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Organized the settings and UI of soft inpainting to allow for toggling the feature, and centralizes default values to reduce the amount of copy-pasta.

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CodeHatchling
2023-12-04 01:27:22 -07:00
parent 552f8bc832
commit aaacf48232
9 changed files with 197 additions and 49 deletions
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35
modules/sd_samplers_cfg_denoiser.py
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@@ -6,6 +6,7 @@ import modules.shared as shared
from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
import modules.soft_inpainting as si
def catenate_conds(conds):
@@ -43,9 +44,7 @@ class CFGDenoiser(torch.nn.Module):
self.model_wrap = None
self.mask = None
self.nmask = None
self.mask_blend_power = 1
self.mask_blend_scale = 0.5
self.inpaint_detail_preservation = 4
self.soft_inpainting: si.SoftInpaintingParameters = None
self.init_latent = None
self.steps = None
"""number of steps as specified by user in UI"""
@@ -95,7 +94,8 @@ class CFGDenoiser(torch.nn.Module):
self.sampler.sampler_extra_args['uncond'] = uc
def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
def latent_blend(a, b, t):
def latent_blend(a, b, t, one_minus_t=None):
"""
Interpolates two latent image representations according to the parameter t,
where the interpolated vectors' magnitudes are also interpolated separately.
@@ -104,7 +104,11 @@ class CFGDenoiser(torch.nn.Module):
"""
# NOTE: We use inplace operations wherever possible.
one_minus_t = 1 - t
if one_minus_t is None:
one_minus_t = 1 - t
if self.soft_inpainting is None:
return a * one_minus_t + b * t
# Linearly interpolate the image vectors.
a_scaled = a * one_minus_t
@@ -119,10 +123,10 @@ class CFGDenoiser(torch.nn.Module):
current_magnitude = torch.norm(image_interp, p=2, dim=1).to(torch.float64).add_(0.00001)
# Interpolate the powered magnitudes, then un-power them (bring them back to a power of 1).
a_magnitude = torch.norm(a, p=2, dim=1).to(torch.float64).pow_(self.inpaint_detail_preservation) * one_minus_t
b_magnitude = torch.norm(b, p=2, dim=1).to(torch.float64).pow_(self.inpaint_detail_preservation) * t
a_magnitude = torch.norm(a, p=2, dim=1).to(torch.float64).pow_(self.soft_inpainting.inpaint_detail_preservation) * one_minus_t
b_magnitude = torch.norm(b, p=2, dim=1).to(torch.float64).pow_(self.soft_inpainting.inpaint_detail_preservation) * t
desired_magnitude = a_magnitude
desired_magnitude.add_(b_magnitude).pow_(1 / self.inpaint_detail_preservation)
desired_magnitude.add_(b_magnitude).pow_(1 / self.soft_inpainting.inpaint_detail_preservation)
del a_magnitude, b_magnitude, one_minus_t
# Change the linearly interpolated image vectors' magnitudes to the value we want.
@@ -156,7 +160,10 @@ class CFGDenoiser(torch.nn.Module):
NOTE: "mask" is not used
"""
return torch.pow(nmask, (_sigma ** self.mask_blend_power) * self.mask_blend_scale)
if self.soft_inpainting is None:
return nmask
return torch.pow(nmask, (_sigma ** self.soft_inpainting.mask_blend_power) * self.soft_inpainting.mask_blend_scale)
if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException
@@ -176,7 +183,10 @@ class CFGDenoiser(torch.nn.Module):
# Blend in the original latents (before)
if self.mask_before_denoising and self.mask is not None:
x = latent_blend(self.init_latent, x, get_modified_nmask(self.nmask, sigma))
if self.soft_inpainting is None:
x = latent_blend(self.init_latent, x, self.nmask, self.mask)
else:
x = latent_blend(self.init_latent, x, get_modified_nmask(self.nmask, sigma))
batch_size = len(conds_list)
repeats = [len(conds_list[i]) for i in range(batch_size)]
@@ -279,7 +289,10 @@ class CFGDenoiser(torch.nn.Module):
# Blend in the original latents (after)
if not self.mask_before_denoising and self.mask is not None:
denoised = latent_blend(self.init_latent, denoised, get_modified_nmask(self.nmask, sigma))
if self.soft_inpainting is None:
denoised = latent_blend(self.init_latent, denoised, self.nmask, self.mask)
else:
denoised = latent_blend(self.init_latent, denoised, get_modified_nmask(self.nmask, sigma))
self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)