattern": ("STRING", {"default": '*', "multiline": False}),
                 "indexing": (["numeric", "alphabetic order"],),
                 "total_frames": ("INT", {"default": 100, "min": 2, "max": 24 * 3600 * 60}),
-                "frames_per_second": ("INT", {"min": 1, "default": 25}),
+                "frames_per_second": ("INT", {"min": 1, "default": 30}),
             },
         }
 
@@ -115,3 +155,33 @@ class DreamDirectoryBackedFrameCounter:
             return (FrameCounter(0, total_frames, frames_per_second),)
         n = max(results.keys()) + 1
         return (FrameCounter(n, total_frames, frames_per_second),)
+
+
+class DreamFrameCountCalculator:
+    NODE_NAME = "Frame Count Calculator"
+    ICON = "⌛"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "hours": ("INT", {"min": 0, "default": 0, "max": 23}),
+                "minutes": ("INT", {"min": 0, "default": 0, "max": 59}),
+                "seconds": ("INT", {"min": 0, "default": 10, "max": 59}),
+                "milliseconds": ("INT", {"min": 0, "default": 0, "max": 59}),
+                "frames_per_second": ("INT", {"min": 1, "default": 30})
+            },
+        }
+
+    CATEGORY = NodeCategories.ANIMATION
+    RETURN_TYPES = ("INT",)
+    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

@@ -0,0 +1,98 @@
+# -*- coding: utf-8 -*-
+import math
+
+from evalidate import Expr, EvalException, base_eval_model
+
+from .categories import *
+from .err import on_error
+from .shared import hashed_as_strings
+
+
+class DreamCalculation:
+    NODE_NAME = "Calculation"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "expression": ("STRING", {"default": "a + b + c - (r * s * t)", "multiline": True})
+            },
+            "optional": {
+                "a_int": ("INT", {"default": 0, "multiline": False}),
+                "b_int": ("INT", {"default": 0, "multiline": False}),
+                "c_int": ("INT", {"default": 0, "multiline": False}),
+                "r_float": ("FLOAT", {"default": 0.0, "multiline": False}),
+                "s_float": ("FLOAT", {"default": 0.0, "multiline": False}),
+                "t_float": ("FLOAT", {"default": 0.0, "multiline": False})
+            }
+        }
+
+    CATEGORY = NodeCategories.ANIMATION_CURVES
+    RETURN_TYPES = ("FLOAT", "INT")
+    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()
+        m.nodes.append('Mult')
+        m.nodes.append('Call')
+        for funname in funcs.keys():
+            m.allowed_functions.append(funname)
+        return (m, funcs)
+
+    def _make_functions(self):
+        return {
+            "round": round,
+            "float": float,
+            "int": int,
+            "abs": abs,
+            "min": min,
+            "max": max,
+            "tan": math.tan,
+            "tanh": math.tanh,
+            "sin": math.sin,
+            "sinh": math.sinh,
+            "cos": math.cos,
+            "cosh": math.cosh,
+            "pow": math.pow,
+            "sqrt": math.sqrt,
+            "ceil": math.ceil,
+            "floor": math.floor,
+            "pi": math.pi,
+            "log": math.log,
+            "log2": math.log2,
+            "acos": math.acos,
+            "asin": math.asin,
+            "acosh": math.acosh,
+            "asinh": math.asinh,
+            "atan": math.atan,
+            "atanh": math.atanh,
+            "exp": math.exp,
+            "fmod": math.fmod,
+            "factorial": math.factorial,
+            "dist": math.dist,
+            "atan2": math.atan2,
+            "log10": math.log10
+        }
+
+    def result(self, expression, **values):
+        model, funcs = self._make_model()
+        vars = funcs
+        for key in ("a_int", "b_int", "c_int", "r_float", "s_float", "t_float"):
+            nm = key.split("_")[0]
+            v = values.get(key, None)
+            if v is not None:
+                vars[nm] = v
+        try:
+            data = Expr(expression, model=model).eval(vars)
+            if isinstance(data, (int, float)):
+                return float(data), int(round(data))
+            else:
+                return 0.0, 0
+        except EvalException as e:
+            on_error(DreamCalculation, str(e))

categories.py

@@ -1,11 +1,15 @@
+# -*- coding: utf-8 -*-
+
 class NodeCategories:
     ANIMATION = "animation"
     ANIMATION_POSTPROCESSING = ANIMATION + "/postprocessing"
     ANIMATION_TRANSFORMS = ANIMATION + "/transforms"
     ANIMATION_CURVES = "animation/curves"
+    CONDITIONING = "conditioning"
     IMAGE_POSTPROCESSING = "image/postprocessing"
     IMAGE_ANIMATION = "image/animation"
     IMAGE_COLORS = "image/color"
     IMAGE_GENERATE = "image/generate"
     IMAGE = "image"
     UTILS = "utils"
+    UTILS_SWITCHES = "utils/switches"

colors.py

@@ -1,6 +1,86 @@
+# -*- coding: utf-8 -*-
+from .categories import NodeCategories
 from .shared import *
 from .types import *
-from .categories import NodeCategories
+
+
+class DreamImageAreaSampler:
+    NODE_NAME = "Sample Image Area as Palette"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "samples": ("INT", {"default": 256, "min": 1, "max": 1024 * 4}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "area": (["top-left", "top-center", "top-right",
+                          "center-left", "center", "center-right",
+                          "bottom-left", "bottom-center", "bottom-right"],)
+            },
+        }
+
+    CATEGORY = NodeCategories.IMAGE_COLORS
+    RETURN_TYPES = (RGBPalette.ID,)
+    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
+        wpart = round(w / 3)
+        hpart = round(h / 3)
+        x0 = 0
+        x1 = wpart - 1
+        x2 = wpart
+        x3 = wpart + wpart - 1
+        x4 = wpart + wpart
+        x5 = w - 1
+        y0 = 0
+        y1 = hpart - 1
+        y2 = hpart
+        y3 = hpart + hpart - 1
+        y4 = hpart + hpart
+        y5 = h - 1
+        if area == "center":
+            return (x2, y2, x3, y3)
+        elif area == "top-center":
+            return (x2, y0, x3, y1)
+        elif area == "bottom-center":
+            return (x2, y4, x3, y5)
+        elif area == "center-left":
+            return (x0, y2, x1, y3)
+        elif area == "top-left":
+            return (x0, y0, x1, y1)
+        elif area == "bottom-left":
+            return (x0, y4, x1, y5)
+        elif area == "center-right":
+            return (x4, y2, x5, y3)
+        elif area == "top-right":
+            return (x4, y0, x5, y1)
+        elif area == "bottom-right":
+            return (x4, y4, x5, y5)
+
+    def result(self, image, samples, seed, area):
+        result = list()
+        r = random.Random()
+        r.seed(seed)
+        for data in image:
+            di = DreamImage(tensor_image=data)
+            area = self._get_pixel_area(di, area)
+
+            pixels = list()
+            for i in range(samples):
+                x = r.randint(area[0], area[2])
+                y = r.randint(area[1], area[3])
+                pixels.append(di.get_pixel(x, y))
+            result.append(RGBPalette(colors=pixels))
+
+        return (tuple(result),)
 
 
 class DreamImageSampler:
@@ -11,7 +91,7 @@ class DreamImageSampler:
         return {
             "required": {
                 "image": ("IMAGE",),
-                "samples": ("INT", {"default": 1024, "min": 1, "max": 1024 * 64}),
+                "samples": ("INT", {"default": 1024, "min": 1, "max": 1024 * 4}),
                 "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff})
             },
         }
@@ -48,7 +128,7 @@ class DreamColorAlign:
     def INPUT_TYPES(cls):
         return {
             "required": SharedTypes.palette | {
-                "target_align": (RGBPalette.ID, {"forceInput": True}),
+                "target_align": (RGBPalette.ID, ),
                 "alignment_factor": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.1}),
             }
         }
@@ -136,6 +216,7 @@ class DreamColorShift:
 
 class DreamAnalyzePalette:
     NODE_NAME = "Analyze Palette"
+    NODE = "📊"
 
