fix: ✨ refactor

__init__.py

@@ -45,19 +45,15 @@ def extract_nodes_from_source(filename):
                 if isinstance(target, ast.Name) and target.id == "__nodes__":
                     value = ast.get_source_segment(source_code, node.value)
                     node_value = ast.parse(value).body[0].value
-                    if isinstance(node_value, ast.List) or isinstance(
-                        node_value, ast.Tuple
-                    ):
-                        for element in node_value.elts:
-                            if isinstance(element, ast.Name):
-                                print(element.id)
-                                nodes.append(element.id)
-
+                    if isinstance(node_value, (ast.List, ast.Tuple)):
+                        nodes.extend(
+                            element.id
+                            for element in node_value.elts
+                            if isinstance(element, ast.Name)
+                        )
                     break
     except SyntaxError:
         log.error("Failed to parse")
-        pass  # File couldn't be parsed
-
     return nodes
 
 
@@ -240,11 +236,10 @@ if hasattr(PromptServer, "instance"):
             log.setLevel(logging.DEBUG)
             log.debug("Debug mode set from API (/mtb/debug POST route)")
 
-        else:
-            if "MTB_DEBUG" in os.environ:
-                # del os.environ["MTB_DEBUG"]
-                os.environ.pop("MTB_DEBUG")
-                log.setLevel(logging.INFO)
+        elif "MTB_DEBUG" in os.environ:
+            # del os.environ["MTB_DEBUG"]
+            os.environ.pop("MTB_DEBUG")
+            log.setLevel(logging.INFO)
 
         return web.json_response(
             {"message": f"Debug mode {'set' if enabled else 'unset'}"}
@@ -258,7 +253,7 @@ if hasattr(PromptServer, "instance"):
         # Check if the request prefers HTML content
         if "text/html" in request.headers.get("Accept", ""):
             # # Return an HTML page
-            html_response = f"""
+            html_response = """
             <div class="flex-container menu">
                 <a href="/mtb/debug">debug</a>
                 <a href="/mtb/status">status</a>
@@ -277,9 +272,7 @@ if hasattr(PromptServer, "instance"):
         from . import endpoint
 
         reload(endpoint)
-        enabled = False
-        if "MTB_DEBUG" in os.environ:
-            enabled = True
+        enabled = "MTB_DEBUG" in os.environ
         # Check if the request prefers HTML content
         if "text/html" in request.headers.get("Accept", ""):
             # # Return an HTML page
@@ -299,7 +292,7 @@ if hasattr(PromptServer, "instance"):
         from . import endpoint
 
         if "text/html" in request.headers.get("Accept", ""):
-            html_response = f"""
+            html_response = """
             <h1>Actions has no get for now...</h1>
             """
             return web.Response(

log.py

@@ -3,7 +3,6 @@ import re
 import os
 
 base_log_level = logging.DEBUG if os.environ.get("MTB_DEBUG") else logging.INFO
-print(f"Log level: {base_log_level}")
 
 
 # Custom object that discards the output

nodes/graph_utils.py

@@ -5,7 +5,7 @@ import urllib.parse
 import torch
 import json
 from comfy.cli_args import args
-from ..utils import pil2tensor
+from ..utils import pil2tensor, apply_easing
 import io
 import numpy as np
 
@@ -40,7 +40,7 @@ class GetBatchFromHistory:
         }
 
     RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = "images"
+    RETURN_NAMES = ("images",)
     CATEGORY = "mtb/animation"
     FUNCTION = "load_from_history"
 
@@ -69,41 +69,32 @@ class GetBatchFromHistory:
         history = json.loads(response.read())
 
         output_images = []
-        for k, run in history.items():
-            for o in run["outputs"]:
-                for node_id in run["outputs"]:
-                    node_output = run["outputs"][node_id]
-                    if "images" in node_output:
-                        images_output = []
-                        for image in node_output["images"]:
-                            image_data = get_image(
-                                image["filename"], image["subfolder"], image["type"]
-                            )
-                            images_output.append(image_data)
-                        output_images.extend(images_output)
+
+        for run in history.values():
+            for node_output in run["outputs"].values():
+                if "images" in node_output:
+                    for image in node_output["images"]:
+                        image_data = get_image(
+                            image["filename"], image["subfolder"], image["type"]
+                        )
+                        output_images.append(image_data)
+
         if not output_images:
             return (torch.zeros(0),)
-        for i, image in enumerate(list(reversed(output_images))):
-            if i < offset:
-                continue
-            if i >= offset + count:
-                break
-            # Decode image as tensor
-            img = Image.open(image)
-            log.debug(f"Image from history {i} of shape {img.size}")
-            frames.append(img)
 
-            # Display the shape of the tensor
-            # print("Tensor shape:", image_tensor.shape)
+        # Directly get desired range of images
+        start_index = max(len(output_images) - offset - count, 0)
+        end_index = len(output_images) - offset
+        selected_images = output_images[start_index:end_index]
+
+        frames = [Image.open(image) for image in selected_images]
 
