diff options
Diffstat (limited to 'modules/sd_samplers.py')
| -rw-r--r-- | modules/sd_samplers.py | 59 |
1 files changed, 31 insertions, 28 deletions
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 8d6eb762..497df943 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -13,46 +13,46 @@ from modules.shared import opts, cmd_opts, state import modules.shared as shared
-SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases'])
+SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
samplers_k_diffusion = [
- ('Euler a', 'sample_euler_ancestral', ['k_euler_a']),
- ('Euler', 'sample_euler', ['k_euler']),
- ('LMS', 'sample_lms', ['k_lms']),
- ('Heun', 'sample_heun', ['k_heun']),
- ('DPM2', 'sample_dpm_2', ['k_dpm_2']),
- ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']),
- ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast']),
- ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']),
+ ('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}),
+ ('Euler', 'sample_euler', ['k_euler'], {}),
+ ('LMS', 'sample_lms', ['k_lms'], {}),
+ ('Heun', 'sample_heun', ['k_heun'], {}),
+ ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
+ ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
+ ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
+ ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
+ ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
+ ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
+ ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
]
-if opts.show_karras_scheduler_variants:
- k_diffusion.sampling.sample_dpm_2_ka = k_diffusion.sampling.sample_dpm_2
- k_diffusion.sampling.sample_dpm_2_ancestral_ka = k_diffusion.sampling.sample_dpm_2_ancestral
- k_diffusion.sampling.sample_lms_ka = k_diffusion.sampling.sample_lms
- samplers_k_diffusion_ka = [
- ('LMS K Scheduling', 'sample_lms_ka', ['k_lms_ka']),
- ('DPM2 K Scheduling', 'sample_dpm_2_ka', ['k_dpm_2_ka']),
- ('DPM2 a K Scheduling', 'sample_dpm_2_ancestral_ka', ['k_dpm_2_a_ka']),
- ]
- samplers_k_diffusion.extend(samplers_k_diffusion_ka)
-
samplers_data_k_diffusion = [
- SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases)
- for label, funcname, aliases in samplers_k_diffusion
+ SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
+ for label, funcname, aliases, options in samplers_k_diffusion
if hasattr(k_diffusion.sampling, funcname)
]
all_samplers = [
*samplers_data_k_diffusion,
- SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),
- SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
+ SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
+ SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
]
samplers = []
samplers_for_img2img = []
+def create_sampler_with_index(list_of_configs, index, model):
+ config = list_of_configs[index]
+ sampler = config.constructor(model)
+ sampler.config = config
+
+ return sampler
+
+
def set_samplers():
global samplers, samplers_for_img2img
@@ -130,6 +130,7 @@ class VanillaStableDiffusionSampler: self.step = 0
self.eta = None
self.default_eta = 0.0
+ self.config = None
def number_of_needed_noises(self, p):
return 0
@@ -291,6 +292,7 @@ class KDiffusionSampler: self.stop_at = None
self.eta = None
self.default_eta = 1.0
+ self.config = None
def callback_state(self, d):
store_latent(d["denoised"])
@@ -355,11 +357,12 @@ class KDiffusionSampler: steps = steps or p.steps
if p.sampler_noise_scheduler_override:
- sigmas = p.sampler_noise_scheduler_override(steps)
- elif self.funcname.endswith('ka'):
- sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
+ sigmas = p.sampler_noise_scheduler_override(steps)
+ elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
+ sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
else:
- sigmas = self.model_wrap.get_sigmas(steps)
+ sigmas = self.model_wrap.get_sigmas(steps)
+
x = x * sigmas[0]
extra_params_kwargs = self.initialize(p)
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