     @classmethod
     def INPUT_TYPES(cls):

curves.py

@@ -1,8 +1,24 @@
-import math, csv
+# -*- coding: utf-8 -*-
+import csv
+import math
 
-from .types import SharedTypes, FrameCounter
-from .shared import hashed_as_strings
 from .categories import NodeCategories
+from .shared import hashed_as_strings
+from .types import SharedTypes, FrameCounter
+
+
+def _linear_value_calc(x, x_start, x_end, y_start, y_end):
+    if x <= x_start:
+        return y_start
+    if x >= x_end:
+        return y_end
+    dx = max(x_end - x_start, 0.0001)
+    n = (x - x_start) / dx
+    return (y_end - y_start) * n + y_start
+
+
+def _curve_result(f: float):
+    return (f, int(round(f)))
 
 
 class DreamSineWave:
@@ -35,7 +51,149 @@ class DreamSineWave:
         b = 2 * math.pi / periodicity_seconds
         d = (max_value + min_value) / 2
         y = a * math.sin(b * (x + c)) + d
-        return (y, int(round(y)))
+        return _curve_result(y)
+
+
+class DreamSawWave:
+    NODE_NAME = "Saw Curve"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": SharedTypes.frame_counter | {
+                "max_value": ("FLOAT", {"default": 1.0, "multiline": False}),
+                "min_value": ("FLOAT", {"default": 0.0, "multiline": False}),
+                "periodicity_seconds": ("FLOAT", {"default": 10.0, "multiline": False, "min": 0.01}),
+                "phase": ("FLOAT", {"default": 0.0, "multiline": False, "min": -1, "max": 1}),
+            },
+        }
+
+    CATEGORY = NodeCategories.ANIMATION_CURVES
+    RETURN_TYPES = ("FLOAT", "INT")
+    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
+        y = x * (max_value - min_value) + min_value
+        return _curve_result(y)
+
+
+class DreamTriangleWave:
+    NODE_NAME = "Triangle Curve"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": SharedTypes.frame_counter | {
+                "max_value": ("FLOAT", {"default": 1.0, "multiline": False}),
+                "min_value": ("FLOAT", {"default": 0.0, "multiline": False}),
+                "periodicity_seconds": ("FLOAT", {"default": 10.0, "multiline": False, "min": 0.01}),
+                "phase": ("FLOAT", {"default": 0.0, "multiline": False, "min": -1, "max": 1}),
+            },
+        }
+
+    CATEGORY = NodeCategories.ANIMATION_CURVES
+    RETURN_TYPES = ("FLOAT", "INT")
+    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
+        if x <= 0.5:
+            x *= 2
+            y = x * (max_value - min_value) + min_value
+        else:
+            x = (x - 0.5) * 2
+            y = max_value - x * (max_value - min_value)
+        return _curve_result(y)
+
+
+class DreamTriangleEvent:
+    NODE_NAME = "Triangle Event Curve"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": SharedTypes.frame_counter | {
+                "max_value": ("FLOAT", {"default": 1.0, "multiline": False}),
+                "min_value": ("FLOAT", {"default": 0.0, "multiline": False}),
+                "width_seconds": ("FLOAT", {"default": 1.0, "multiline": False, "min": 0.1}),
+                "center_seconds": ("FLOAT", {"default": 10.0, "multiline": False, "min": 0.0}),
+            },
+        }
+
+    CATEGORY = NodeCategories.ANIMATION_CURVES
+    RETURN_TYPES = ("FLOAT", "INT")
+    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
+        end = center_seconds + width_seconds * 0.5
+        if start <= x <= center_seconds:
+            y = _linear_value_calc(x, start, center_seconds, min_value, max_value)
+        elif center_seconds < x <= end:
+            y = _linear_value_calc(x, center_seconds, end, max_value, min_value)
+        else:
+            y = min_value
+        return _curve_result(y)
+
+
+class DreamSmoothEvent:
+    NODE_NAME = "Smooth Event Curve"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": SharedTypes.frame_counter | {
+                "max_value": ("FLOAT", {"default": 1.0, "multiline": False}),
+                "min_value": ("FLOAT", {"default": 0.0, "multiline": False}),
+                "width_seconds": ("FLOAT", {"default": 1.0, "multiline": False, "min": 0.1}),
+                "center_seconds": ("FLOAT", {"default": 10.0, "multiline": False, "min": 0.0}),
+            },
+        }
+
+    CATEGORY = NodeCategories.ANIMATION_CURVES
+    RETURN_TYPES = ("FLOAT", "INT")
+    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
+        end = center_seconds + width_seconds * 0.5
+        if start <= x <= center_seconds:
+            y = _linear_value_calc(x, start, center_seconds, 0.0, 1.0)
+        elif center_seconds < x <= end:
+            y = _linear_value_calc(x, center_seconds, end, 1.0, 0.0)
+        else:
+            y = 0.0
+        if y < 0.5:
+            y = ((y + y) * (y + y)) * 0.5
+        else:
+            a = (y - 0.5) * 2
+            y = math.pow(a, 0.25) * 0.5 + 0.5
+        return _curve_result(y * (max_value - min_value) + min_value)
 
 
 class DreamBeatCurve:
@@ -82,7 +240,8 @@ class DreamBeatCurve:
             return 1.0 - ((frame - accent_start) / frames_per_beat)
         return 0
 
-    def result(self, bpm, frame_counter: FrameCounter, measure_length, low_value, high_value, power, invert, time_offset, **accents):
+    def result(self, bpm, frame_counter: FrameCounter, measure_length, low_value, high_value, power, invert,
+               time_offset, **accents):
         frame_offset = int(round(time_offset * frame_counter.frames_per_second))
         accents_set = set(filter(lambda v: v >= 1 and v <= measure_length,
                                  map(lambda i: accents.get("accent_" + str(i), -1), range(30))))
@@ -93,7 +252,7 @@ class DreamBeatCurve:
             v = 1.0 - v
 
         r = low_value + v * (high_value - low_value)
-        return (r, int(round(r)))
+        return _curve_result(r)
 
 
 class DreamLinear:
@@ -133,6 +292,7 @@ def _is_as_float(s: str):
 
 class DreamCSVGenerator:
     NODE_NAME = "CSV Generator"
+    ICON = "⌗"
 
