fix: 🐛 fix from merge
This commit is contained in:
melMass
@@ -7,6 +7,7 @@ import sys
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from typing import List, Optional
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from pytoshop.user import nested_layers
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from pytoshop import enums
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# from pytoshop.layers import LayerMask, LayerRecord
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from .log import log
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from typing import List
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@@ -475,241 +476,6 @@ def apply_easing(value, easing_type):
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# endregion
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# endregion
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# region MODEL Utilities
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def download_antelopev2():
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antelopev2_url = "https://drive.google.com/uc?id=18wEUfMNohBJ4K3Ly5wpTejPfDzp-8fI8"
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try:
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import gdown
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import folder_paths
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log.debug("Loading antelopev2 model")
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dest = Path(folder_paths.models_dir) / "insightface"
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archive = dest / "antelopev2.zip"
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final_path = dest / "models" / "antelopev2"
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if not final_path.exists():
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log.info(f"antelopev2 not found, downloading to {dest}")
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gdown.download(
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antelopev2_url,
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archive.as_posix(),
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resume=True,
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)
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log.info(f"Unzipping antelopev2 to {final_path}")
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if archive.exists():
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# we unzip it
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import zipfile
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with zipfile.ZipFile(archive.as_posix(), "r") as zip_ref:
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zip_ref.extractall(final_path.parent.as_posix())
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except Exception as e:
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log.error(
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f"Could not load or download antelopev2 model, download it manually from {antelopev2_url}"
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)
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raise e
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# endregion
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# region UV Utilities
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def create_uv_map_tensor(width=512, height=512):
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u = torch.linspace(0.0, 1.0, steps=width)
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v = torch.linspace(0.0, 1.0, steps=height)
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U, V = torch.meshgrid(u, v)
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uv_map = torch.zeros(height, width, 3, dtype=torch.float32)
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uv_map[:, :, 0] = U.t()
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uv_map[:, :, 1] = V.t()
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return uv_map.unsqueeze(0)
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# endregion
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# region ANIMATION Utilities
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def apply_easing(value, easing_type):
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if value < 0 or value > 1:
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raise ValueError("The value should be between 0 and 1.")
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if easing_type == "Linear":
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return value
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# Back easing functions
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def easeInBack(t):
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s = 1.70158
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return t * t * ((s + 1) * t - s)
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def easeOutBack(t):
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s = 1.70158
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return ((t - 1) * t * ((s + 1) * t + s)) + 1
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def easeInOutBack(t):
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s = 1.70158 * 1.525
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if t < 0.5:
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return (t * t * (t * (s + 1) - s)) * 2
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return ((t - 2) * t * ((s + 1) * t + s) + 2) * 2
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# Elastic easing functions
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def easeInElastic(t):
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if t == 0:
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return 0
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if t == 1:
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return 1
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p = 0.3
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s = p / 4
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return -(math.pow(2, 10 * (t - 1)) * math.sin((t - 1 - s) * (2 * math.pi) / p))
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def easeOutElastic(t):
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if t == 0:
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return 0
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if t == 1:
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return 1
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p = 0.3
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s = p / 4
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return math.pow(2, -10 * t) * math.sin((t - s) * (2 * math.pi) / p) + 1
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def easeInOutElastic(t):
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if t == 0:
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return 0
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if t == 1:
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return 1
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p = 0.3 * 1.5
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s = p / 4
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t = t * 2
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if t < 1:
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return -0.5 * (
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math.pow(2, 10 * (t - 1)) * math.sin((t - 1 - s) * (2 * math.pi) / p)
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)
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return (
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0.5 * math.pow(2, -10 * (t - 1)) * math.sin((t - 1 - s) * (2 * math.pi) / p)
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+ 1
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)
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# Bounce easing functions
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def easeInBounce(t):
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return 1 - easeOutBounce(1 - t)
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def easeOutBounce(t):
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if t < (1 / 2.75):
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return 7.5625 * t * t
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elif t < (2 / 2.75):
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t -= 1.5 / 2.75
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return 7.5625 * t * t + 0.75
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elif t < (2.5 / 2.75):
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t -= 2.25 / 2.75
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return 7.5625 * t * t + 0.9375
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else:
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t -= 2.625 / 2.75
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return 7.5625 * t * t + 0.984375
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def easeInOutBounce(t):
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if t < 0.5:
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return easeInBounce(t * 2) * 0.5
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return easeOutBounce(t * 2 - 1) * 0.5 + 0.5
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# Quart easing functions
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def easeInQuart(t):
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return t * t * t * t
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def easeOutQuart(t):
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t -= 1
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return -(t**2 * t * t - 1)
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def easeInOutQuart(t):
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t *= 2
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if t < 1:
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return 0.5 * t * t * t * t
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t -= 2
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return -0.5 * (t**2 * t * t - 2)
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# Cubic easing functions
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def easeInCubic(t):
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return t * t * t
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def easeOutCubic(t):
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t -= 1
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return t**2 * t + 1
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def easeInOutCubic(t):
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t *= 2
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if t < 1:
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return 0.5 * t * t * t
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t -= 2
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return 0.5 * (t**2 * t + 2)
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# Circ easing functions
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def easeInCirc(t):
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return -(math.sqrt(1 - t * t) - 1)
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def easeOutCirc(t):
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t -= 1
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return math.sqrt(1 - t**2)
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def easeInOutCirc(t):
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t *= 2
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if t < 1:
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return -0.5 * (math.sqrt(1 - t**2) - 1)
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t -= 2
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return 0.5 * (math.sqrt(1 - t**2) + 1)
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# Sine easing functions
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def easeInSine(t):
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return -math.cos(t * (math.pi / 2)) + 1
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def easeOutSine(t):
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return math.sin(t * (math.pi / 2))
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def easeInOutSine(t):
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return -0.5 * (math.cos(math.pi * t) - 1)
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easing_functions = {
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"Sine In": easeInSine,
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"Sine Out": easeOutSine,
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"Sine In/Out": easeInOutSine,
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"Quart In": easeInQuart,
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"Quart Out": easeOutQuart,
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"Quart In/Out": easeInOutQuart,
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"Cubic In": easeInCubic,
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"Cubic Out": easeOutCubic,
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"Cubic In/Out": easeInOutCubic,
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"Circ In": easeInCirc,
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"Circ Out": easeOutCirc,
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"Circ In/Out": easeInOutCirc,
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"Back In": easeInBack,
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"Back Out": easeOutBack,
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"Back In/Out": easeInOutBack,
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"Elastic In": easeInElastic,
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"Elastic Out": easeOutElastic,
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"Elastic In/Out": easeInOutElastic,
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"Bounce In": easeInBounce,
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"Bounce Out": easeOutBounce,
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"Bounce In/Out": easeInOutBounce,
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}
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function_ease = easing_functions.get(easing_type)
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if function_ease:
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return function_ease(value)
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log.error(f"Unknown easing type: {easing_type}")
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log.error(f"Available easing types: {list(easing_functions.keys())}")
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raise ValueError(f"Unknown easing type: {easing_type}")
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# endregion
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def tensor2pytolayer(
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tensor: torch.Tensor,
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name: str,
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@@ -723,29 +489,27 @@ def tensor2pytolayer(
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metadata: dict = {},
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layer_color=0,
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color_mode=None,
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mask: Optional[torch.Tensor] = None, # Add the mask parameter with default value as None
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mask: Optional[
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torch.Tensor
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] = None, # Add the mask parameter with default value as None
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) -> nested_layers.Image:
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batch_count = 1
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if len(tensor.shape) > 3:
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batch_count = tensor.size(0)
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batch_count = tensor.size(0) if len(tensor.shape) > 3 else 1
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if batch_count > 1:
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raise Exception(
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raise ValueError(
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f"Only one image is supported (batch size is currently {batch_count})"
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)
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out_channels = tensor2pil(tensor)[0]
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arr = np.array(out_channels)
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# If a mask is provided, convert it to numpy array
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# If a mask is provided, convert it to numpy array
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if mask is not None:
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mask_arr = np.array(tensor2pil(mask)[0])
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else:
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mask_arr = np.full_like(arr, 255, dtype=np.uint8)
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channels = [arr[:, :, 0], arr[:, :, 1], arr[:, :, 2], mask_arr[:, :, 0]]
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image = nested_layers.Image(
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image = nested_layers.Image(
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name=name,
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visible=visible,
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opacity=opacity,
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@@ -759,5 +523,4 @@ def tensor2pytolayer(
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color_mode=color_mode,
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)
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return image
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return image
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