APIFo / ldm_patched /modules /samplers.py
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from ldm_patched.k_diffusion import sampling as k_diffusion_sampling
from ldm_patched.unipc import uni_pc
import torch
import collections
from ldm_patched.modules import model_management
import math
def get_area_and_mult(conds, x_in, timestep_in):
area = (x_in.shape[2], x_in.shape[3], 0, 0)
strength = 1.0
if 'timestep_start' in conds:
timestep_start = conds['timestep_start']
if timestep_in[0] > timestep_start:
return None
if 'timestep_end' in conds:
timestep_end = conds['timestep_end']
if timestep_in[0] < timestep_end:
return None
if 'area' in conds:
area = conds['area']
if 'strength' in conds:
strength = conds['strength']
input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
if 'mask' in conds:
# Scale the mask to the size of the input
# The mask should have been resized as we began the sampling process
mask_strength = 1.0
if "mask_strength" in conds:
mask_strength = conds["mask_strength"]
mask = conds['mask']
assert(mask.shape[1] == x_in.shape[2])
assert(mask.shape[2] == x_in.shape[3])
mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength
mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)
else:
mask = torch.ones_like(input_x)
mult = mask * strength
if 'mask' not in conds:
rr = 8
if area[2] != 0:
for t in range(rr):
mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1))
if (area[0] + area[2]) < x_in.shape[2]:
for t in range(rr):
mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1))
if area[3] != 0:
for t in range(rr):
mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1))
if (area[1] + area[3]) < x_in.shape[3]:
for t in range(rr):
mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1))
conditioning = {}
model_conds = conds["model_conds"]
for c in model_conds:
conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area)
control = conds.get('control', None)
patches = None
if 'gligen' in conds:
gligen = conds['gligen']
patches = {}
gligen_type = gligen[0]
gligen_model = gligen[1]
if gligen_type == "position":
gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device)
else:
gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device)
patches['middle_patch'] = [gligen_patch]
cond_obj = collections.namedtuple('cond_obj', ['input_x', 'mult', 'conditioning', 'area', 'control', 'patches'])
return cond_obj(input_x, mult, conditioning, area, control, patches)
def cond_equal_size(c1, c2):
if c1 is c2:
return True
if c1.keys() != c2.keys():
return False
for k in c1:
if not c1[k].can_concat(c2[k]):
return False
return True
def can_concat_cond(c1, c2):
if c1.input_x.shape != c2.input_x.shape:
return False
def objects_concatable(obj1, obj2):
if (obj1 is None) != (obj2 is None):
return False
if obj1 is not None:
if obj1 is not obj2:
return False
return True
if not objects_concatable(c1.control, c2.control):
return False
if not objects_concatable(c1.patches, c2.patches):
return False
return cond_equal_size(c1.conditioning, c2.conditioning)
def cond_cat(c_list):
c_crossattn = []
c_concat = []
c_adm = []
crossattn_max_len = 0
temp = {}
for x in c_list:
for k in x:
cur = temp.get(k, [])
cur.append(x[k])
temp[k] = cur
out = {}
for k in temp:
conds = temp[k]
out[k] = conds[0].concat(conds[1:])
return out
def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
out_cond = torch.zeros_like(x_in)
out_count = torch.ones_like(x_in) * 1e-37
out_uncond = torch.zeros_like(x_in)
out_uncond_count = torch.ones_like(x_in) * 1e-37
COND = 0
UNCOND = 1
to_run = []
for x in cond:
p = get_area_and_mult(x, x_in, timestep)
if p is None:
continue
to_run += [(p, COND)]
if uncond is not None:
for x in uncond:
p = get_area_and_mult(x, x_in, timestep)
if p is None:
continue
to_run += [(p, UNCOND)]
while len(to_run) > 0:
first = to_run[0]
first_shape = first[0][0].shape
to_batch_temp = []
for x in range(len(to_run)):
if can_concat_cond(to_run[x][0], first[0]):
to_batch_temp += [x]
to_batch_temp.reverse()
to_batch = to_batch_temp[:1]
free_memory = model_management.get_free_memory(x_in.device)
for i in range(1, len(to_batch_temp) + 1):
batch_amount = to_batch_temp[:len(to_batch_temp)//i]
input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
if model.memory_required(input_shape) < free_memory:
to_batch = batch_amount
break
input_x = []
mult = []
c = []
cond_or_uncond = []
area = []
control = None
patches = None
for x in to_batch:
o = to_run.pop(x)
p = o[0]
input_x.append(p.input_x)
mult.append(p.mult)
c.append(p.conditioning)
area.append(p.area)
cond_or_uncond.append(o[1])
control = p.control
patches = p.patches
batch_chunks = len(cond_or_uncond)
input_x = torch.cat(input_x)
c = cond_cat(c)
timestep_ = torch.cat([timestep] * batch_chunks)
if control is not None:
c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond))
transformer_options = {}
if 'transformer_options' in model_options:
transformer_options = model_options['transformer_options'].copy()
if patches is not None:
if "patches" in transformer_options:
cur_patches = transformer_options["patches"].copy()
for p in patches:
if p in cur_patches:
cur_patches[p] = cur_patches[p] + patches[p]
else:
cur_patches[p] = patches[p]
else:
transformer_options["patches"] = patches
transformer_options["cond_or_uncond"] = cond_or_uncond[:]
transformer_options["sigmas"] = timestep
c['transformer_options'] = transformer_options
if 'model_function_wrapper' in model_options:
output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
else:
output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
del input_x
for o in range(batch_chunks):
if cond_or_uncond[o] == COND:
out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
else:
out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
del mult
out_cond /= out_count
del out_count
out_uncond /= out_uncond_count
del out_uncond_count
return out_cond, out_uncond
#The main sampling function shared by all the samplers
#Returns denoised
def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
uncond_ = None
else:
uncond_ = uncond
cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)
if "sampler_cfg_function" in model_options:
args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
"cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
cfg_result = x - model_options["sampler_cfg_function"](args)
else:
cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
for fn in model_options.get("sampler_post_cfg_function", []):
args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred,
"sigma": timestep, "model_options": model_options, "input": x}
cfg_result = fn(args)
return cfg_result
class CFGNoisePredictor(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def apply_model(self, x, timestep, cond, uncond, cond_scale, model_options={}, seed=None):
out = sampling_function(self.inner_model, x, timestep, uncond, cond, cond_scale, model_options=model_options, seed=seed)
return out
def forward(self, *args, **kwargs):
return self.apply_model(*args, **kwargs)
class KSamplerX0Inpaint(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None):
if denoise_mask is not None:
latent_mask = 1. - denoise_mask
x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask
out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed)
if denoise_mask is not None:
out = out * denoise_mask + self.latent_image * latent_mask
return out
def simple_scheduler(model, steps):
s = model.model_sampling
sigs = []
ss = len(s.sigmas) / steps
for x in range(steps):
sigs += [float(s.sigmas[-(1 + int(x * ss))])]
sigs += [0.0]
return torch.FloatTensor(sigs)
def ddim_scheduler(model, steps):
s = model.model_sampling
sigs = []
ss = len(s.sigmas) // steps
x = 1
while x < len(s.sigmas):
sigs += [float(s.sigmas[x])]
x += ss
sigs = sigs[::-1]
sigs += [0.0]
return torch.FloatTensor(sigs)
def normal_scheduler(model, steps, sgm=False, floor=False):
s = model.model_sampling
start = s.timestep(s.sigma_max)
end = s.timestep(s.sigma_min)
if sgm:
timesteps = torch.linspace(start, end, steps + 1)[:-1]
else:
timesteps = torch.linspace(start, end, steps)
sigs = []
for x in range(len(timesteps)):
ts = timesteps[x]
sigs.append(s.sigma(ts))
sigs += [0.0]
return torch.FloatTensor(sigs)
def get_mask_aabb(masks):
if masks.numel() == 0:
return torch.zeros((0, 4), device=masks.device, dtype=torch.int)
b = masks.shape[0]
bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int)
is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool)
for i in range(b):
mask = masks[i]
if mask.numel() == 0:
continue
if torch.max(mask != 0) == False:
is_empty[i] = True
continue
y, x = torch.where(mask)
bounding_boxes[i, 0] = torch.min(x)
bounding_boxes[i, 1] = torch.min(y)
bounding_boxes[i, 2] = torch.max(x)
bounding_boxes[i, 3] = torch.max(y)
return bounding_boxes, is_empty
def resolve_areas_and_cond_masks(conditions, h, w, device):