     @classmethod
     def INPUT_TYPES(cls):

disable.py

@@ -1,6 +1,7 @@
-
+# -*- coding: utf-8 -*-
 def run_disable():
     pass
 
+
 if __name__ == "__main__":
-    run_disable()
+    run_disable()

dreamlogger.py

@@ -0,0 +1,18 @@
+class DreamLog:
+    def __init__(self, debug_active=False):
+        self._debug = debug_active
+
+    def _print(self, text: str, *args, **kwargs):
+        if args or kwargs:
+            text = text.format(*args, **kwargs)
+        print("[DREAM] " + text)
+
+    def error(self, text: str, *args, **kwargs):
+        self._print(text, *args, **kwargs)
+
+    def info(self, text: str, *args, **kwargs):
+        self._print(text, *args, **kwargs)
+
+    def debug(self, text: str, *args, **kwargs):
+        if self._debug:
+            self._print(text, *args, **kwargs)

embedded_config.py

@@ -0,0 +1,41 @@
+EMBEDDED_CONFIGURATION = {
+    "ffmpeg": {
+        "file_extension": "mp4",
+        "path": "ffmpeg",
+        "arguments": ["-r", "%FPS%", "-f", "concat", "-safe", "0", "-vsync",
+                      "cfr", "-i", "%FRAMES%", "-c:v", "libx265", "-pix_fmt",
+                      "yuv420p", "%OUTPUT%"]
+    },
+    "mpeg_coder": {
+        "encoding_threads": 4,
+        "bitrate_factor": 1.0,
+        "max_b_frame": 2,
+        "file_extension": "mp4",
+        "codec_name": "libx265"
+    },
+    "encoding": {
+        "jpeg_quality": 95
+    },
+    "debug": False,
+    "ui": {
+        "top_category": "Dream",
+        "prepend_icon_to_category": True,
+        "append_icon_to_category": False,
+        "prepend_icon_to_node": True,
+        "append_icon_to_node": False,
+        "category_icons": {
+            "animation": "🎥",
+            "postprocessing": "⚙",
+            "transforms": "🔀",
+            "curves": "📈",
+            "color": "🎨",
+            "generate": "⚡",
+            "utils": "🛠",
+            "image": "🌄",
+            "switches": "⭆",
+            "conditioning": "☯",
+            "Dream": "✨"
+        }
+    },
+
+}

enable.py

@@ -1,5 +1,7 @@
+# -*- coding: utf-8 -*-
 def run_enable():
     pass
 
+
 if __name__ == "__main__":
     run_enable()

err.py

@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+def _get_node_name(cls):
+    return cls.__dict__.get("NODE_NAME", str(cls))
+
+
+def on_error(node_cls: type, message: str):
+    msg = "Failure in [" + _get_node_name(node_cls) + "]:" + message
+    print(msg)
+    raise Exception(msg)

image_processing.py

@@ -1,13 +1,15 @@
+# -*- coding: utf-8 -*-
 import math
+
 import numpy
 import torch
-from PIL.Image import Resampling
 from PIL import Image, ImageDraw
-from .categories import *
-from .types import SharedTypes, FrameCounter
+from PIL.Image import Resampling
 
+from .categories import *
 from .shared import ALWAYS_CHANGED_FLAG, convertTensorImageToPIL, DreamImageProcessor, \
     DreamImage, DreamMask
+from .types import SharedTypes, FrameCounter
 
 
 class DreamImageMotion:
@@ -85,7 +87,8 @@ class DreamImageMotion:
         def _limit_range(f):
             return max(-1.0, min(1.0, f))
 
-        def _motion(image: DreamImage, batch_counter, zoom, x_translation, y_translation, mask_1_overlap, mask_2_overlap,
+        def _motion(image: DreamImage, batch_counter, zoom, x_translation, y_translation, mask_1_overlap,
+                    mask_2_overlap,
                     mask_3_overlap):
             zoom = _limit_range(zoom / frame_counter.frames_per_second)
             x_translation = _limit_range(x_translation / frame_counter.frames_per_second)

inputfields.py

@@ -0,0 +1,100 @@
+from .categories import *
+from .shared import *
+
+class DreamInputText:
+    NODE_NAME = "Text Input"
+    ICON = "✍"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("STRING", {"default": "", "multiline": True}),
+            },
+        }
+
+    CATEGORY = NodeCategories.UTILS
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("STRING",)
+    FUNCTION = "noop"
+
+    @classmethod
+    def IS_CHANGED(cls, *values):
+        return hashed_as_strings(*values)
+
+    def noop(self, value):
+        return (value,)
+
+class DreamInputString:
+    NODE_NAME = "String Input"
+    ICON = "✍"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("STRING", {"default": "", "multiline": False}),
+            },
+        }
+
+    CATEGORY = NodeCategories.UTILS
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("STRING",)
+    FUNCTION = "noop"
+
+    @classmethod
+    def IS_CHANGED(cls, *values):
+        return hashed_as_strings(*values)
+
+    def noop(self, value):
+        return (value,)
+
+
+class DreamInputFloat:
+    NODE_NAME = "Float Input"
+    ICON = "✍"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("FLOAT", {"default": 0.0}),
+            },
+        }
+
+    CATEGORY = NodeCategories.UTILS
+    RETURN_TYPES = ("FLOAT",)
+    RETURN_NAMES = ("FLOAT",)
+    FUNCTION = "noop"
+
+    @classmethod
+    def IS_CHANGED(cls, *values):
+        return hashed_as_strings(*values)
+
+    def noop(self, value):
+        return (value,)
+
+
+class DreamInputInt:
+    NODE_NAME = "Int Input"
+    ICON = "✍"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("INT", {"default": 0}),
+            },
+        }
+
+    CATEGORY = NodeCategories.UTILS
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("INT",)
+    FUNCTION = "noop"
+
+    @classmethod
+    def IS_CHANGED(cls, *values):
+        return hashed_as_strings(*values)
+
+    def noop(self, value):
+        return (value,)

install.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 from .shared import DreamConfig
 
 

license.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Morgan Johansson/Dream Project
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

loaders.py

@@ -1,12 +1,12 @@
-from PIL import Image
-from .types import SharedTypes, FrameCounter
-from .shared import ALWAYS_CHANGED_FLAG, list_images_in_directory, convertFromPILToTensorImage, DreamImage
+# -*- coding: utf-8 -*-
 from .categories import NodeCategories
-import os
+from .shared import ALWAYS_CHANGED_FLAG, list_images_in_directory, DreamImage
+from .types import SharedTypes, FrameCounter
 
 
 class DreamImageSequenceInputWithDefaultFallback:
     NODE_NAME = "Image Sequence Loader"
+    ICON = "💾"
 
     @classmethod
     def INPUT_TYPES(cls):

node_list.json

@@ -1,23 +1,47 @@
 {
   "Analyze Palette [Dream]": "Output brightness, red, green and blue averages of a palette",
   "Beat Curve [Dream]": "Beat pattern curve with impulses at specified beats of a measure",
+  "Big Float Switch [Dream]": "Switch for up to 10 inputs",
+  "Big Image Switch [Dream]": "Switch for up to 10 inputs",
+  "Big Int Switch [Dream]": "Switch for up to 10 inputs",
+  "Big Latent Switch [Dream]": "Switch for up to 10 inputs",
+  "Big Palette Switch [Dream]": "Switch for up to 10 inputs",
+  "Big Text Switch [Dream]": "Switch for up to 10 inputs",
+  "Boolean To Float [Dream]": "Converts a boolean value to two different float values",
+  "Boolean To Int [Dream]": "Converts a boolean value to two different int values",
+  "Build Prompt [Dream]": "Weighted text prompt builder utility",
   "CSV Curve [Dream]": "CSV input curve where first column is frame or second and second column is value",
   "CSV Generator [Dream]": "CSV output, mainly for debugging purposes",
+  "Calculation [Dream]": "Mathematical calculation node",
   "Common Frame Dimensions [Dream]": "Utility for calculating good width/height based on common video dimensions",
   "FFMPEG Video Encoder [Dream]": "Post processing for animation sequences calling FFMPEG to generate video file",
   "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)",
+  "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",
   "Image Motion [Dream]": "Node supporting zooming in/out and translating an image",
   "Image Sequence Blend [Dream]": "Post processing for animation sequences blending frame for a smoother blurred effect",
   "Image Sequence Loader [Dream]": "Loads a frame from a directory of images",
   "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)",
   "Linear Curve [Dream]": "Linear interpolation between two value over the full animation",
+  "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",
+  "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",
-  "Sine Curve [Dream]": "Simple sine wave curve"
-}
+  "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)",
+  "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"
+} and switches.

