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sd-dynamic-thresholding
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remove python 3.10 code, for #1

pull/9/head
Alex "mcmonkey" Goodwin 2023-01-28 21:46:25 -08:00
parent 3fe3e96543
commit 4a36b4e7ac
2 changed files with 21 additions and 26 deletions
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1
.gitignore vendored Normal file
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@ -0,0 +1 @@
__pycache__/

46
scripts/dynamic_thresholding.py
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@ -103,32 +103,26 @@ class CustomCFGDenoiser(sd_samplers.CFGDenoiser):
def dynthresh(self, cond, uncond, cfgScale, conds_list):
mimicScale = self.mimic_scale
match self.mimic_mode:
case "Constant":
pass
case "Linear Down":
mimicScale *= 1.0 - (self.step / self.maxSteps)
case "Cosine Down":
mimicScale *= 1.0 - math.cos(self.step / self.maxSteps)
case "Linear Up":
mimicScale *= self.step / self.maxSteps
pass
case "Cosine Up":
mimicScale *= math.cos(self.step / self.maxSteps)
pass
match self.cfg_mode:
case "Constant":
pass
case "Linear Down":
cfgScale *= 1.0 - (self.step / self.maxSteps)
case "Cosine Down":
cfgScale *= 1.0 - math.cos(self.step / self.maxSteps)
case "Linear Up":
cfgScale *= self.step / self.maxSteps
pass
case "Cosine Up":
cfgScale *= math.cos(self.step / self.maxSteps)
pass
if self.mimic_mode == "Constant":
pass
elif self.mimic_mode == "Linear Down":
mimicScale *= 1.0 - (self.step / self.maxSteps)
elif self.mimic_mode == "Cosine Down":
mimicScale *= 1.0 - math.cos(self.step / self.maxSteps)
elif self.mimic_mode == "Linear Up":
mimicScale *= self.step / self.maxSteps
elif self.mimic_mode == "Cosine Up":
mimicScale *= math.cos(self.step / self.maxSteps)
if self.cfg_mode == "Constant":
pass
elif self.cfg_mode == "Linear Down":
cfgScale *= 1.0 - (self.step / self.maxSteps)
elif self.cfg_mode == "Cosine Down":
cfgScale *= 1.0 - math.cos(self.step / self.maxSteps)
elif self.cfg_mode == "Linear Up":
cfgScale *= self.step / self.maxSteps
elif self.cfg_mode == "Cosine Up":
cfgScale *= math.cos(self.step / self.maxSteps)
# uncond shape is (batch, 4, height, width)
conds_per_batch = cond.shape[0] / uncond.shape[0]
assert conds_per_batch == int(conds_per_batch), "Expected # of conds per batch to be constant across batches"