diff options
Diffstat (limited to 'modules/sd_samplers.py')
| -rw-r--r-- | modules/sd_samplers.py | 19 |
1 files changed, 13 insertions, 6 deletions
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 01221b89..6261d1f7 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -138,9 +138,9 @@ def samples_to_image_grid(samples, approximation=None): def store_latent(decoded):
state.current_latent = decoded
- if opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
+ if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
if not shared.parallel_processing_allowed:
- shared.state.current_image = sample_to_image(decoded)
+ shared.state.assign_current_image(sample_to_image(decoded))
class InterruptedException(BaseException):
@@ -243,7 +243,7 @@ class VanillaStableDiffusionSampler: self.nmask = p.nmask if hasattr(p, 'nmask') else None
def adjust_steps_if_invalid(self, p, num_steps):
- if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
+ if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
valid_step = 999 / (1000 // num_steps)
if valid_step == floor(valid_step):
return int(valid_step) + 1
@@ -266,8 +266,7 @@ class VanillaStableDiffusionSampler: if image_conditioning is not None:
conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-
-
+
samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
return samples
@@ -352,6 +351,13 @@ class CFGDenoiser(torch.nn.Module): x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
+ devices.test_for_nans(x_out, "unet")
+
+ if opts.live_preview_content == "Prompt":
+ store_latent(x_out[0:uncond.shape[0]])
+ elif opts.live_preview_content == "Negative prompt":
+ store_latent(x_out[-uncond.shape[0]:])
+
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
if self.mask is not None:
@@ -423,7 +429,8 @@ class KDiffusionSampler: def callback_state(self, d):
step = d['i']
latent = d["denoised"]
- store_latent(latent)
+ if opts.live_preview_content == "Combined":
+ store_latent(latent)
self.last_latent = latent
if self.stop_at is not None and step > self.stop_at:
|