diff options
Diffstat (limited to 'modules/sd_samplers.py')
| -rw-r--r-- | modules/sd_samplers.py | 28 |
1 files changed, 19 insertions, 9 deletions
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 6e743f7e..20309e06 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -7,7 +7,7 @@ import inspect import k_diffusion.sampling
import ldm.models.diffusion.ddim
import ldm.models.diffusion.plms
-from modules import prompt_parser
+from modules import prompt_parser, devices, processing
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
@@ -57,7 +57,7 @@ def set_samplers(): global samplers, samplers_for_img2img
hidden = set(opts.hide_samplers)
- hidden_img2img = set(opts.hide_samplers + ['PLMS', 'DPM fast', 'DPM adaptive'])
+ hidden_img2img = set(opts.hide_samplers + ['PLMS'])
samplers = [x for x in all_samplers if x.name not in hidden]
samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
@@ -83,7 +83,7 @@ def setup_img2img_steps(p, steps=None): def sample_to_image(samples):
- x_sample = shared.sd_model.decode_first_stage(samples[0:1].type(shared.sd_model.dtype))[0]
+ x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0]
x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
@@ -365,16 +365,26 @@ class KDiffusionSampler: else:
sigmas = self.model_wrap.get_sigmas(steps)
- noise = noise * sigmas[steps - t_enc - 1]
- xi = x + noise
-
- extra_params_kwargs = self.initialize(p)
-
sigma_sched = sigmas[steps - t_enc - 1:]
+ xi = x + noise * sigma_sched[0]
+
+ extra_params_kwargs = self.initialize(p)
+ if 'sigma_min' in inspect.signature(self.func).parameters:
+ ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
+ extra_params_kwargs['sigma_min'] = sigma_sched[-2]
+ if 'sigma_max' in inspect.signature(self.func).parameters:
+ extra_params_kwargs['sigma_max'] = sigma_sched[0]
+ if 'n' in inspect.signature(self.func).parameters:
+ extra_params_kwargs['n'] = len(sigma_sched) - 1
+ if 'sigma_sched' in inspect.signature(self.func).parameters:
+ extra_params_kwargs['sigma_sched'] = sigma_sched
+ if 'sigmas' in inspect.signature(self.func).parameters:
+ extra_params_kwargs['sigmas'] = sigma_sched
self.model_wrap_cfg.init_latent = x
- return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+ return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
steps = steps or p.steps
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