diff options
Diffstat (limited to 'modules/sd_samplers_kdiffusion.py')
| -rw-r--r-- | modules/sd_samplers_kdiffusion.py | 382 |
1 files changed, 74 insertions, 308 deletions
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 5552a8dc..8a8c87e0 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -1,47 +1,60 @@ -from collections import deque
import torch
import inspect
import k_diffusion.sampling
-from modules import prompt_parser, devices, sd_samplers_common
+from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser
+from modules.sd_samplers_cfg_denoiser import CFGDenoiser # noqa: F401
+from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
-from modules.shared import opts, state
+from modules.shared import opts
import modules.shared as shared
-from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
-from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
-from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
samplers_k_diffusion = [
+ ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+ ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
+ ('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}),
+ ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
('Euler', 'sample_euler', ['k_euler'], {}),
('LMS', 'sample_lms', ['k_lms'], {}),
('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
- ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
- ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
+ ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True, "second_order": True}),
+ ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
+ ('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun'], {"brownian_noise": True, "solver_type": "heun"}),
+ ('DPM++ 2M SDE Heun Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_ka'], {'scheduler': 'karras', "brownian_noise": True, "solver_type": "heun"}),
+ ('DPM++ 2M SDE Heun Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_exp'], {'scheduler': 'exponential', "brownian_noise": True, "solver_type": "heun"}),
+ ('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {'discard_next_to_last_sigma': True, "brownian_noise": True}),
+ ('DPM++ 3M SDE Karras', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
+ ('DPM++ 3M SDE Exponential', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_exp'], {'scheduler': 'exponential', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
- ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
- ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
- ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
+ ('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras', "second_order": True}),
]
+
samplers_data_k_diffusion = [
sd_samplers_common.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)
+ if callable(funcname) or hasattr(k_diffusion.sampling, funcname)
]
sampler_extra_params = {
'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+ 'sample_dpm_fast': ['s_noise'],
+ 'sample_dpm_2_ancestral': ['s_noise'],
+ 'sample_dpmpp_2s_ancestral': ['s_noise'],
+ 'sample_dpmpp_sde': ['s_noise'],
+ 'sample_dpmpp_2m_sde': ['s_noise'],
+ 'sample_dpmpp_3m_sde': ['s_noise'],
}
k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
@@ -53,289 +66,27 @@ k_diffusion_scheduler = { }
-def catenate_conds(conds):
- if not isinstance(conds[0], dict):
- return torch.cat(conds)
-
- return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
-
-
-def subscript_cond(cond, a, b):
- if not isinstance(cond, dict):
- return cond[a:b]
-
- return {key: vec[a:b] for key, vec in cond.items()}
-
-
-def pad_cond(tensor, repeats, empty):
- if not isinstance(tensor, dict):
- return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
-
- tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
- return tensor
-
-
-class CFGDenoiser(torch.nn.Module):
- """
- Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
- that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
- instead of one. Originally, the second prompt is just an empty string, but we use non-empty
- negative prompt.
- """
-
- def __init__(self, model):
- super().__init__()
- self.inner_model = model
- self.mask = None
- self.nmask = None
- self.init_latent = None
- self.step = 0
- self.image_cfg_scale = None
- self.padded_cond_uncond = False
-
- def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
- denoised_uncond = x_out[-uncond.shape[0]:]
- denoised = torch.clone(denoised_uncond)
-
- for i, conds in enumerate(conds_list):
- for cond_index, weight in conds:
- denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
-
- return denoised
-
- def combine_denoised_for_edit_model(self, x_out, cond_scale):
- out_cond, out_img_cond, out_uncond = x_out.chunk(3)
- denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
-
- return denoised
-
- def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
- if state.interrupted or state.skipped:
- raise sd_samplers_common.InterruptedException
-
- # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
- # so is_edit_model is set to False to support AND composition.