noise.py

@@ -1,10 +1,31 @@
+# -*- coding: utf-8 -*-
+import math
+
+from .categories import NodeCategories
 from .shared import *
 from .types import *
-from .categories import NodeCategories
+
+
+def _generate_noise(image: DreamImage, color_function, rng: random.Random, block_size, blur_amount,
+                    density) -> DreamImage:
+    w = block_size[0]
+    h = block_size[1]
+    blur_radius = round(max(image.width, image.height) * blur_amount * 0.25)
+    if w <= (image.width // 128) or h <= (image.height // 128):
+        return image
+    max_placements = round(density * (image.width * image.height))
+    num = min(max_placements, round((image.width * image.height * 2) / (w * h)))
+    for i in range(num):
+        x = rng.randint(-w + 1, image.width - 1)
+        y = rng.randint(-h + 1, image.height - 1)
+        image.color_area(x, y, w, h, color_function(x + (w >> 1), y + (h >> 1)))
+    image = image.blur(blur_radius)
+    return _generate_noise(image, color_function, rng, (w >> 1, h >> 1), blur_amount, density)
 
 
 class DreamNoiseFromPalette:
     NODE_NAME = "Noise from Palette"
+    ICON = "🌫"
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -12,8 +33,8 @@ class DreamNoiseFromPalette:
             "required": SharedTypes.palette | {
                 "width": ("INT", {"default": 512, "min": 1, "max": 8192}),
                 "height": ("INT", {"default": 512, "min": 1, "max": 8192}),
-                "blur_amount": ("FLOAT", {"default": 0.1, "min": 0, "max": 1.0, "step": 0.05}),
-                "iterations": ("INT", {"default": 4, "min": 1, "max": 64}),
+                "blur_amount": ("FLOAT", {"default": 0.3, "min": 0, "max": 1.0, "step": 0.05}),
+                "density": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.025}),
                 "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff})
             },
         }
@@ -27,28 +48,116 @@ class DreamNoiseFromPalette:
     def IS_CHANGED(cls, *values):
         return ALWAYS_CHANGED_FLAG
 
-    def generate_noise(self, image: DreamImage, color_function, rng: random.Random, i: int, blur_amount) -> DreamImage:
-        w = image.width >> i
-        h = image.height >> i
-        blur_radius = round(max(image.width, image.height) * blur_amount * 0.25)
-        if w <= 1 or h <= 1:
-            return image
-        for i in range(1 << (i*2)):
-            x = rng.randint(-w+1, image.width - 1)
-            y = rng.randint(-h+1, image.height - 1)
-            image.color_area(x, y, w, h, color_function())
-        image = image.blur(blur_radius)
-        return self.generate_noise(image, color_function, rng, i + 1, blur_amount)
-
-    def result(self, palette: Tuple[RGBPalette], width, height, seed, blur_amount, iterations):
+    def result(self, palette: Tuple[RGBPalette], width, height, seed, blur_amount, density):
         outputs = list()
         rng = random.Random()
         for p in palette:
             seed += 1
             color_iterator = p.random_iteration(seed)
             image = DreamImage(pil_image=Image.new("RGB", (width, height), color=next(color_iterator)))
-            for n in range(iterations):
-                image = self.generate_noise(image, lambda: next(color_iterator), rng, 1, blur_amount)
+            image = _generate_noise(image, lambda x, y: next(color_iterator), rng,
+                                    (image.width >> 1, image.height >> 1), blur_amount, density)
             outputs.append(image)
 
         return (DreamImage.join_to_tensor_data(outputs),)
+
+
+class DreamNoiseFromAreaPalettes:
+    NODE_NAME = "Noise from Area Palettes"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "top_left_palette": (RGBPalette.ID,),
+                "top_center_palette": (RGBPalette.ID,),
+                "top_right_palette": (RGBPalette.ID,),
+                "center_left_palette": (RGBPalette.ID,),
+                "center_palette": (RGBPalette.ID,),
+                "center_right_palette": (RGBPalette.ID,),
+                "bottom_left_palette": (RGBPalette.ID,),
+                "bottom_center_palette": (RGBPalette.ID,),
+                "bottom_right_palette": (RGBPalette.ID,),
+            },
+            "required": {
+                "area_sharpness": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05}),
+                "width": ("INT", {"default": 512, "min": 1, "max": 8192}),
+                "height": ("INT", {"default": 512, "min": 1, "max": 8192}),
+                "blur_amount": ("FLOAT", {"default": 0.3, "min": 0, "max": 1.0, "step": 0.05}),
+                "density": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.025}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+        }
+
+    CATEGORY = NodeCategories.IMAGE_GENERATE
+    ICON = "🌫"
+    RETURN_TYPES = ("IMAGE",)
+    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
+        return {
+            "top_left_palette": (dx, dy),
+            "top_center_palette": (dx * 3, dy),
+            "top_right_palette": (dx * 5, dy),
+            "center_left_palette": (dx, dy * 3),
+            "center_palette": (dx * 3, dy * 3),
+            "center_right_palette": (dx * 5, dy * 3),
+            "bottom_left_palette": (dx * 1, dy * 5),
+            "bottom_center_palette": (dx * 3, dy * 5),
+            "bottom_right_palette": (dx * 5, dy * 5),
+        }
+
+    def _pick_random_area(self, active_coordinates, x, y, rng, area_sharpness):
+        def _dst(x1, y1, x2, y2):
+            a = x1 - x2
+            b = y1 - y2
+            return math.sqrt(a * a + b * b)
+
+        distances = list(map(lambda item: (item[0], _dst(item[1][0], item[1][1], x, y)), active_coordinates))
+        areas_by_weight = list(
+            map(lambda item: (math.pow((1.0 / max(1, item[1])), 0.5 + 4.5 * area_sharpness), item[0]), distances))
+        return pick_random_by_weight(areas_by_weight, rng)
+
+    def _setup_initial_colors(self, image: DreamImage, color_func):
+        w = image.width
+        h = image.height
+        wpart = round(w / 3)
+        hpart = round(h / 3)
+        for i in range(3):
+            for j in range(3):
+                image.color_area(wpart * i, hpart * j, w, h,
+                                 color_func(wpart * i + w // 2, hpart * j + h // 2))
+
+    def result(self, width, height, seed, blur_amount, density, area_sharpness, **palettes):
+        outputs = list()
+        rng = random.Random()
+        coordinates = self._area_coordinates(width, height)
+        active_palettes = list(filter(lambda pair: pair[1] is not None and len(pair[1]) > 0, palettes.items()))
+        active_coordinates = list(map(lambda item: (item[0], coordinates[item[0]]), active_palettes))
+
+        n = max(list(map(len, palettes.values())) + [0])
+        for b in range(n):
+            batch_palettes = dict(map(lambda item: (item[0], item[1][b].random_iteration(seed)), active_palettes))
+
+            def _color_func(x, y):
+                name = self._pick_random_area(active_coordinates, x, y, rng, area_sharpness)
+                rgb = batch_palettes[name]
+                return next(rgb)
+
+            image = DreamImage(pil_image=Image.new("RGB", (width, height)))
+            self._setup_initial_colors(image, _color_func)
+            image = _generate_noise(image, _color_func, rng, (round(image.width / 3), round(image.height / 3)),
+                                    blur_amount, density)
+            outputs.append(image)
+
+        if not outputs:
+            outputs.append(DreamImage(pil_image=Image.new("RGB", (width, height))))
+
+        return (DreamImage.join_to_tensor_data(outputs),)