-            # return (output_images,)
         if not frames:
             return (torch.zeros(0),)
         elif len(frames) != count:
             log.warning(f"Expected {count} images, got {len(frames)} instead")
-        output = pil2tensor(
-            list(reversed(frames)),
-        )
+
+        output = pil2tensor(frames)
 
         return (output,)
 
@@ -181,6 +172,33 @@ class FitNumber:
                 "source_max": ("FLOAT", {"default": 1.0}),
                 "target_min": ("FLOAT", {"default": 0.0}),
                 "target_max": ("FLOAT", {"default": 1.0}),
+                "easing": (
+                    [
+                        "Linear",
+                        "Sine In",
+                        "Sine Out",
+                        "Sine In/Out",
+                        "Quart In",
+                        "Quart Out",
+                        "Quart In/Out",
+                        "Cubic In",
+                        "Cubic Out",
+                        "Cubic In/Out",
+                        "Circ In",
+                        "Circ Out",
+                        "Circ In/Out",
+                        "Back In",
+                        "Back Out",
+                        "Back In/Out",
+                        "Elastic In",
+                        "Elastic Out",
+                        "Elastic In/Out",
+                        "Bounce In",
+                        "Bounce Out",
+                        "Bounce In/Out",
+                    ],
+                    {"default": "Linear"},
+                ),
             }
         }
 
@@ -196,10 +214,14 @@ class FitNumber:
         source_max: float,
         target_min: float,
         target_max: float,
+        easing: str,
     ):
-        res = target_min + (target_max - target_min) * (value - source_min) / (
-            source_max - source_min
-        )
+        normalized_value = (value - source_min) / (source_max - source_min)
+
+        eased_value = apply_easing(normalized_value, easing)
+
+        # - Convert the eased value to the target range
+        res = target_min + (target_max - target_min) * eased_value
 
         if clamp:
             if target_min > target_max:

utils.py

@@ -4,30 +4,34 @@ import torch
 from pathlib import Path
 import sys
 from typing import List
-from .log import log
 import signal
 from contextlib import suppress
 from queue import Queue, Empty
 import subprocess
 import threading
 import os
+import math
 
-# - detect mode
-comfy_mode = None
-if os.environ.get("COLAB_GPU"):
-    comfy_mode = "colab"
-elif "python_embeded" in sys.executable:
-    comfy_mode = "embeded"
-elif ".venv" in sys.executable:
-    comfy_mode = "venv"
+try:
+    from .log import log
+except ImportError:
+    try:
+        from log import log
+
+        log.warn("Imported log without relative path")
+    except ImportError:
+        import logging
+
+        log = logging.getLogger("comfy mtb utils")
+        log.warn("[comfy mtb] You probably called the file outside a module.")
 
 
+# region MISC Utilities
 def hex_to_rgb(hex_color):
     hex_color = hex_color.lstrip("#")
     return tuple(int(hex_color[i : i + 2], 16) for i in (0, 2, 4))
 
 
-# region MISC Utilities
 def add_path(path, prepend=False):
     if isinstance(path, list):
         for p in path:
@@ -143,7 +147,17 @@ def import_install(package_name):
 
 # endregion
 
+
 # region GLOBAL VARIABLES
+# - detect mode
+comfy_mode = None
+if os.environ.get("COLAB_GPU"):
+    comfy_mode = "colab"
+elif "python_embeded" in sys.executable:
+    comfy_mode = "embeded"
+elif ".venv" in sys.executable:
+    comfy_mode = "venv"
+
 # - Get the absolute path of the parent directory of the current script
 here = Path(__file__).parent.resolve()
 
@@ -172,8 +186,6 @@ PIL_FILTER_MAP = {
     "bicubic": Image.Resampling.BICUBIC,
     "lanczos": Image.Resampling.LANCZOS,
 }
-
-
 # endregion
 
 
@@ -260,3 +272,194 @@ def download_antelopev2():
 
 
 # endregion
+
+
+# region UV Utilities
+
+
+def create_uv_map_tensor(width=512, height=512):
+    u = torch.linspace(0.0, 1.0, steps=width)
+    v = torch.linspace(0.0, 1.0, steps=height)
+
+    U, V = torch.meshgrid(u, v)
+
+    uv_map = torch.zeros(height, width, 3, dtype=torch.float32)
+    uv_map[:, :, 0] = U.t()
+    uv_map[:, :, 1] = V.t()
+
+    return uv_map.unsqueeze(0)
+
+
+# endregion
+
+
+# region ANIMATION Utilities
+def apply_easing(value, easing_type):
+    if value < 0 or value > 1:
+        raise ValueError("The value should be between 0 and 1.")
+
+    if easing_type == "Linear":
+        return value
+
+    # Back easing functions
+    def easeInBack(t):
+        s = 1.70158
+        return t * t * ((s + 1) * t - s)
+
+    def easeOutBack(t):
+        s = 1.70158
+        return ((t - 1) * t * ((s + 1) * t + s)) + 1
+
+    def easeInOutBack(t):
+        s = 1.70158 * 1.525
+        if t < 0.5:
+            return (t * t * (t * (s + 1) - s)) * 2
+        return ((t - 2) * t * ((s + 1) * t + s) + 2) * 2
+
+    # Elastic easing functions
+    def easeInElastic(t):
+        if t == 0:
+            return 0
+        if t == 1:
+            return 1
+        p = 0.3
+        s = p / 4
+        return -(math.pow(2, 10 * (t - 1)) * math.sin((t - 1 - s) * (2 * math.pi) / p))
+
+    def easeOutElastic(t):
+        if t == 0:
+            return 0
+        if t == 1:
+            return 1
+        p = 0.3
+        s = p / 4
+        return math.pow(2, -10 * t) * math.sin((t - s) * (2 * math.pi) / p) + 1
+
+    def easeInOutElastic(t):
+        if t == 0:
+            return 0
+        if t == 1:
+            return 1
+        p = 0.3 * 1.5
+        s = p / 4
+        t = t * 2
+        if t < 1:
+            return -0.5 * (
+                math.pow(2, 10 * (t - 1)) * math.sin((t - 1 - s) * (2 * math.pi) / p)
+            )
+        return (
+            0.5 * math.pow(2, -10 * (t - 1)) * math.sin((t - 1 - s) * (2 * math.pi) / p)
+            + 1
+        )
+
+    # Bounce easing functions
+    def easeInBounce(t):
+        return 1 - easeOutBounce(1 - t)
+
+    def easeOutBounce(t):
+        if t < (1 / 2.75):
+            return 7.5625 * t * t
+        elif t < (2 / 2.75):
+            t -= 1.5 / 2.75
+            return 7.5625 * t * t + 0.75
+        elif t < (2.5 / 2.75):
+            t -= 2.25 / 2.75
+            return 7.5625 * t * t + 0.9375
+        else:
+            t -= 2.625 / 2.75
+            return 7.5625 * t * t + 0.984375
+
+    def easeInOutBounce(t):
+        if t < 0.5:
+            return easeInBounce(t * 2) * 0.5
+        return easeOutBounce(t * 2 - 1) * 0.5 + 0.5
+
+    # Quart easing functions
+    def easeInQuart(t):
+        return t * t * t * t
+
+    def easeOutQuart(t):
+        t -= 1
+        return -(t**2 * t * t - 1)
+
+    def easeInOutQuart(t):
+        t *= 2
+        if t < 1:
+            return 0.5 * t * t * t * t
+        t -= 2
+        return -0.5 * (t**2 * t * t - 2)
+
+    # Cubic easing functions
+    def easeInCubic(t):
+        return t * t * t
+
+    def easeOutCubic(t):
+        t -= 1
+        return t**2 * t + 1
+
+    def easeInOutCubic(t):
+        t *= 2
+        if t < 1:
+            return 0.5 * t * t * t
+        t -= 2
+        return 0.5 * (t**2 * t + 2)
+
+    # Circ easing functions
+    def easeInCirc(t):
+        return -(math.sqrt(1 - t * t) - 1)
+
+    def easeOutCirc(t):
+        t -= 1
+        return math.sqrt(1 - t**2)
+
+    def easeInOutCirc(t):
+        t *= 2
+        if t < 1:
+            return -0.5 * (math.sqrt(1 - t**2) - 1)
+        t -= 2
+        return 0.5 * (math.sqrt(1 - t**2) + 1)
+
+    # Sine easing functions
+    def easeInSine(t):
+        return -math.cos(t * (math.pi / 2)) + 1
+
+    def easeOutSine(t):
+        return math.sin(t * (math.pi / 2))
+
+    def easeInOutSine(t):
+        return -0.5 * (math.cos(math.pi * t) - 1)
+
+    easing_functions = {
+        "Sine In": easeInSine,
+        "Sine Out": easeOutSine,
+        "Sine In/Out": easeInOutSine,
+        "Quart In": easeInQuart,
+        "Quart Out": easeOutQuart,
+        "Quart In/Out": easeInOutQuart,
+        "Cubic In": easeInCubic,
+        "Cubic Out": easeOutCubic,
+        "Cubic In/Out": easeInOutCubic,
+        "Circ In": easeInCirc,
+        "Circ Out": easeOutCirc,
+        "Circ In/Out": easeInOutCirc,
+        "Back In": easeInBack,
+        "Back Out": easeOutBack,
+        "Back In/Out": easeInOutBack,
+        "Elastic In": easeInElastic,
+        "Elastic Out": easeOutElastic,
+        "Elastic In/Out": easeInOutElastic,
+        "Bounce In": easeInBounce,
+        "Bounce Out": easeOutBounce,
+        "Bounce In/Out": easeInOutBounce,
+    }
+
+    function_ease = easing_functions.get(easing_type)
+    if function_ease:
+        return function_ease(value)
+
+    log.error(f"Unknown easing type: {easing_type}")
+    log.error(f"Available easing types: {list(easing_functions.keys())}")
+    raise ValueError(f"Unknown easing type: {easing_type}")
+
+
+# endregion