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add sa solver and prototype instaflow

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Vladimir Mandic
2024-01-31 09:51:34 -05:00
parent b1ccadf793
commit 09d1f1e788
12 changed files with 108 additions and 86 deletions
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modules/sd_samplers_diffusers.py
+17 -9
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@@ -11,6 +11,7 @@ try:
from diffusers import (
DDIMScheduler,
DDPMScheduler,
UniPCMultistepScheduler,
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
@@ -19,10 +20,9 @@ try:
EulerDiscreteScheduler,
HeunDiscreteScheduler,
KDPM2DiscreteScheduler,
PNDMScheduler,
UniPCMultistepScheduler,
LMSDiscreteScheduler,
KDPM2AncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
LCMScheduler,
)
except Exception as e:
@@ -32,22 +32,22 @@ except Exception as e:
config = {
# beta_start, beta_end are typically per-scheduler, but we don't want them as they should be taken from the model itself as those are values model was trained on
# prediction_type is ideally set in model as well, but it maybe needed that we do auto-detect of model type in the future
'All': { 'num_train_timesteps': 500, 'beta_start': 0.0001, 'beta_end': 0.02, 'beta_schedule': 'linear', 'prediction_type': 'epsilon' },
'All': { 'num_train_timesteps': 1000, 'beta_start': 0.0001, 'beta_end': 0.02, 'beta_schedule': 'linear', 'prediction_type': 'epsilon' },
'DDIM': { 'clip_sample': True, 'set_alpha_to_one': True, 'steps_offset': 0, 'clip_sample_range': 1.0, 'sample_max_value': 1.0, 'timestep_spacing': 'linspace', 'rescale_betas_zero_snr': False },
'DDPM': { 'variance_type': "fixed_small", 'clip_sample': True, 'thresholding': False, 'clip_sample_range': 1.0, 'sample_max_value': 1.0, 'timestep_spacing': 'linspace'},
'UniPC': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'predict_x0': 'bh2', 'lower_order_final': True },
'DEIS': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "deis", 'solver_type': "logrho", 'lower_order_final': True },
'DPM++ 1S': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False },
'DPM++ 2M': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False },
'DPM 1S': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'final_sigmas_type': 'zero' },
'DPM 2M': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'final_sigmas_type': 'zero' },
'DPM SDE': { 'use_karras_sigmas': False },
'Euler a': { 'rescale_betas_zero_snr': False },
'Euler': { 'interpolation_type': "linear", 'use_karras_sigmas': False, 'rescale_betas_zero_snr': False },
'Heun': { 'use_karras_sigmas': False },
'DDPM': { 'variance_type': "fixed_small", 'clip_sample': True, 'thresholding': False, 'clip_sample_range': 1.0, 'sample_max_value': 1.0, 'timestep_spacing': 'linspace', 'rescale_betas_zero_snr': False },
'KDPM2': { 'steps_offset': 0 },
'KDPM2 a': { 'steps_offset': 0 },
'LMSD': { 'use_karras_sigmas': False, 'timestep_spacing': 'linspace', 'steps_offset': 0 },
'PNDM': { 'skip_prk_steps': False, 'set_alpha_to_one': False, 'steps_offset': 0 },
'UniPC': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'predict_x0': 'bh2', 'lower_order_final': True },
'LCM': { 'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': "scaled_linear", 'set_alpha_to_one': True, 'rescale_betas_zero_snr': False },
'LCM': { 'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': "scaled_linear", 'set_alpha_to_one': True, 'rescale_betas_zero_snr': False, 'thresholding': False },
}
samplers_data_diffusers = [
@@ -69,6 +69,14 @@ samplers_data_diffusers = [
sd_samplers_common.SamplerData('LCM', lambda model: DiffusionSampler('LCM', LCMScheduler, model), [], {}),
]
try:
from diffusers import SASolverScheduler
config['SA Solver'] = {'predictor_order': 2, 'corrector_order': 2, 'thresholding': False, 'lower_order_final': True, 'use_karras_sigmas': False, 'timestep_spacing': 'linspace'}
samplers_data_diffusers.append(sd_samplers_common.SamplerData('SA Solver', lambda model: DiffusionSampler('SA Solver', SASolverScheduler, model), [], {}))
except Exception as e:
shared.log.debug(f'Sampler: {e}')
class DiffusionSampler:
def __init__(self, name, constructor, model, **kwargs):
if name == 'Default':