output.py

@@ -1,14 +1,18 @@
+# -*- coding: utf-8 -*-
 import json
-from PIL.PngImagePlugin import PngInfo
-from .categories import NodeCategories
-import folder_paths as comfy_paths
-from .types import SharedTypes, FrameCounter, AnimationSequence
-from .shared import hashed_as_strings, DreamImageProcessor, DreamImage, \
-    list_images_in_directory, DreamConfig
 import os
 
+import folder_paths as comfy_paths
+from PIL.PngImagePlugin import PngInfo
+
+from .categories import NodeCategories
+from .shared import hashed_as_strings, DreamImageProcessor, DreamImage, \
+    list_images_in_directory, DreamConfig
+from .types import SharedTypes, FrameCounter, AnimationSequence
+
 CONFIG = DreamConfig()
 
+
 def _save_png(pil_image, filepath, embed_info, prompt, extra_pnginfo):
     info = PngInfo()
     if extra_pnginfo is not None:
@@ -28,6 +32,7 @@ def _save_jpg(pil_image, filepath, quality):
 
 class DreamImageSequenceOutput:
     NODE_NAME = "Image Sequence Saver"
+    ICON = "💾"
 
     @classmethod
     def INPUT_TYPES(cls):

prompting.py

@@ -0,0 +1,69 @@
+from .categories import NodeCategories
+from .shared import hashed_as_strings
+from .types import PartialPrompt
+
+
+class DreamWeightedPromptBuilder:
+    NODE_NAME = "Build Prompt"
+    ICON = "⚖"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "partial_prompt": (PartialPrompt.ID,)
+            },
+            "required": {
+                "added_prompt": ("STRING", {"default": "", "multiline": True}),
+                "weight": ("FLOAT", {"default": 1.0}),
+            },
+        }
+
+    CATEGORY = NodeCategories.CONDITIONING
+    RETURN_TYPES = (PartialPrompt.ID,)
+    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)
+        return (p,)
+
+
+class DreamPromptFinalizer:
+    NODE_NAME = "Finalize Prompt"
+    ICON = "🗫"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "partial_prompt": (PartialPrompt.ID,),
+                "adjustment": (["raw", "by_abs_max", "by_abs_sum"],),
+                "clamp": ("FLOAT", {"default": 2.0, "min": 0.1, "step": 0.1}),
+                "adjustment_reference": ("FLOAT", {"default": 1.0, "min": 0.1}),
+            },
+        }
+
+    CATEGORY = NodeCategories.CONDITIONING
+    RETURN_TYPES = ("STRING", "STRING")
+    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():
+            return partial_prompt.finalize(clamp)
+        elif adjustment == "by_abs_sum":
+            f = adjustment_reference / partial_prompt.abs_sum()
+            return partial_prompt.scaled_by(f).finalize(clamp)
+        else:
+            f = adjustment_reference / partial_prompt.abs_max()
+            return partial_prompt.scaled_by(f).finalize(clamp)

readme.md

@@ -9,6 +9,12 @@ I have demonstrated the use of these custom nodes in this [youtube video](https:
 
 ## Installation
 
+### Simple option
+
+You can install Dream Project Animation Nodes using the ComfyUI Manager.
+
+### Manual option
+
 Run within (ComfyUI)/custom_nodes/ folder:
 
 * git clone https://github.com/alt-key-project/comfyui-dream-project.git
@@ -26,6 +32,76 @@ Finally:
 After startup, a configuration file 'config.json' should have been created in the 'comfyui-dream-project' directory. 
 Specifically check that the path of ffmpeg works in your system (add full path to the command if needed).
 
+## Upgrade
+
+When upgrading, it is good to re-run the pip install command as specified in the install section. This will install any 
+new dependencies.
+
+## Configuration
+
+### debug
+
+Setting this to true will enable some trace-level logging.
+
+### ffmpeg.file_extension
+
+Sets the output file extension and with that the envelope used.
+
+### ffmpeg.path 
+
+Path to the ffmpeg executable or just the command if ffmpeg is in PATH.
+
+### ffmpeg.arguments
+
+The arguments sent to FFMPEG. A few of the values are provided by the node:
+
+* %FPS% the target framerate
+* %FRAMES% a frame ionput file
+* %OUTPUT% output video file path
+
+### encoding.jpeg__quality
+
+Sets the encoding quality of jpeg images.
+
+### ui.top_category
+
+Sets the name of the top level category on the menu. Set to empty string "" to remove the top level. If the top level 
+is removed you may also want to disable the category icons to get nodes into existing category folders.
+
+### prepend_icon_to_category / append_icon_to_category
+
+Flags to add a icon before and/or after the category name at each level.
+
+### prepend_icon_icon_to_node / append_icon_icon_to_node
+
+Flags to add an icon before and/or after the node name.
+
+### ui.category_icons
+
+Each key defines a unicode symbol as an icon used for the specified category.
+
+### mpeg_coder.bitrate_factor
+
+This factor allows changing the bitrate to better fit the required quality and codec. A value of 1 is typically 
+suitable for H.265.
+
+### mpeg_coder.codec_name
+
+Codec names as specified by ffmpeg. Some common options include "libx264", "libx264" and "mpeg2video".
+
+### mpeg_coder.encoding_threads
+
+Increasing the number of encoding threads in mpegCoder will generally reduce the overall encoding time, but it will also 
+increase the load on the computer.
+
+### mpeg_coder.file_extension
+
+Sets the output file extension and with that the envelope used.
+
+### mpeg_coder.max_b_frame
+
+Sets the max-b-frames parameter for as specified in ffmpeg.
+
 ## Concepts used
 
 These are some concepts used in nodes:
@@ -51,83 +127,157 @@ video file using ffmpeg. These nodes should be seen as a convenience and they ar
 nodes in parallel - they will not work as intended!
 
 ## The nodes
-  ### Analyze Palette [Dream]
-  Output brightness, red, green and blue averages of a palette. Useful to control other processing.
+### Analyze Palette [Dream]
+Output brightness, red, green and blue averages of a palette. Useful to control other processing.
 
