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integrate the new samplers PR

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AUTOMATIC
2022-10-06 14:12:52 +03:00
parent a971e4a767
commit 5993df24a1
5 changed files with 36 additions and 87 deletions
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59
modules/sd_samplers.py
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@@ -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)