# We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes.
# While we're doing this, we can also resolve the mask device and scaling for performance reasons
for i in range(len(conditions)):
c = conditions[i]
if 'area' in c:
area = c['area']
if area[0] == "percentage":
modified = c.copy()
area = (max(1, round(area[1] * h)), max(1, round(area[2] * w)), round(area[3] * h), round(area[4] * w))
modified['area'] = area
c = modified
conditions[i] = c
if 'mask' in c:
mask = c['mask']
mask = mask.to(device=device)
modified = c.copy()
if len(mask.shape) == 2:
mask = mask.unsqueeze(0)
if mask.shape[1] != h or mask.shape[2] != w:
mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(h, w), mode='bilinear', align_corners=False).squeeze(1)
if modified.get("set_area_to_bounds", False):
bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0)
boxes, is_empty = get_mask_aabb(bounds)
if is_empty[0]:
# Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway)
modified['area'] = (8, 8, 0, 0)
else:
box = boxes[0]
H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0])
H = max(8, H)
W = max(8, W)
area = (int(H), int(W), int(Y), int(X))
modified['area'] = area
modified['mask'] = mask
conditions[i] = modified
def create_cond_with_same_area_if_none(conds, c):
if 'area' not in c:
return
c_area = c['area']
smallest = None
for x in conds:
if 'area' in x:
a = x['area']
if c_area[2] >= a[2] and c_area[3] >= a[3]:
if a[0] + a[2] >= c_area[0] + c_area[2]:
if a[1] + a[3] >= c_area[1] + c_area[3]:
if smallest is None:
smallest = x
elif 'area' not in smallest:
smallest = x
else:
if smallest['area'][0] * smallest['area'][1] > a[0] * a[1]:
smallest = x
else:
if smallest is None:
smallest = x
if smallest is None:
return
if 'area' in smallest:
if smallest['area'] == c_area:
return
out = c.copy()
out['model_conds'] = smallest['model_conds'].copy() #TODO: which fields should be copied?
conds += [out]
def calculate_start_end_timesteps(model, conds):
s = model.model_sampling
for t in range(len(conds)):
x = conds[t]
timestep_start = None
timestep_end = None
if 'start_percent' in x:
timestep_start = s.percent_to_sigma(x['start_percent'])
if 'end_percent' in x:
timestep_end = s.percent_to_sigma(x['end_percent'])
if (timestep_start is not None) or (timestep_end is not None):
n = x.copy()
if (timestep_start is not None):
n['timestep_start'] = timestep_start
if (timestep_end is not None):
n['timestep_end'] = timestep_end
conds[t] = n
def pre_run_control(model, conds):
s = model.model_sampling
for t in range(len(conds)):
x = conds[t]
timestep_start = None
timestep_end = None
percent_to_timestep_function = lambda a: s.percent_to_sigma(a)
if 'control' in x:
x['control'].pre_run(model, percent_to_timestep_function)
def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):
cond_cnets = []
cond_other = []
uncond_cnets = []
uncond_other = []
for t in range(len(conds)):
x = conds[t]
if 'area' not in x:
if name in x and x[name] is not None:
cond_cnets.append(x[name])
else:
cond_other.append((x, t))
for t in range(len(uncond)):
x = uncond[t]
if 'area' not in x:
if name in x and x[name] is not None:
uncond_cnets.append(x[name])
else:
uncond_other.append((x, t))
if len(uncond_cnets) > 0:
return
for x in range(len(cond_cnets)):
temp = uncond_other[x % len(uncond_other)]
o = temp[0]
if name in o and o[name] is not None:
n = o.copy()
n[name] = uncond_fill_func(cond_cnets, x)
uncond += [n]
else:
n = o.copy()
n[name] = uncond_fill_func(cond_cnets, x)
uncond[temp[1]] = n
def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwargs):
for t in range(len(conds)):
x = conds[t]
params = x.copy()
params["device"] = device
params["noise"] = noise
params["width"] = params.get("width", noise.shape[3] * 8)
params["height"] = params.get("height", noise.shape[2] * 8)
params["prompt_type"] = params.get("prompt_type", prompt_type)
for k in kwargs:
if k not in params:
params[k] = kwargs[k]
out = model_function(**params)
x = x.copy()
model_conds = x['model_conds'].copy()
for k in out:
model_conds[k] = out[k]
x['model_conds'] = model_conds
conds[t] = x
return conds
class Sampler:
def sample(self):
pass
def max_denoise(self, model_wrap, sigmas):
max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max)
sigma = float(sigmas[0])
return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma
class UNIPC(Sampler):
def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, disable=disable_pbar)
class UNIPCBH2(Sampler):
def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar)
KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"]
class KSAMPLER(Sampler):
def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
self.sampler_function = sampler_function
self.extra_options = extra_options
self.inpaint_options = inpaint_options
def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
extra_args["denoise_mask"] = denoise_mask
model_k = KSamplerX0Inpaint(model_wrap)
model_k.latent_image = latent_image
if self.inpaint_options.get("random", False): #TODO: Should this be the default?