-  ### Beat Curve [Dream]
-  Beat pattern curve with impulses at specified beats of a measure.
-  
-  ### CSV Curve [Dream]
-  CSV input curve where first column is frame or second and second column is value.
-  
-  ### CSV Generator [Dream]
-  CSV output, mainly for debugging purposes. First column is frame number and second is value.
-  Recreates file at frame 0 (removing and existing content in the file).
-  
-  ### Common Frame Dimensions [Dream]
-  Utility for calculating good width/height based on common video dimensions.
-  
-  ### FFMPEG Video Encoder [Dream]
-  Post processing for animation sequences calling FFMPEG to generate video file.
-  
-  ### File Count [Dream]
-  Finds the number of files in a directory matching specified patterns.
-  
-  ### Frame Counter (Directory) [Dream]
-  Directory backed frame counter, for output directories.
-  
-  ### Frame Counter (Simple) [Dream]
-  Integer value used as frame counter. Useful for testing or if an auto-incrementing primitive is used as a frame 
-  counter.
-  
-  ### Frame Counter Offset [Dream]
-  Adds an offset to a frame counter.
-  
-  ### Image Motion [Dream]
-  Node supporting zooming in/out and translating an image.
-  
-  ### Image Sequence Blend [Dream]
-  Post processing for animation sequences blending frame for a smoother blurred effect.
-  
-  ### Image Sequence Loader [Dream]
-  Loads a frame from a directory of images.
-  
-  ### Image Sequence Saver [Dream]
-  Saves a frame to a directory.
-  
-  ### Image Sequence Tweening [Dream]
-  Post processing for animation sequences generating blended in-between frames.
-  
-  ### Linear Curve [Dream]
-  Linear interpolation between two values over the full animation.
-  
-  ### 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. If the alignment factor 
-  is 0.5 the result is nearly an average of the two palettes. At 0 no alignment is done and at 1 we get a close 
-  alignment to the target. Above one we will overshoot the alignment.
-  
-  ### Palette Color Shift [Dream]
-  Multiplies the color values in a palette to shift the color balance or brightness.
-  
-  ### Sample Image as Palette [Dream]
-  Randomly samples pixels from a source image to build a palette from it.
-  
-  ### Sine Curve [Dream]
-  Simple sine wave 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 
- custom nodes as well!
+### Beat Curve [Dream]
+Beat pattern curve with impulses at specified beats of a measure.
 
- ## Examples
+### Big *** Switch [Dream]
+Switch nodes for different type for up to ten inputs.
 
- ### Image Motion with Curves
+### Boolean To Float/Int [Dream]
+Converts a boolean value to two different numeric values.
+
+### Build Prompt [Dream] (and Finalize Prompt [Dream])
+Weighted text prompt builder utility. Chain any number of these nodes and terminate with 'Finalize Prompt'.
+
+### Calculation [Dream]
+Mathematical calculation node. Exposes most of the mathematical functions in the python 
+[math module](https://docs.python.org/3/library/math.html), mathematical operators as well as round, abs, int, 
+float, max and min.
+
+### CSV Curve [Dream]
+CSV input curve where first column is frame or second and second column is value.
+
+### CSV Generator [Dream]
+CSV output, mainly for debugging purposes. First column is frame number and second is value.
+Recreates file at frame 0 (removing and existing content in the file).
+
+### Common Frame Dimensions [Dream]
+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.
+
+### Frame Count Calculator [Dream]
+Simple utility to calculate number of frames based on time and framerate.
+
+### Frame Counter (Directory) [Dream]
+Directory backed frame counter, for output directories.
+
+### Frame Counter (Simple) [Dream]
+Integer value used as frame counter. Useful for testing or if an auto-incrementing primitive is used as a frame 
+counter.
+
+### Frame Counter Info [Dream]
+Extracts information from the frame counter.
+
+### Frame Counter Offset [Dream]
+Adds an offset to a frame counter.
+
+### Image Motion [Dream]
+Node supporting zooming in/out and translating an image.
+
+### Image Sequence Blend [Dream]
+Post processing for animation sequences blending frame for a smoother blurred effect.
+
+### Image Sequence Loader [Dream]
+Loads a frame from a directory of images.
+
+### Image Sequence Saver [Dream]
+Saves a frame to a directory.
+
+### Image Sequence Tweening [Dream]
+Post processing for animation sequences generating blended in-between frames.
+
+### Linear Curve [Dream]
+Linear interpolation between two values over the full animation.
+
+### Noise from Area Palettes [Dream]
+Generates noise based on the colors of up to nine different palettes, each connected to position/area of the 
+image. Although the palettes are optional, at least one palette should be provided.
+
+### 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. If the alignment factor 
+is 0.5 the result is nearly an average of the two palettes. At 0 no alignment is done and at 1 we get a close 
+alignment to the target. Above one we will overshoot the alignment.
+
+### Palette Color Shift [Dream]
+Multiplies the color values in a palette to shift the color balance or brightness.
+
+### Sample Image Area as Palette [Dream]
+Randomly samples a palette from an image based on pre-defined areas. The image is separated into nine rectangular areas 
+of equal size and each node may sample one of these.
+
+### Sample Image as Palette [Dream]
+Randomly samples pixels from a source image to build a palette from it.
+
+### 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. 
+
+### Triangle Curve [Dream]
+Triangle wave curve. 
+
+### Triangle Event Curve [Dream]
+Single event/peak curve with triangular shape.
+
+### Other custom nodes
+
+Many of the nodes found in 'WAS Node Suite' are useful the Dream Project Animation nodes - I suggest you install those 
+custom nodes as well!
+
+## Examples
+
+### Image Motion with Curves
 
 This example should be a starting point for anyone wanting to build with the Dream Project Animation nodes.
 
 [motion-workflow-example](examples/motion-workflow-example.json)
 
+### Image Motion with Color Coherence
+
+Same as above but with added color coherence through palettes.
+
+[motion-workflow-with-color-coherence](examples/motion-workflow-with-color-coherence.json)
+
+### Area Sampled Noise
+
+This flow demonstrates sampling image areas into palettes and generating noise for these areas.
+
+[area-sampled-noise](examples/area-sampled-noise.json)
+
+## Known issues
+
+### FFMPEG
+
+The call to FFMPEG currently in the default configuration (in config.json) does not seem to work for everyone. The good 
+news is that you can change the arguments to whatever works for you - the node-supplied parameters (that probably all need to be in the call)
+are:
+
+* -i %FRAMES% (the input file listing frames)
+* -r %FPS% (sets the frame rate)
+* %OUTPUT% (the path to the video file)
+
+If possible, I will change the default configuration to one that more versions/builds of ffmpeg will accept. Do let me 
+know what arguments are causing issues for you!
+
+### Framerate is not always right with mpegCoder encoding node
+
+The mpegCoder library will always use variable frame rate encoding if it is available in the output format. With most 
+outputs this means that your actual framerate will differ slightly from the requested one.

requirements.txt

@@ -2,4 +2,6 @@ imageio
 pilgram
 scipy
 numpy<1.24>=1.18
-torchvision
+torchvision
+mpegCoder
+evalidate

seq_processing.py

@@ -1,12 +1,17 @@
-from typing import Iterable, Tuple
-
-from .types import *
-from .categories import NodeCategories
-from .shared import DreamConfig, DreamImage
-import os, tempfile, subprocess, shutil, random
+# -*- coding: utf-8 -*-
+import os
+import shutil
+import subprocess
+import tempfile
 from functools import lru_cache
+
 from PIL import Image
 
+from .categories import NodeCategories
+from .err import on_error
+from .shared import DreamConfig, MpegEncoderUtility
+from .types import *
+
 CONFIG = DreamConfig()
 
 
@@ -117,21 +122,87 @@ def _ffmpeg(config, filenames, fps, output):
         for (key, value) in replacements.items():
             cmd = list(map(lambda s: s.replace(key, value), cmd))
 
-        subprocess.run(cmd, shell=True)
+        subprocess.check_output(cmd, shell=True)
     finally:
         os.unlink(tempfilepath)
 
