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rework Negative Guidance minimum sigma to work with AND, add infotext and copypaste parameters support

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AUTOMATIC
2023-04-29 15:57:09 +03:00
parent 3591eefedf
commit 1d11e89698
4 changed files with 30 additions and 20 deletions
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43
modules/sd_samplers_kdiffusion.py
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@@ -115,20 +115,21 @@ class CFGDenoiser(torch.nn.Module):
sigma_in = denoiser_params.sigma
tensor = denoiser_params.text_cond
uncond = denoiser_params.text_uncond
skip_uncond = False
if self.step % 2 and s_min_uncond > 0 and not is_edit_model:
# alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
sigma_threshold = s_min_uncond
if(torch.dot(sigma,sigma) < sigma.shape[0] * (sigma_threshold*sigma_threshold) ):
uncond = torch.zeros([0,0,uncond.shape[2]])
x_in=x_in[:x_in.shape[0]//2]
sigma_in=sigma_in[:sigma_in.shape[0]//2]
# alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
skip_uncond = True
x_in = x_in[:-batch_size]
sigma_in = sigma_in[:-batch_size]
if tensor.shape[1] == uncond.shape[1]:
if not is_edit_model:
cond_in = torch.cat([tensor, uncond])
else:
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
if is_edit_model:
cond_in = torch.cat([tensor, uncond, uncond])
elif skip_uncond:
cond_in = tensor
else:
cond_in = torch.cat([tensor, uncond])
if shared.batch_cond_uncond:
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
@@ -152,9 +153,15 @@ class CFGDenoiser(torch.nn.Module):
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
if uncond.shape[0]:
if not skip_uncond:
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
if skip_uncond:
#x_out = torch.cat([x_out, x_out[0:batch_size]]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
denoised_image_indexes = [x[0][0] for x in conds_list]
fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
x_out = torch.cat([x_out, fake_uncond])
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
cfg_denoised_callback(denoised_params)
@@ -165,13 +172,12 @@ class CFGDenoiser(torch.nn.Module):
elif opts.live_preview_content == "Negative prompt":
sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
if not is_edit_model:
if uncond.shape[0]:
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
else:
denoised = x_out
else:
if is_edit_model:
denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
elif skip_uncond:
denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
else:
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
if self.mask is not None:
denoised = self.init_latent * self.mask + self.nmask * denoised
@@ -221,6 +227,7 @@ class KDiffusionSampler:
self.eta = None
self.config = None
self.last_latent = None
self.s_min_uncond = None
self.conditioning_key = sd_model.model.conditioning_key