testing auto-update at comfyregistry

Amorano · May 22, 2024 · 8916ec5 · 8916ec5
1 parent b068126
commit 8916ec5
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Showing 4 changed files with 11 additions and 11 deletions.
diff --git a/core/calc.py b/core/calc.py
@@ -462,9 +462,9 @@ def run(self, **kw) -> Tuple[Any, Any]:
             # make sure we only interpolate between the longest "stride" we can
             size = min(3, max(len(A), len(B)))
             best_type = [EnumConvertType.FLOAT, EnumConvertType.VEC2, EnumConvertType.VEC3, EnumConvertType.VEC4][size]
-            A = parse_param(A, best_type, A)
-            B = parse_param(B, best_type, B)
-            alpha = parse_param(alpha, best_type, [alpha])
+            A = parse_value(A, best_type, A)
+            B = parse_value(B, best_type, B)
+            alpha = parse_value(alpha, best_type, [alpha])
             if op == "NONE":
                 val = [B[x] * alpha[x] + A[x] * (1 - alpha[x]) for x in range(size)]
             else:

diff --git a/core/create.py b/core/create.py
@@ -347,7 +347,7 @@ def INPUT_TYPES(cls) -> dict:
 
     def run(self, **kw) -> Tuple[torch.Tensor, torch.Tensor]:
         pA = parse_param(kw, Lexicon.PIXEL, EnumConvertType.IMAGE, None)
-        baseline = parse_param(kw, Lexicon.INT, 1, 0.1, EnumConvertType.FLOAT)
+        baseline = parse_param(kw, Lexicon.INT, EnumConvertType.FLOAT, 0, 0.1, 1)
         focal_length = parse_dynamic(Lexicon.VALUE, kw, EnumConvertType.FLOAT)
         images = []
         params = list(zip_longest_fill(pA, baseline, focal_length))

diff --git a/core/utility.py b/core/utility.py
@@ -373,10 +373,10 @@ def run(self, **kw) -> None:
         format = parse_param(kw, Lexicon.FORMAT, EnumConvertType.STRING, "gif")[0]
         overwrite = parse_param(kw, Lexicon.OVERWRITE, EnumConvertType.BOOLEAN, False)[0]
         optimize = parse_param(kw, Lexicon.OPTIMIZE, EnumConvertType.BOOLEAN, False)[0]
-        quality = parse_param(kw, Lexicon.QUALITY, 100, 0, EnumConvertType.INT, 1)[0]
-        motion = parse_param(kw, Lexicon.QUALITY_M, 100, 0, EnumConvertType.INT, 1)[0]
-        fps = parse_param(kw, Lexicon.FPS, 60, 24, EnumConvertType.INT, 1)[0]
-        loop = parse_param(kw, Lexicon.LOOP, 0, 0, EnumConvertType.INT)[0]
+        quality = parse_param(kw, Lexicon.QUALITY, EnumConvertType.INT, 100, 0, 1)[0]
+        motion = parse_param(kw, Lexicon.QUALITY_M, EnumConvertType.INT, 100, 0, 1)[0]
+        fps = parse_param(kw, Lexicon.FPS, EnumConvertType.INT, 60, 1, 24)[0]
+        loop = parse_param(kw, Lexicon.LOOP, EnumConvertType.INT, 0, 0)[0]
         output_dir = Path(output_dir)
         output_dir.mkdir(parents=True, exist_ok=True)
 
@@ -538,7 +538,7 @@ def run(self, **kw) -> Tuple[int, list]:
                 latents.append(False)
 
         if mode == EnumBatchMode.PICK:
-            index = parse_param(kw, Lexicon.BATCH_CHUNK, 0, 0, EnumConvertType.INT)
+            index = parse_param(kw, Lexicon.BATCH_CHUNK, EnumConvertType.INT, 0, 0)
             index = index if index < len(extract) else -1
             extract = [extract[index]]
             if latents[index]:
@@ -564,7 +564,7 @@ def run(self, **kw) -> Tuple[int, list]:
             if latents[idx]:
                 extract = {"samples": extract}
         elif mode == EnumBatchMode.INDEX_LIST:
-            indices = parse_param(kw, Lexicon.STRING, ", "", EnumConvertType.STRING).split(")
+            indices = parse_param(kw, Lexicon.STRING, EnumConvertType.STRING, "")
             data = [extract[i:j] for i, j in zip([0]+indices, indices+[None])]
             latents = [latents[i:j] for i, j in zip([0]+indices, indices+[None])]
             extract = []

diff --git a/pyproject.toml b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "jovimetrix"
 description = "Compose like Substance Designer. Webcams, Media Streams (in/out), Tick animation, Color correction, Geometry manipulation, Pixel shader, Polygonal shape generator, Remap images gometry and color, Heavily inspired by WAS and MTB Node Suites."
-version = "1.0.0"
+version = "1.0.1"
 license = "LICENSE"
 dependencies = ["torch", "numpy", "matplotlib", "opencv-contrib-python", "ffmpeg-python", "librosa", "loguru", "moderngl", "mss", "requests", "Pillow", "pywin32==306; platform_system==\"Windows\"", "scikit-image", "blendmodes", "mido[ports-rtmidi]", "pyaudio", "daltonlens", "numba", "PyOpenGL", "PyOpenGL-accelerate", "SpoutGL; platform_system==\"Windows\"", "vnoise", "stereoscopy[auto_align]", "aenum<4,>=3.1.15"]