 
-class DreamVideoEncoder:
-    NODE_NAME = "FFMPEG Video Encoder"
+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 | {
-                "filename": ("STRING", {"default": 'video.mp4', "multiline": False}),
+                "name": ("STRING", {"default": 'video', "multiline": False}),
                 "framerate_factor": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0}),
-                "remove_images": (["yes", "no"],)
+                "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 DreamVideoEncoder:
+    NODE_NAME = "FFMPEG Video Encoder"
+    DISPLAY_NAME = "Video Encoder (FFMPEG)"
+    ICON = "🎬"
+
+    @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})
             },
         }
 
@@ -160,22 +231,22 @@ class DreamVideoEncoder:
         filename = self._find_free_filename(filename, os.path.dirname(files[0]))
         _ffmpeg(config, files, fps, filename)
 
-    def encode(self, sequence: AnimationSequence, filename: str, remove_images, framerate_factor):
+    def encode(self, sequence: AnimationSequence, name: str, remove_images, framerate_factor):
         if not sequence.is_defined:
             return ()
 
         config = DreamConfig()
+        filename = _make_video_filename(name, config.get("ffmpeg.file_extension", "mp4"))
         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)
-                if remove_images == "yes":
+                if remove_images:
                     for imagepath in images:
                         if os.path.isfile(imagepath):
                             os.unlink(imagepath)
             except Exception as e:
-                print("Failed to encode files in dir {}!".format(os.path.dirname(images[0])))
-                print(str(e))
+                on_error(self.__class__, str(e))
         return ()
 
 

shared.py

@@ -1,13 +1,22 @@
-import hashlib, os, json, glob
+# -*- coding: utf-8 -*-
 
+import hashlib
+import json
+import os
+import random
+import time
+
+import folder_paths as comfy_paths
+import glob
 import numpy
 import torch
 from PIL import Image, ImageFilter
 from PIL.ImageDraw import ImageDraw
 from PIL.PngImagePlugin import PngInfo
+from .embedded_config import EMBEDDED_CONFIGURATION
 from typing import Dict, Tuple, List
 
-import folder_paths as comfy_paths
+from .dreamlogger import DreamLog
 
 NODE_FILE = os.path.abspath(__file__)
 DREAM_NODES_SOURCE_ROOT = os.path.dirname(NODE_FILE)
@@ -30,25 +39,40 @@ def _replace_pil_image(data):
         return data
 
 
+_config_data = None
+
+
 class DreamConfig:
     FILEPATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "config.json")
-    DEFAULT_CONFIG = {
-        "ffmpeg": {
-            "path": "ffmpeg",
-            "arguments": ["-r", "%FPS%", "-f", "concat", "-safe", "0", "-i", "%FRAMES%", "-c:v", "libx265", "-pix_fmt",
-                          "yuv420p", "%OUTPUT%"]
-        },
-        "encoding": {
-            "jpeg_quality": 95
-        }
-    }
+    DEFAULT_CONFIG = EMBEDDED_CONFIGURATION
 
     def __init__(self):
+        global _config_data
         if not os.path.isfile(DreamConfig.FILEPATH):
-            with open(DreamConfig.FILEPATH, "w") as f:
-                json.dump(DreamConfig.DEFAULT_CONFIG, f, indent=2)
-        with open(DreamConfig.FILEPATH) as f:
-            self._data = json.load(f)
+            self._data = DreamConfig.DEFAULT_CONFIG
+            self._save()
+        if _config_data is None:
+            with open(DreamConfig.FILEPATH, encoding="utf-8") as f:
+                self._data = json.load(f)
+                if self._merge_with_defaults(self._data, DreamConfig.DEFAULT_CONFIG):
+                    self._save()
+                _config_data = self._data
+        else:
+            self._data = _config_data
+
+    def _save(self):
+        with open(DreamConfig.FILEPATH, "w", encoding="utf-8") as f:
+            json.dump(self._data, f, indent=2)
+
+    def _merge_with_defaults(self, config: dict, default_config: dict) -> bool:
+        changed = False
+        for key in default_config.keys():
+            if key not in config:
+                changed = True
+                config[key] = default_config[key]
+            elif isinstance(default_config[key], dict):
+                changed = changed or self._merge_with_defaults(config[key], default_config[key])
+        return changed
 
     def get(self, key: str, default=None):
         key = key.split(".")
@@ -61,6 +85,11 @@ class DreamConfig:
             return d
 
 
+def get_logger():
+    config = DreamConfig()
+    return DreamLog(config.get("debug", False))
+
+
 class DreamImageProcessor:
     def __init__(self, inputs: torch.Tensor, **extra_args):
         self._images_in_batch = [convertTensorImageToPIL(tensor) for tensor in inputs]
@@ -89,31 +118,46 @@ class DreamImageProcessor:
         return tuple(map(lambda l: torch.cat(l, dim=0), output))
 
 
+def pick_random_by_weight(data: List[Tuple[float, object]], rng: random.Random):
+    total_weight = sum(map(lambda item: item[0], data))
+    r = rng.random()
+    for (weight, obj) in data:
+        r -= weight / total_weight
+        if r <= 0:
+            return obj
+    return data[0][1]
+
+
 class DreamImage:
     @classmethod
     def join_to_tensor_data(cls, images):
         l = list(map(lambda i: i.create_tensor_image(), images))
         return torch.cat(l, dim=0)
 
-    def __init__(self, tensor_image=None, pil_image=None, file_path=None):
+    def __init__(self, tensor_image=None, pil_image=None, file_path=None, with_alpha=False):
         if pil_image is not None:
             self.pil_image = pil_image
         elif tensor_image is not None:
             self.pil_image = convertTensorImageToPIL(tensor_image)
         else:
             self.pil_image = Image.open(file_path)
-        if self.pil_image.mode not in ("RGB", "RGBA"):
+        if with_alpha and self.pil_image.mode != "RGBA":
             self.pil_image = self.pil_image.convert("RGBA")
+        else:
+            if self.pil_image.mode not in ("RGB", "RGBA"):
+                self.pil_image = self.pil_image.convert("RGB")
         self.width = self.pil_image.width
         self.height = self.pil_image.height
         self.size = self.pil_image.size
         self._draw = ImageDraw(self.pil_image)
 
+    def numpy_array(self):
+        return numpy.array(self.pil_image)
+
     def _renew(self, pil_image):
         self.pil_image = pil_image
         self._draw = ImageDraw(self.pil_image)
 
-
     def __iter__(self):
         class _Pixels:
             def __init__(self, image: DreamImage):
@@ -133,6 +177,11 @@ class DreamImage:
 
         return _Pixels(self)
 
+    def convert(self, mode="RGB"):
+        if self.pil_image.mode == mode:
+            return self
+        return DreamImage(pil_image=self.pil_image.convert(mode))
+
     def create_tensor_image(self):
         return convertFromPILToTensorImage(self.pil_image)
 
@@ -175,6 +224,10 @@ class DreamImage:
     def save_jpg(self, filepath, quality=98):
         self.pil_image.save(filepath, quality=quality, optimize=True)
 
+    @classmethod
+    def from_file(cls, file_path):
+        return DreamImage(pil_image=Image.open(file_path))
+
 
 class DreamMask:
     def __init__(self, tensor_image=None, pil_image=None):
@@ -297,3 +350,52 @@ def hashed_as_strings(*items):
     m = hashlib.sha256()
     m.update(tokens.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 = 0.075
+        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

