nodes/mcmonkeyprojects-sd-dynamic-thresholding
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add experiment mode 3 (rgb-approx assisted latent hacking)

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Alex "mcmonkey" Goodwin
2023-02-08 17:49:45 -08:00
parent ff14beafd3
commit 0e324249e0
1 changed files with 21 additions and 0 deletions
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scripts/dynamic_thresholding.py
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@@ -175,6 +175,7 @@ class CustomCFGDenoiser(sd_samplers_kdiffusion.CFGDenoiser):
### Now add it back onto the averages to get into real scale again and return
result = cfg_renormalized + cfg_means
actualRes = result.unflatten(2, mim_target.shape[2:])
if self.experiment_mode == 1:
num = actualRes.cpu().numpy()
for y in range(0, 64):
@@ -198,4 +199,24 @@ class CustomCFGDenoiser(sd_samplers_kdiffusion.CFGDenoiser):
for z in range(0, 4):
num[0][z][y][x] *= 0.7
actualRes = torch.from_numpy(num).to(device=uncond.device)
elif self.experiment_mode == 3:
coefs = torch.tensor([
# R G B W
[0.298, 0.207, 0.208, 0.0], # L1
[0.187, 0.286, 0.173, 0.0], # L2
[-0.158, 0.189, 0.264, 0.0], # L3
[-0.184, -0.271, -0.473, 1.0], # L4
], device=uncond.device)
resRGB = torch.einsum("laxy,ab -> lbxy", actualRes, coefs)
maxR, maxG, maxB, maxW = resRGB[0][0].max(), resRGB[0][1].max(), resRGB[0][2].max(), resRGB[0][3].max()
maxRGB = max(maxR, maxG, maxB)
print(f"test max = r={maxR}, g={maxG}, b={maxB}, w={maxW}, rgb={maxRGB}")
if self.step / (self.maxSteps - 1) > 0.2:
if maxRGB < 2.0 and maxW < 3.0:
resRGB /= maxRGB / 2.4
else:
if maxRGB > 2.4 and maxW > 3.0:
resRGB /= maxRGB / 2.4
actualRes = torch.einsum("laxy,ab -> lbxy", resRGB, coefs.inverse())
return actualRes