Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-19 01:04:32 +02:00
Code Issues Packages Projects Releases Wiki Activity

redo diffusers scheduler

Browse Source
This commit is contained in:
Vladimir Mandic
2023-07-04 13:07:05 -04:00
parent b216a35ddd
commit 18ef9e6fd7
11 changed files with 69 additions and 91 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/sd_samplers_diffusers.py
+31 -13
View File
@@ -13,25 +13,43 @@ from diffusers import (
)
from modules import sd_samplers_common
config = {
'All': { 'num_train_timesteps': 1000, 'beta_start': 0.0001, 'beta_end': 0.02, 'beta_schedule': 'linear', 'prediction_type': 'epsilon' },
'UniPC': { 'solver_order': 2, 'thresholding': False, 'dynamic_thresholding_ratio': 0.995, 'sample_max_value': 1.0, 'predict_x0': 'bh2', 'lower_order_final': True },
'DDIM': { 'clip_sample': True, 'set_alpha_to_one': True, 'steps_offset': 0, 'thresholding': False, 'dynamic_thresholding_ratio': 0.995, 'clip_sample_range': 1.0, 'sample_max_value': 1.0, 'timestep_spacing': 'leading', 'rescale_betas_zero_snr': False },
'DEIS': { 'solver_order': 2, 'thresholding': False, 'dynamic_thresholding_ratio': 0.995, 'sample_max_value': 1.0, 'algorithm_type': "deis", 'solver_type': "logrho", 'lower_order_final': True },
'Euler a': {},
}
samplers_data_diffusers = [
sd_samplers_common.SamplerData('UniPC', lambda model: DiffusionSampler('UniPC', UniPCMultistepScheduler, model), [], {}),
sd_samplers_common.SamplerData('DDIM', lambda model: DiffusionSampler('DDIM', DDIMScheduler, model), [], {}),
sd_samplers_common.SamplerData('DDPM', lambda model: DiffusionSampler('DDPM', DDPMScheduler, model), [], {}),
# sd_samplers_common.SamplerData('DDPM', lambda model: DiffusionSampler('DDPM', DDPMScheduler, model), [], {}),
sd_samplers_common.SamplerData('DEIS', lambda model: DiffusionSampler('DEIS', DEISMultistepScheduler, model), [], {}),
sd_samplers_common.SamplerData('DPM++ 2M', lambda model: DiffusionSampler('DPM++ 2M', DPMSolverMultistepScheduler, model), [], {}),
sd_samplers_common.SamplerData('DPM++ 1S', lambda model: DiffusionSampler('DPM++ 1S', DPMSolverSinglestepScheduler, model), [], {}),
sd_samplers_common.SamplerData('DPM++ 2M SDE', lambda model: DiffusionSampler('DPM++ 2M SDE', DPMSolverMultistepScheduler, model, algorithm_type="sde-dpmsolver++"), [], {}),
sd_samplers_common.SamplerData('DPM++ 2M Karras', lambda model: DiffusionSampler('DPM++ 2M Karras', DPMSolverMultistepScheduler, model, use_karras_sigmas=True), [], {}),
sd_samplers_common.SamplerData('DPM++ 1S Karras', lambda model: DiffusionSampler('DPM++ 1S Karras', DPMSolverSinglestepScheduler, model, use_karras_sigmas=True), [], {}),
sd_samplers_common.SamplerData('DPM++ 2M SDE Karras', lambda model: DiffusionSampler('DPM++ 2M SDE Karras', DPMSolverMultistepScheduler, model, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++"), [], {}),
sd_samplers_common.SamplerData('Euler', lambda model: DiffusionSampler('Euler', EulerDiscreteScheduler, model), [], {}),
# sd_samplers_common.SamplerData('DPM++ 2M', lambda model: DiffusionSampler('DPM++ 2M', DPMSolverMultistepScheduler, model), [], {}),
# sd_samplers_common.SamplerData('DPM++ 1S', lambda model: DiffusionSampler('DPM++ 1S', DPMSolverSinglestepScheduler, model), [], {}),
# sd_samplers_common.SamplerData('DPM++ 2M SDE', lambda model: DiffusionSampler('DPM++ 2M SDE', DPMSolverMultistepScheduler, model, algorithm_type="sde-dpmsolver++"), [], {}),
# sd_samplers_common.SamplerData('DPM++ 2M Karras', lambda model: DiffusionSampler('DPM++ 2M Karras', DPMSolverMultistepScheduler, model, use_karras_sigmas=True), [], {}),
# sd_samplers_common.SamplerData('DPM++ 1S Karras', lambda model: DiffusionSampler('DPM++ 1S Karras', DPMSolverSinglestepScheduler, model, use_karras_sigmas=True), [], {}),
# sd_samplers_common.SamplerData('DPM++ 2M SDE Karras', lambda model: DiffusionSampler('DPM++ 2M SDE Karras', DPMSolverMultistepScheduler, model, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++"), [], {}),
# sd_samplers_common.SamplerData('Euler', lambda model: DiffusionSampler('Euler', EulerDiscreteScheduler, model), [], {}),
sd_samplers_common.SamplerData('Euler a', lambda model: DiffusionSampler('Euler a', EulerAncestralDiscreteScheduler, model), [], {}),
sd_samplers_common.SamplerData('Heun', lambda model: DiffusionSampler('Heun', HeunDiscreteScheduler, model), [], {}),
sd_samplers_common.SamplerData('DPM2++ 2M', lambda model: DiffusionSampler('KDPM2', KDPM2DiscreteScheduler, model), [], {}),
sd_samplers_common.SamplerData('PNDM', lambda model: DiffusionSampler('PNDM', PNDMScheduler, model), [], {}),
# sd_samplers_common.SamplerData('Heun', lambda model: DiffusionSampler('Heun', HeunDiscreteScheduler, model), [], {}),
# sd_samplers_common.SamplerData('DPM2++ 2M', lambda model: DiffusionSampler('KDPM2', KDPM2DiscreteScheduler, model), [], {}),
# sd_samplers_common.SamplerData('PNDM', lambda model: DiffusionSampler('PNDM', PNDMScheduler, model), [], {}),
]
class DiffusionSampler:
def __init__(self, name, constructor, sd_model, **kwargs):
self.sampler = constructor.from_pretrained(sd_model, subfolder="scheduler", **kwargs)
def __init__(self, name, constructor, model, **kwargs):
self.config = config['All'].copy()
for key, value in config.get(name, {}).items(): # diffusers defaults
if key in self.config:
self.config[key] = value
for key, value in model.scheduler.config.items(): # model defaults
if key in self.config:
self.config[key] = value
for key, value in kwargs.items(): # user args
if key in self.config:
self.config[key] = value
self.sampler = constructor(**self.config)
self.sampler.name = name