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using global sigmas for better results

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lvmin
2023-10-14 06:06:34 -07:00
parent e27383c07f
commit b3d662054f
4 changed files with 22 additions and 8 deletions
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modules/default_pipeline.py
+10
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@@ -1,6 +1,7 @@
import modules.core as core
import os
import torch
import modules.patch
import modules.path
import fcbh.model_management
import fcbh.latent_formats
@@ -331,6 +332,15 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
print(f'[Sampler] refiner_swap_method = {refiner_swap_method}')
minmax_sigmas = calculate_sigmas(sampler=sampler_name, scheduler=scheduler_name, model=final_unet.model, steps=steps, denoise=denoise)
sigma_min, sigma_max = minmax_sigmas[minmax_sigmas > 0].min(), minmax_sigmas.max()
sigma_min = float(sigma_min.cpu().numpy())
sigma_max = float(sigma_max.cpu().numpy())
print(f'[Sampler] sigma_min = {sigma_min}, sigma_max = {sigma_max}')
modules.patch.sigma_min = sigma_min
modules.patch.sigma_max = sigma_max
if latent is None:
empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=1)
else: