diff options
Diffstat (limited to 'modules/sd_samplers_common.py')
| -rw-r--r-- | modules/sd_samplers_common.py | 25 |
1 files changed, 18 insertions, 7 deletions
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index 40c7aae0..07fc4434 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -92,7 +92,15 @@ def images_tensor_to_samples(image, approximation=None, model=None): model = shared.sd_model
image = image.to(shared.device, dtype=devices.dtype_vae)
image = image * 2 - 1
- x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
+ if len(image) > 1:
+ x_latent = torch.stack([
+ model.get_first_stage_encoding(
+ model.encode_first_stage(torch.unsqueeze(img, 0))
+ )[0]
+ for img in image
+ ])
+ else:
+ x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
return x_latent
@@ -145,7 +153,7 @@ def apply_refiner(cfg_denoiser): refiner_switch_at = cfg_denoiser.p.refiner_switch_at
refiner_checkpoint_info = cfg_denoiser.p.refiner_checkpoint_info
- if refiner_switch_at is not None and completed_ratio <= refiner_switch_at:
+ if refiner_switch_at is not None and completed_ratio < refiner_switch_at:
return False
if refiner_checkpoint_info is None or shared.sd_model.sd_checkpoint_info == refiner_checkpoint_info:
@@ -276,19 +284,19 @@ class Sampler: s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
s_noise = getattr(opts, 's_noise', p.s_noise)
- if s_churn != self.s_churn:
+ if 's_churn' in extra_params_kwargs and s_churn != self.s_churn:
extra_params_kwargs['s_churn'] = s_churn
p.s_churn = s_churn
p.extra_generation_params['Sigma churn'] = s_churn
- if s_tmin != self.s_tmin:
+ if 's_tmin' in extra_params_kwargs and s_tmin != self.s_tmin:
extra_params_kwargs['s_tmin'] = s_tmin
p.s_tmin = s_tmin
p.extra_generation_params['Sigma tmin'] = s_tmin
- if s_tmax != self.s_tmax:
+ if 's_tmax' in extra_params_kwargs and s_tmax != self.s_tmax:
extra_params_kwargs['s_tmax'] = s_tmax
p.s_tmax = s_tmax
p.extra_generation_params['Sigma tmax'] = s_tmax
- if s_noise != self.s_noise:
+ if 's_noise' in extra_params_kwargs and s_noise != self.s_noise:
extra_params_kwargs['s_noise'] = s_noise
p.s_noise = s_noise
p.extra_generation_params['Sigma noise'] = s_noise
@@ -305,5 +313,8 @@ class Sampler: current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
+ def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ raise NotImplementedError()
-
+ def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ raise NotImplementedError()
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