- is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
-
- conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
- uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
-
- assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
-
- batch_size = len(conds_list)
- repeats = [len(conds_list[i]) for i in range(batch_size)]
-
- if shared.sd_model.model.conditioning_key == "crossattn-adm":
- image_uncond = torch.zeros_like(image_cond)
- make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm}
- else:
- image_uncond = image_cond
- if isinstance(uncond, dict):
- make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
- else:
- make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]}
-
- if not is_edit_model:
- x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
- sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
- image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
- else:
- x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
- sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
- image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
-
- denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
- cfg_denoiser_callback(denoiser_params)
- x_in = denoiser_params.x
- image_cond_in = denoiser_params.image_cond
- sigma_in = denoiser_params.sigma
- tensor = denoiser_params.text_cond
- uncond = denoiser_params.text_uncond
- skip_uncond = False
-
- # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
- if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
- skip_uncond = True
- x_in = x_in[:-batch_size]
- sigma_in = sigma_in[:-batch_size]
-
- self.padded_cond_uncond = False
- if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
- empty = shared.sd_model.cond_stage_model_empty_prompt
- num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
-
- if num_repeats < 0:
- tensor = pad_cond(tensor, -num_repeats, empty)
- self.padded_cond_uncond = True
- elif num_repeats > 0:
- uncond = pad_cond(uncond, num_repeats, empty)
- self.padded_cond_uncond = True
-
- if tensor.shape[1] == uncond.shape[1] or skip_uncond:
- if is_edit_model:
- cond_in = catenate_conds([tensor, uncond, uncond])
- elif skip_uncond:
- cond_in = tensor
- else:
- cond_in = catenate_conds([tensor, uncond])
-
- if shared.batch_cond_uncond:
- x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
- else:
- x_out = torch.zeros_like(x_in)
- for batch_offset in range(0, x_out.shape[0], batch_size):
- a = batch_offset
- b = a + batch_size
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
- else:
- x_out = torch.zeros_like(x_in)
- batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
- for batch_offset in range(0, tensor.shape[0], batch_size):
- a = batch_offset
- b = min(a + batch_size, tensor.shape[0])
-
- if not is_edit_model:
- c_crossattn = subscript_cond(tensor, a, b)
- else:
- c_crossattn = torch.cat([tensor[a:b]], uncond)
-
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
-
- if not skip_uncond:
- x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
+class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):
+ @property
+ def inner_model(self):
+ if self.model_wrap is None:
+ denoiser = k_diffusion.external.CompVisVDenoiser if shared.sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
+ self.model_wrap = denoiser(shared.sd_model, quantize=shared.opts.enable_quantization)
- denoised_image_indexes = [x[0][0] for x in conds_list]
- if skip_uncond:
- fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
- x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
+ return self.model_wrap
- denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
- cfg_denoised_callback(denoised_params)
- devices.test_for_nans(x_out, "unet")
-
- if opts.live_preview_content == "Prompt":
- sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
- elif opts.live_preview_content == "Negative prompt":
- sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
-
- if is_edit_model:
- denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
- elif skip_uncond:
- denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
- else:
- denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
-
- if self.mask is not None:
- denoised = self.init_latent * self.mask + self.nmask * denoised
-
- after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
- cfg_after_cfg_callback(after_cfg_callback_params)
- denoised = after_cfg_callback_params.x
-
- self.step += 1
- return denoised
-
-
-class TorchHijack:
- def __init__(self, sampler_noises):
- # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
- # implementation.
- self.sampler_noises = deque(sampler_noises)
-
- def __getattr__(self, item):
- if item == 'randn_like':
- return self.randn_like
-
- if hasattr(torch, item):
- return getattr(torch, item)
-
- raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
-
- def randn_like(self, x):
- if self.sampler_noises:
- noise = self.sampler_noises.popleft()
- if noise.shape == x.shape:
- return noise
-
- if opts.randn_source == "CPU" or x.device.type == 'mps':
- return torch.randn_like(x, device=devices.cpu).to(x.device)
- else:
- return torch.randn_like(x)
+class KDiffusionSampler(sd_samplers_common.Sampler):
+ def __init__(self, funcname, sd_model, options=None):
+ super().__init__(funcname)
+ self.extra_params = sampler_extra_params.get(funcname, [])
-class KDiffusionSampler:
- def __init__(self, funcname, sd_model):
- denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
+ self.options = options or {}
+ self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
- self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
- self.funcname = funcname
- self.func = getattr(k_diffusion.sampling, self.funcname)
- self.extra_params = sampler_extra_params.get(funcname, [])
- self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
- self.sampler_noises = None
- self.stop_at = None
- self.eta = None
- self.config = None # set by the function calling the constructor
- self.last_latent = None
- self.s_min_uncond = None
-
- self.conditioning_key = sd_model.model.conditioning_key
-
- def callback_state(self, d):
- step = d['i']
- latent = d["denoised"]
- if opts.live_preview_content == "Combined":
- sd_samplers_common.store_latent(latent)
- self.last_latent = latent
-
- if self.stop_at is not None and step > self.stop_at:
- raise sd_samplers_common.InterruptedException
-
- state.sampling_step = step
- shared.total_tqdm.update()
-
- def launch_sampling(self, steps, func):
- state.sampling_steps = steps
- state.sampling_step = 0
-
- try:
- return func()
- except RecursionError:
- print(
- 'Encountered RecursionError during sampling, returning last latent. '
- 'rho >5 with a polyexponential scheduler may cause this error. '
- 'You should try to use a smaller rho value instead.'