@@ -0,0 +1,212 @@
+from .categories import NodeCategories
+from .err import *
+from .shared import ALWAYS_CHANGED_FLAG, hashed_as_strings
+from .types import RGBPalette
+
+
+def _generate_switch_input(type: str):
+    d = dict()
+    for i in range(10):
+        d["input_" + str(i)] = (type,)
+    return {
+        "required": {
+            "select": ("INT", {"defualt": 0, "min": 0, "max": 9}),
+            "on_missing": (["previous", "next"],)
+        },
+        "optional": d
+    }
+
+
+def _do_pick(cls, select, 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:
+        select = (select + direction) % 10
+    return args["input_" + str(select)],
+
+
+class DreamBigImageSwitch:
+    _switch_type = "IMAGE"
+    NODE_NAME = "Big 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)
+
+    @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)
+
+
+class DreamBigLatentSwitch:
+    _switch_type = "LATENT"
+    NODE_NAME = "Big 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)
+
+    @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)
+
+
+class DreamBigTextSwitch:
+    _switch_type = "STRING"
+    NODE_NAME = "Big 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)
+
+    @classmethod
+    def IS_CHANGED(cls, *values):
+        return hashed_as_strings(values)
+
+    def pick(self, select, on_missing, **args):
+        return _do_pick(self.__class__, select, on_missing, **args)
+
+
+class DreamBigPaletteSwitch:
+    _switch_type = RGBPalette.ID
+    NODE_NAME = "Big 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)
+
+    @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)
+
+
+class DreamBigFloatSwitch:
+    _switch_type = "FLOAT"
+    NODE_NAME = "Big 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)
+
+    @classmethod
+    def IS_CHANGED(cls, *values):
+        return hashed_as_strings(values)
+
+    def pick(self, select, on_missing, **args):
+        return _do_pick(self.__class__, select, on_missing, **args)
+
+
+class DreamBigIntSwitch:
+    _switch_type = "INT"
+    NODE_NAME = "Big 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)
+
+    @classmethod
+    def IS_CHANGED(cls, *values):
+        return hashed_as_strings(values)
+
+    def pick(self, select, on_missing, **args):
+        return _do_pick(self.__class__, select, on_missing, **args)
+
+
+class DreamBoolToFloat:
+    NODE_NAME = "Boolean To Float"
+    ICON = "⬖"
+    CATEGORY = NodeCategories.UTILS_SWITCHES
+    RETURN_TYPES = ("FLOAT",)
+    RETURN_NAMES = ("result",)
+    FUNCTION = "pick"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "boolean": ("BOOLEAN", {"default": False}),
+                "on_true": ("FLOAT", {"default": 1.0}),
+                "on_false": ("FLOAT", {"default": 0.0})
+            }
+        }
+
+    @classmethod
+    def IS_CHANGED(cls, *values):
+        return hashed_as_strings(values)
+
+    def pick(self, boolean, on_true, on_false):
+        if boolean:
+            return (on_true,)
+        else:
+            return (on_false,)
+
+
+class DreamBoolToInt:
+    NODE_NAME = "Boolean To Int"
+    ICON = "⬖"
+    CATEGORY = NodeCategories.UTILS_SWITCHES
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("result",)
+    FUNCTION = "pick"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "boolean": ("BOOLEAN", {"default": False}),
+                "on_true": ("INT", {"default": 1}),
+                "on_false": ("INT", {"default": 0})
+            }
+        }
+
+    @classmethod
+    def IS_CHANGED(cls, *values):
+        return hashed_as_strings(values)
+
+    def pick(self, boolean, on_true, on_false):
+        if boolean:
+            return (on_true,)
+        else:
+            return (on_false,)

types.py

@@ -1,6 +1,10 @@
+# -*- coding: utf-8 -*-
+import random
+import time
+
 from typing import List, Dict
+
 from .shared import DreamImage
-import random, time
 
 
 class RGBPalette:
@@ -60,6 +64,52 @@ class RGBPalette:
         return _ColorIterator()
 
 
+class PartialPrompt:
+    ID = "PARTIAL_PROMPT"
+
+    def __init__(self):
+        self._data = {}
+
+    def add(self, text: str, weight: float):
+        output = PartialPrompt()
+        output._data = dict(self._data)
+        output._data[text.strip()] = weight
+        return output
+
+    def is_empty(self):
+        return not self._data
+
+    def abs_sum(self):
+        if not self._data:
+            return 0.0
+        return sum(map(abs, self._data.values()))
+
+    def abs_max(self):
+        if not self._data:
+            return 0.0
+        return max(map(abs, self._data.values()))
+
+    def scaled_by(self, f: float):
+        new_data = PartialPrompt()
+        new_data._data = dict(self._data)
+        for text, weight in new_data._data.items():
+            new_data._data[text] = weight * f
+        return new_data
+
+    def finalize(self, clamp: float):
+        items = self._data.items()
+        items = sorted(items, key=lambda pair: (pair[1], pair[0]))
+        pos = list()
+        neg = list()
+        for text, w in sorted(items, key=lambda pair: (-pair[1], pair[0])):
+            if w >= 0.0001:
+                pos.append("({}:{:.3f})".format(text, min(clamp, w)))
+        for text, w in sorted(items, key=lambda pair: (pair[1], pair[0])):
+            if w <= -0.0001:
+                neg.append("({}:{:.3f})".format(text, min(clamp, -w)))
+        return ", ".join(pos), ", ".join(neg)
+
+
 class FrameCounter:
     ID = "FRAME_COUNTER"
 
@@ -87,6 +137,14 @@ class FrameCounter:
     def current_time_in_seconds(self):
         return float(self.current_frame) / self.frames_per_second
 
+    @property
+    def total_time_in_seconds(self):
+        return float(self.total_frames) / self.frames_per_second
+
+    @property
+    def remaining_time_in_seconds(self):
+        return self.total_time_in_seconds - self.current_time_in_seconds
+
     @property
     def progress(self):
         return float(self.current_frame) / (max(2, self.total_frames) - 1)
@@ -123,6 +181,6 @@ class AnimationSequence:
 
 
 class SharedTypes:
-    frame_counter = {"frame_counter": (FrameCounter.ID, {"forceInput": True})}
-    sequence = {"sequence": (AnimationSequence.ID, {"forceInput": True})}
-    palette = {"palette": (RGBPalette.ID, {"forceInput": True})}
+    frame_counter = {"frame_counter": (FrameCounter.ID,)}
+    sequence = {"sequence": (AnimationSequence.ID,)}
+    palette = {"palette": (RGBPalette.ID,)}

uninstall.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 def run_uninstall():
     pass
 

utility.py

@@ -1,7 +1,9 @@
-from .shared import hashed_as_strings
-from .categories import NodeCategories
+# -*- coding: utf-8 -*-
 import math
 
+from .categories import NodeCategories
+from .shared import hashed_as_strings
+
 
 def _align_num(n: int, alignment: int, type: str):
     if alignment <= 1:
@@ -16,6 +18,8 @@ def _align_num(n: int, alignment: int, type: str):
 
 class DreamFrameDimensions:
     NODE_NAME = "Common Frame Dimensions"
+    ICON = "⌗"
+
     @classmethod
     def INPUT_TYPES(cls):
         return {
@@ -48,3 +52,4 @@ class DreamFrameDimensions:
             return (width, height, final_width, final_height)
         else:
             return (height, width, final_height, final_width)
+