diff options
Diffstat (limited to 'modules/sd_samplers_kdiffusion.py')
| -rw-r--r-- | modules/sd_samplers_kdiffusion.py | 14 |
1 files changed, 8 insertions, 6 deletions
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 6a54ce32..17d24df4 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -116,11 +116,13 @@ class CFGDenoiser(torch.nn.Module): tensor = denoiser_params.text_cond
uncond = denoiser_params.text_uncond
- sigma_thresh = s_min_uncond
- if(torch.dot(sigma,sigma) < sigma.shape[0] * (sigma_thresh*sigma_thresh) and not is_edit_model):
- 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]
+ 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]
if tensor.shape[1] == uncond.shape[1]:
if not is_edit_model:
@@ -159,7 +161,7 @@ class CFGDenoiser(torch.nn.Module): devices.test_for_nans(x_out, "unet")
if opts.live_preview_content == "Prompt":
- sd_samplers_common.store_latent(x_out[0:uncond.shape[0]])
+ sd_samplers_common.store_latent(x_out[0:x_out.shape[0]-uncond.shape[0]])
elif opts.live_preview_content == "Negative prompt":
sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
|