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self.scheduler.config
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+178
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‎examples/community/adaptive_mask_inpainting.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1148,7 +1148,7 @@ def __call__(
11481148
# run segmentation
11491149
if use_adaptive_mask:
11501150
if enforce_full_mask_ratio > 0.0:
1151-
use_default_mask = t < self.scheduler.config.num_train_timesteps * enforce_full_mask_ratio
1151+
use_default_mask = t < self.scheduler._schedule.num_train_timesteps * enforce_full_mask_ratio
11521152
elif enforce_full_mask_ratio == 0.0:
11531153
use_default_mask = False
11541154
else:

‎examples/community/edict_pipeline.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -97,7 +97,7 @@ def noise_step(
9797
model_output: torch.Tensor,
9898
timestep: torch.Tensor,
9999
):
100-
prev_timestep = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps
100+
prev_timestep = timestep - self.scheduler._schedule.num_train_timesteps / self.scheduler.num_inference_steps
101101

102102
alpha_prod_t, beta_prod_t = self._get_alpha_and_beta(timestep)
103103
alpha_prod_t_prev, beta_prod_t_prev = self._get_alpha_and_beta(prev_timestep)
@@ -116,7 +116,7 @@ def denoise_step(
116116
model_output: torch.Tensor,
117117
timestep: torch.Tensor,
118118
):
119-
prev_timestep = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps
119+
prev_timestep = timestep - self.scheduler._schedule.num_train_timesteps / self.scheduler.num_inference_steps
120120

121121
alpha_prod_t, beta_prod_t = self._get_alpha_and_beta(timestep)
122122
alpha_prod_t_prev, beta_prod_t_prev = self._get_alpha_and_beta(prev_timestep)

‎examples/community/lpw_stable_diffusion_xl.py‎

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -1050,8 +1050,8 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
10501050
if denoising_start is not None:
10511051
discrete_timestep_cutoff = int(
10521052
round(
1053-
self.scheduler.config.num_train_timesteps
1054-
- (denoising_start * self.scheduler.config.num_train_timesteps)
1053+
self.scheduler._schedule.num_train_timesteps
1054+
- (denoising_start * self.scheduler._schedule.num_train_timesteps)
10551055
)
10561056
)
10571057

@@ -1819,8 +1819,8 @@ def denoising_value_valid(dnv):
18191819
elif self.denoising_end is not None and denoising_value_valid(self.denoising_end):
18201820
discrete_timestep_cutoff = int(
18211821
round(
1822-
self.scheduler.config.num_train_timesteps
1823-
- (self.denoising_end * self.scheduler.config.num_train_timesteps)
1822+
self.scheduler._schedule.num_train_timesteps
1823+
- (self.denoising_end * self.scheduler._schedule.num_train_timesteps)
18241824
)
18251825
)
18261826
num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))

‎examples/community/masked_stable_diffusion_xl_img2img.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -376,8 +376,8 @@ def denoising_value_valid(dnv):
376376
elif self.denoising_end is not None and denoising_value_valid(self.denoising_end):
377377
discrete_timestep_cutoff = int(
378378
round(
379-
self.scheduler.config.num_train_timesteps
380-
- (self.denoising_end * self.scheduler.config.num_train_timesteps)
379+
self.scheduler._schedule.num_train_timesteps
380+
- (self.denoising_end * self.scheduler._schedule.num_train_timesteps)
381381
)
382382
)
383383
num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))

‎examples/community/pipeline_demofusion_sdxl.py‎

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -933,8 +933,8 @@ def __call__(
933933
if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:
934934
discrete_timestep_cutoff = int(
935935
round(
936-
self.scheduler.config.num_train_timesteps
937-
- (denoising_end * self.scheduler.config.num_train_timesteps)
936+
self.scheduler._schedule.num_train_timesteps
937+
- (denoising_end * self.scheduler._schedule.num_train_timesteps)
938938
)
939939
)
940940
num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
@@ -1040,8 +1040,8 @@ def __call__(
10401040
1
10411041
+ torch.cos(
10421042
torch.pi
1043-
* (self.scheduler.config.num_train_timesteps - t)
1044-
/ self.scheduler.config.num_train_timesteps
1043+
* (self.scheduler._schedule.num_train_timesteps - t)
1044+
/ self.scheduler._schedule.num_train_timesteps
10451045
)
10461046
).cpu()
10471047
)

‎examples/community/pipeline_flux_differential_img2img.py‎

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -876,10 +876,10 @@ def __call__(
876876
image_seq_len = (int(height) // self.vae_scale_factor) * (int(width) // self.vae_scale_factor)
877877
mu = calculate_shift(
878878
image_seq_len,
879-
self.scheduler.config.base_image_seq_len,
880-
self.scheduler.config.max_image_seq_len,
881-
self.scheduler.config.base_shift,
882-
self.scheduler.config.max_shift,
879+
self.scheduler._schedule.base_image_seq_len,
880+
self.scheduler._schedule.max_image_seq_len,
881+
self.scheduler._schedule.base_shift,
882+
self.scheduler._schedule.max_shift,
883883
)
884884
timesteps, num_inference_steps = retrieve_timesteps(
885885
self.scheduler,

‎examples/community/pipeline_flux_rf_inversion.py‎

Lines changed: 8 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -822,10 +822,10 @@ def __call__(
822822
image_seq_len = (int(height) // self.vae_scale_factor // 2) * (int(width) // self.vae_scale_factor // 2)
823823
mu = calculate_shift(
824824
image_seq_len,
825-
self.scheduler.config.base_image_seq_len,
826-
self.scheduler.config.max_image_seq_len,
827-
self.scheduler.config.base_shift,
828-
self.scheduler.config.max_shift,
825+
self.scheduler._schedule.base_image_seq_len,
826+
self.scheduler._schedule.max_image_seq_len,
827+
self.scheduler._schedule.base_shift,
828+
self.scheduler._schedule.max_shift,
829829
)
830830
timesteps, num_inference_steps = retrieve_timesteps(
831831
self.scheduler,
@@ -992,10 +992,10 @@ def invert(
992992
image_seq_len = (int(height) // self.vae_scale_factor // 2) * (int(width) // self.vae_scale_factor // 2)
993993
mu = calculate_shift(
994994
image_seq_len,
995-
self.scheduler.config.base_image_seq_len,
996-
self.scheduler.config.max_image_seq_len,
997-
self.scheduler.config.base_shift,
998-
self.scheduler.config.max_shift,
995+
self.scheduler._schedule.base_image_seq_len,
996+
self.scheduler._schedule.max_image_seq_len,
997+
self.scheduler._schedule.base_shift,
998+
self.scheduler._schedule.max_shift,
999999
)
10001000
timesteps, num_inversion_steps = retrieve_timesteps(
10011001
self.scheduler,

‎examples/community/pipeline_flux_with_cfg.py‎

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -757,10 +757,10 @@ def __call__(
757757
image_seq_len = latents.shape[1]
758758
mu = calculate_shift(
759759
image_seq_len,
760-
self.scheduler.config.base_image_seq_len,
761-
self.scheduler.config.max_image_seq_len,
762-
self.scheduler.config.base_shift,
763-
self.scheduler.config.max_shift,
760+
self.scheduler._schedule.base_image_seq_len,
761+
self.scheduler._schedule.max_image_seq_len,
762+
self.scheduler._schedule.base_shift,
763+
self.scheduler._schedule.max_shift,
764764
)
765765
timesteps, num_inference_steps = retrieve_timesteps(
766766
self.scheduler,

‎examples/community/pipeline_kolors_differential_img2img.py‎

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -580,8 +580,8 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
580580
if denoising_start is not None:
581581
discrete_timestep_cutoff = int(
582582
round(
583-
self.scheduler.config.num_train_timesteps
584-
- (denoising_start * self.scheduler.config.num_train_timesteps)
583+
self.scheduler._schedule.num_train_timesteps
584+
- (denoising_start * self.scheduler._schedule.num_train_timesteps)
585585
)
586586
)
587587

@@ -1159,8 +1159,8 @@ def denoising_value_valid(dnv):
11591159
elif self.denoising_end is not None and denoising_value_valid(self.denoising_end):
11601160
discrete_timestep_cutoff = int(
11611161
round(
1162-
self.scheduler.config.num_train_timesteps
1163-
- (self.denoising_end * self.scheduler.config.num_train_timesteps)
1162+
self.scheduler._schedule.num_train_timesteps
1163+
- (self.denoising_end * self.scheduler._schedule.num_train_timesteps)
11641164
)
11651165
)
11661166
num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))

‎examples/community/pipeline_null_text_inversion.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -87,7 +87,7 @@ def latent2image(self, latents):
8787
return image
8888

8989
def prev_step(self, model_output, timestep, sample):
90-
prev_timestep = timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
90+
prev_timestep = timestep - self.scheduler._schedule.num_train_timesteps // self.scheduler.num_inference_steps
9191
alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
9292
alpha_prod_t_prev = (
9393
self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
@@ -100,7 +100,7 @@ def prev_step(self, model_output, timestep, sample):
100100

101101
def next_step(self, model_output, timestep, sample):
102102
timestep, next_timestep = (
103-
min(timestep - self.scheduler.config.num_train_timesteps // self.num_inference_steps, 999),
103+
min(timestep - self.scheduler._schedule.num_train_timesteps // self.num_inference_steps, 999),
104104
timestep,
105105
)
106106
alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod

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