mirror of
https://github.com/vladmandic/automatic
synced 2026-09-18 16:54:33 +02:00
diffuser sampler settings
This commit is contained in:
Vladimir Mandic
@@ -474,7 +474,7 @@ class SdModelData:
|
||||
elif shared.backend == shared.Backend.DIFFUSERS:
|
||||
load_diffuser()
|
||||
else:
|
||||
shared.log.error(f"Unknown Stable Diffusion backend: {shared.opts.sd_backend}")
|
||||
shared.log.error(f"Unknown Stable Diffusion backend: {shared.backend}")
|
||||
self.initial = False
|
||||
except Exception as e:
|
||||
shared.log.error("Failed to load stable diffusion model")
|
||||
|
||||
@@ -7,7 +7,7 @@ from diffusers import (
|
||||
EulerAncestralDiscreteScheduler,
|
||||
EulerDiscreteScheduler,
|
||||
HeunDiscreteScheduler,
|
||||
KDPM2DiscreteScheduler,
|
||||
# KDPM2DiscreteScheduler,
|
||||
PNDMScheduler,
|
||||
UniPCMultistepScheduler,
|
||||
)
|
||||
@@ -15,41 +15,57 @@ 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 },
|
||||
'UniPC': { 'solver_order': 2, 'thresholding': False, '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, 'clip_sample_range': 1.0, 'sample_max_value': 1.0, 'timestep_spacing': 'leading', '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 },
|
||||
'DEIS': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "deis", 'solver_type': "logrho", 'lower_order_final': True },
|
||||
'Euler': { 'interpolation_type': "linear", 'use_karras_sigmas': False },
|
||||
'Euler a': {},
|
||||
'Heun': { 'use_karras_sigmas': False },
|
||||
'PNDM': { 'skip_prk_steps': False, 'set_alpha_to_one': False, 'steps_offset': 0 },
|
||||
'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 },
|
||||
}
|
||||
|
||||
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 1S', lambda model: DiffusionSampler('DPM++ 1S', DPMSolverSinglestepScheduler, model), [], {}),
|
||||
sd_samplers_common.SamplerData('DPM 2M', lambda model: DiffusionSampler('DPM++ 2M', DPMSolverMultistepScheduler, model), [], {}),
|
||||
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('PNDM', lambda model: DiffusionSampler('PNDM', PNDMScheduler, model), [], {}),
|
||||
]
|
||||
|
||||
class DiffusionSampler:
|
||||
def __init__(self, name, constructor, model, **kwargs):
|
||||
from modules.shared import opts, log
|
||||
self.config = config['All'].copy()
|
||||
for key, value in config.get(name, {}).items(): # diffusers defaults
|
||||
if key in self.config:
|
||||
self.config[key] = value
|
||||
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
|
||||
if opts.schedulers_prediction_type != 'default':
|
||||
self.config['prediction_type'] = opts.schedulers_prediction_type
|
||||
if opts.schedulers_beta_schedule != 'default':
|
||||
self.config['beta_schedule'] = opts.schedulers_beta_schedule
|
||||
if 'use_karras_sigmas' in self.config:
|
||||
self.config['use_karras_sigmas'] = opts.schedulers_use_karras
|
||||
if 'thresholding' in self.config:
|
||||
self.config['thresholding'] = opts.schedulers_use_thresholding
|
||||
if 'lower_order_final' in self.config:
|
||||
self.config['lower_order_final'] = opts.schedulers_use_loworder
|
||||
if 'solver_order' in self.config:
|
||||
self.config['solver_order'] = opts.schedulers_solver_order
|
||||
if name.startswith('DPM'):
|
||||
self.config['algorithm_type'] = opts.schedulers_dpm_solver
|
||||
self.sampler = constructor(**self.config)
|
||||
self.sampler.name = name
|
||||
log.debug(f'Diffusers sampler: {name} {self.config}')
|
||||
|
||||
+31
-19
@@ -188,6 +188,8 @@ class State:
|
||||
|
||||
state = State()
|
||||
state.server_start = time.time()
|
||||
backend = Backend.DIFFUSERS if cmd_opts.backend.lower() == 'diffusers' else Backend.ORIGINAL
|
||||
log.info(f'Pipeline: {backend}')
|
||||
|
||||
|
||||
class OptionInfo:
|
||||
@@ -470,23 +472,37 @@ options_templates.update(options_section(('sampler-params', "Sampler Settings"),
|
||||
"show_samplers": OptionInfo(["Euler a", "UniPC", "DDIM", "DPM++ 2M SDE", "DPM++ 2M SDE Karras", "DPM2 Karras", "DPM++ 2M Karras", "DEIS"], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers() if x.name != "PLMS"]}),
|
||||
"fallback_sampler": OptionInfo("Euler a", "Secondary sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
|
||||
"force_latent_sampler": OptionInfo("None", "Force latent upscaler sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
|
||||
"always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching enabled on low memory systems"),
|