generator = torch.manual_seed(extra_args.get("seed", 41) + 1)
model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device)
else:
model_k.noise = noise
if self.max_denoise(model_wrap, sigmas):
noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0)
else:
noise = noise * sigmas[0]
k_callback = None
total_steps = len(sigmas) - 1
if callback is not None:
k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
if latent_image is not None:
noise += latent_image
samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
return samples
def ksampler(sampler_name, extra_options={}, inpaint_options={}):
if sampler_name == "dpm_fast":
def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable):
sigma_min = sigmas[-1]
if sigma_min == 0:
sigma_min = sigmas[-2]
total_steps = len(sigmas) - 1
return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable)
sampler_function = dpm_fast_function
elif sampler_name == "dpm_adaptive":
def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable):
sigma_min = sigmas[-1]
if sigma_min == 0:
sigma_min = sigmas[-2]
return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable)
sampler_function = dpm_adaptive_function
else:
sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))
return KSAMPLER(sampler_function, extra_options, inpaint_options)
def wrap_model(model):
model_denoise = CFGNoisePredictor(model)
return model_denoise
def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
positive = positive[:]
negative = negative[:]
resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], device)
resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], device)
model_wrap = wrap_model(model)
calculate_start_end_timesteps(model, negative)
calculate_start_end_timesteps(model, positive)
if latent_image is not None:
latent_image = model.process_latent_in(latent_image)
if hasattr(model, 'extra_conds'):
positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask, seed=seed)
negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask, seed=seed)
#make sure each cond area has an opposite one with the same area
for c in positive:
create_cond_with_same_area_if_none(negative, c)
for c in negative:
create_cond_with_same_area_if_none(positive, c)
pre_run_control(model, negative + positive)
apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x])
apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])
extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed}
samples = sampler.sample(model_wrap, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
return model.process_latent_out(samples.to(torch.float32))
SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
def calculate_sigmas_scheduler(model, scheduler_name, steps):
if scheduler_name == "karras":
sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max))
elif scheduler_name == "exponential":
sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max))
elif scheduler_name == "normal":
sigmas = normal_scheduler(model, steps)
elif scheduler_name == "simple":
sigmas = simple_scheduler(model, steps)
elif scheduler_name == "ddim_uniform":
sigmas = ddim_scheduler(model, steps)
elif scheduler_name == "sgm_uniform":
sigmas = normal_scheduler(model, steps, sgm=True)
else:
print("error invalid scheduler", scheduler_name)
return sigmas
def sampler_object(name):
if name == "uni_pc":
sampler = UNIPC()
elif name == "uni_pc_bh2":
sampler = UNIPCBH2()
elif name == "ddim":
sampler = ksampler("euler", inpaint_options={"random": True})
else:
sampler = ksampler(name)
return sampler
class KSampler:
SCHEDULERS = SCHEDULER_NAMES
SAMPLERS = SAMPLER_NAMES
def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
self.model = model
self.device = device
if scheduler not in self.SCHEDULERS:
scheduler = self.SCHEDULERS[0]
if sampler not in self.SAMPLERS:
sampler = self.SAMPLERS[0]
self.scheduler = scheduler
self.sampler = sampler
self.set_steps(steps, denoise)
self.denoise = denoise
self.model_options = model_options
def calculate_sigmas(self, steps):
sigmas = None
discard_penultimate_sigma = False
if self.sampler in ['dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2']:
steps += 1
discard_penultimate_sigma = True
sigmas = calculate_sigmas_scheduler(self.model, self.scheduler, steps)
if discard_penultimate_sigma:
sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
return sigmas
def set_steps(self, steps, denoise=None):
self.steps = steps
if denoise is None or denoise > 0.9999:
self.sigmas = self.calculate_sigmas(steps).to(self.device)
else:
new_steps = int(steps/denoise)
sigmas = self.calculate_sigmas(new_steps).to(self.device)
self.sigmas = sigmas[-(steps + 1):]
def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
if sigmas is None:
sigmas = self.sigmas
if last_step is not None and last_step < (len(sigmas) - 1):
sigmas = sigmas[:last_step + 1]
if force_full_denoise:
sigmas[-1] = 0
if start_step is not None:
if start_step < (len(sigmas) - 1):
sigmas = sigmas[start_step:]
else:
if latent_image is not None:
return latent_image
else:
return torch.zeros_like(noise)
sampler = sampler_object(self.sampler)
return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)