- )
- return self.last_latent
- except sd_samplers_common.InterruptedException:
- return self.last_latent
-
- def number_of_needed_noises(self, p):
- return p.steps
-
- def initialize(self, p):
- self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
- self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
- self.model_wrap_cfg.step = 0
- self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
- self.eta = p.eta if p.eta is not None else opts.eta_ancestral
- self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
-
- k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
-
- extra_params_kwargs = {}
- for param_name in self.extra_params:
- if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
- extra_params_kwargs[param_name] = getattr(p, param_name)
-
- if 'eta' in inspect.signature(self.func).parameters:
- if self.eta != 1.0:
- p.extra_generation_params["Eta"] = self.eta
-
- extra_params_kwargs['eta'] = self.eta
-
- return extra_params_kwargs
+ self.model_wrap_cfg = CFGDenoiserKDiffusion(self)
+ self.model_wrap = self.model_wrap_cfg.inner_model
def get_sigmas(self, p, steps):
discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
@@ -376,6 +127,9 @@ class KDiffusionSampler: sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
+ elif self.config is not None and self.config.options.get('scheduler', None) == 'exponential':
+ m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+ sigmas = k_diffusion.sampling.get_sigmas_exponential(n=steps, sigma_min=m_sigma_min, sigma_max=m_sigma_max, device=shared.device)
else:
sigmas = self.model_wrap.get_sigmas(steps)
@@ -384,24 +138,21 @@ class KDiffusionSampler: return sigmas
- def create_noise_sampler(self, x, sigmas, p):
- """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
- if shared.opts.no_dpmpp_sde_batch_determinism:
- return None
-
- from k_diffusion.sampling import BrownianTreeNoiseSampler
- sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
- current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
- return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
-
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
sigmas = self.get_sigmas(p, steps)
-
sigma_sched = sigmas[steps - t_enc - 1:]
+
xi = x + noise * sigma_sched[0]
+ if opts.img2img_extra_noise > 0:
+ p.extra_generation_params["Extra noise"] = opts.img2img_extra_noise
+ extra_noise_params = ExtraNoiseParams(noise, x, xi)
+ extra_noise_callback(extra_noise_params)
+ noise = extra_noise_params.noise
+ xi += noise * opts.img2img_extra_noise
+
extra_params_kwargs = self.initialize(p)
parameters = inspect.signature(self.func).parameters
@@ -421,9 +172,12 @@ class KDiffusionSampler: noise_sampler = self.create_noise_sampler(x, sigmas, p)
extra_params_kwargs['noise_sampler'] = noise_sampler
+ if self.config.options.get('solver_type', None) == 'heun':
+ extra_params_kwargs['solver_type'] = 'heun'
+
self.model_wrap_cfg.init_latent = x
self.last_latent = x
- extra_args = {
+ self.sampler_extra_args = {
'cond': conditioning,
'image_cond': image_conditioning,
'uncond': unconditional_conditioning,
@@ -431,7 +185,7 @@ class KDiffusionSampler: 's_min_uncond': self.s_min_uncond
}
- samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+ samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
@@ -443,34 +197,46 @@ class KDiffusionSampler: sigmas = self.get_sigmas(p, steps)
- x = x * sigmas[0]
+ if opts.sgm_noise_multiplier:
+ p.extra_generation_params["SGM noise multiplier"] = True
+ x = x * torch.sqrt(1.0 + sigmas[0] ** 2.0)
+ else:
+ x = x * sigmas[0]
extra_params_kwargs = self.initialize(p)
parameters = inspect.signature(self.func).parameters
+ if 'n' in parameters:
+ extra_params_kwargs['n'] = steps
+
if 'sigma_min' in parameters:
extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
- if 'n' in parameters:
- extra_params_kwargs['n'] = steps
- else:
+
+ if 'sigmas' in parameters:
extra_params_kwargs['sigmas'] = sigmas
if self.config.options.get('brownian_noise', False):
noise_sampler = self.create_noise_sampler(x, sigmas, p)
extra_params_kwargs['noise_sampler'] = noise_sampler
+ if self.config.options.get('solver_type', None) == 'heun':
+ extra_params_kwargs['solver_type'] = 'heun'
+
self.last_latent = x
- samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
+ self.sampler_extra_args = {
'cond': conditioning,
'image_cond': image_conditioning,
'uncond': unconditional_conditioning,
'cond_scale': p.cfg_scale,
's_min_uncond': self.s_min_uncond
- }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+ }
+
+ samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
return samples
+
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