||||
"enable_quantization": OptionInfo(True, "Enable samplers quantization for sharper and cleaner results"),
|
||||
"eta_ancestral": OptionInfo(1.0, "Noise multiplier for ancestral samplers (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"eta_ddim": OptionInfo(0.0, "Noise multiplier for DDIM (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad']}),
|
||||
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_min_uncond': OptionInfo(0, "sigma negative guidance minimum ", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
|
||||
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
'eta_noise_seed_delta': OptionInfo(0, "Noise seed delta (eta)", gr.Number, {"precision": 0}),
|
||||
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
|
||||
'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
|
||||
'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
|
||||
'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
|
||||
'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
|
||||
}))
|
||||
|
||||
if backend == Backend.ORIGINAL:
|
||||
options_templates.update(options_section(('sampler-params', "Sampler Settings"), {
|
||||
"always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching enabled on low memory systems"),
|
||||
"enable_quantization": OptionInfo(True, "Enable samplers quantization for sharper and cleaner results"),
|
||||
"eta_ancestral": OptionInfo(1.0, "Noise multiplier for ancestral samplers (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"eta_ddim": OptionInfo(0.0, "Noise multiplier for DDIM (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad']}),
|
||||
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_min_uncond': OptionInfo(0, "sigma negative guidance minimum ", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
|
||||
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
'eta_noise_seed_delta': OptionInfo(0, "Noise seed delta (eta)", gr.Number, {"precision": 0}),
|
||||
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
|
||||
'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
|
||||
'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
|
||||
'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
|
||||
'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
|
||||
}))
|
||||
elif backend == Backend.DIFFUSERS:
|
||||
options_templates.update(options_section(('sampler-params', "Sampler Settings"), {
|
||||
"schedulers_prediction_type": OptionInfo("default", "Samplers override model prediction type", gr.Radio, lambda: {"choices": ['default', 'epsilon', 'sample', 'v-prediction']}),
|
||||
"schedulers_beta_schedule": OptionInfo("default", "Samplers override beta schedule", gr.Radio, lambda: {"choices": ['default', 'linear', 'scaled_linear', 'squaredcos_cap_v2']}),
|
||||
"schedulers_solver_order": OptionInfo(2, "Samplers solver order where applicable", gr.Slider, {"minimum": 1, "maximum": 5, "step": 1}),
|
||||
"schedulers_use_karras": OptionInfo(True, "Samplers should use Karras sigmas where applicable"),
|
||||
"schedulers_use_loworder": OptionInfo(True, "Samplers should use use lower-order solvers in the final steps where applicable"),
|
||||
"schedulers_use_thresholding": OptionInfo(False, "Samplers should use dynamic thresholding where applicable"),
|
||||
"schedulers_dpm_solver": OptionInfo("sde-dpmsolver++", "Samplers DPM solver algorithm", gr.Radio, lambda: {"choices": ['dpmsolver', 'dpmsolver++', 'sde-dpmsolver++']}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('postprocessing', "Postprocessing"), {
|
||||
'postprocessing_enable_in_main_ui': OptionInfo([], "Enable addtional postprocessing operations", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
|
||||
'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
|
||||
@@ -701,10 +717,6 @@ opts = Options()
|
||||
config_filename = cmd_opts.config
|
||||
opts.load(config_filename)
|
||||
cmd_opts = cmd_args.compatibility_args(opts, cmd_opts)
|
||||
if cmd_opts.backend:
|
||||
opts.data['sd_backend'] = cmd_opts.backend.lower()
|
||||
backend = Backend.DIFFUSERS if opts.sd_backend == 'diffusers' else Backend.ORIGINAL
|
||||
log.info(f'Pipeline: {opts.sd_backend}')
|
||||
|
||||
prompt_styles = modules.styles.StyleDatabase(opts.styles_dir)
|
||||
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or (cmd_opts.server_name or False)) and not cmd_opts.insecure
|
||||
|
||||
Reference in New Issue
Block a user