833 lines
32 KiB
Python
833 lines
32 KiB
Python
from .k_diffusion import sampling as k_diffusion_sampling
|
|
from .extra_samplers import uni_pc
|
|
import torch
|
|
import collections
|
|
from comfy import model_management
|
|
import math
|
|
import logging
|
|
import comfy.sampler_helpers
|
|
import scipy.stats
|
|
import numpy
|
|
|
|
def get_area_and_mult(conds, x_in, timestep_in):
|
|
dims = tuple(x_in.shape[2:])
|
|
area = None
|
|
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 = list(conds['area'])
|
|
if 'strength' in conds:
|
|
strength = conds['strength']
|
|
|
|
input_x = x_in
|
|
if area is not None:
|
|
for i in range(len(dims)):
|
|
area[i] = min(input_x.shape[i + 2] - area[len(dims) + i], area[i])
|
|
input_x = input_x.narrow(i + 2, area[len(dims) + i], area[i])
|
|
|
|
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:])
|
|
|
|
mask = mask[:input_x.shape[0]]
|
|
if area is not None:
|
|
for i in range(len(dims)):
|
|
mask = mask.narrow(i + 1, area[len(dims) + i], area[i])
|
|
|
|
mask = mask * 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 and area is not None:
|
|
rr = 8
|
|
for i in range(len(dims)):
|
|
if area[len(dims) + i] != 0:
|
|
for t in range(rr):
|
|
m = mult.narrow(i + 2, t, 1)
|
|
m *= ((1.0/rr) * (t + 1))
|
|
if (area[i] + area[len(dims) + i]) < x_in.shape[i + 2]:
|
|
for t in range(rr):
|
|
m = mult.narrow(i + 2, area[i] - 1 - t, 1)
|
|
m *= ((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_batch(model, conds, x_in, timestep, model_options):
|
|
out_conds = []
|
|
out_counts = []
|
|
to_run = []
|
|
|
|
for i in range(len(conds)):
|
|
out_conds.append(torch.zeros_like(x_in))
|
|
out_counts.append(torch.ones_like(x_in) * 1e-37)
|
|
|
|
cond = conds[i]
|
|
if cond is not None:
|
|
for x in cond:
|
|
p = get_area_and_mult(x, x_in, timestep)
|
|
if p is None:
|
|
continue
|
|
|
|
to_run += [(p, i)]
|
|
|
|
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) * 1.5 < 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]
|
|
transformer_options["patches"] = cur_patches
|
|
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)
|
|
|
|
for o in range(batch_chunks):
|
|
cond_index = cond_or_uncond[o]
|
|
a = area[o]
|
|
if a is None:
|
|
out_conds[cond_index] += output[o] * mult[o]
|
|
out_counts[cond_index] += mult[o]
|
|
else:
|
|
out_c = out_conds[cond_index]
|
|
out_cts = out_counts[cond_index]
|
|
dims = len(a) // 2
|
|
for i in range(dims):
|
|
out_c = out_c.narrow(i + 2, a[i + dims], a[i])
|
|
out_cts = out_cts.narrow(i + 2, a[i + dims], a[i])
|
|
out_c += output[o] * mult[o]
|
|
out_cts += mult[o]
|
|
|
|
for i in range(len(out_conds)):
|
|
out_conds[i] /= out_counts[i]
|
|
|
|
return out_conds
|
|
|
|
def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove
|
|
logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.")
|
|
return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options))
|
|
|
|
def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None):
|
|
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
|
|
|
|
#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
|
|
|
|
conds = [cond, uncond_]
|
|
out = calc_cond_batch(model, conds, x, timestep, model_options)
|
|
|
|
for fn in model_options.get("sampler_pre_cfg_function", []):
|
|
args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep,
|
|
"input": x, "sigma": timestep, "model": model, "model_options": model_options}
|
|
out = fn(args)
|
|
|
|
return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_)
|
|
|
|
|
|
class KSamplerX0Inpaint:
|
|
def __init__(self, model, sigmas):
|
|
self.inner_model = model
|
|
self.sigmas = sigmas
|
|
def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None):
|
|
if denoise_mask is not None:
|
|
if "denoise_mask_function" in model_options:
|
|
denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas})
|
|
latent_mask = 1. - denoise_mask
|
|
x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask
|
|
out = self.inner_model(x, sigma, 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_sampling, steps):
|
|
s = 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_sampling, steps):
|
|
s = model_sampling
|
|
sigs = []
|
|
x = 1
|
|
if math.isclose(float(s.sigmas[x]), 0, abs_tol=0.00001):
|
|
steps += 1
|
|
sigs = []
|
|
else:
|
|
sigs = [0.0]
|
|
|
|
ss = max(len(s.sigmas) // steps, 1)
|
|
while x < len(s.sigmas):
|
|
sigs += [float(s.sigmas[x])]
|
|
x += ss
|
|
sigs = sigs[::-1]
|
|
return torch.FloatTensor(sigs)
|
|
|
|
def normal_scheduler(model_sampling, steps, sgm=False, floor=False):
|
|
s = model_sampling
|
|
start = s.timestep(s.sigma_max)
|
|
end = s.timestep(s.sigma_min)
|
|
|
|
append_zero = True
|
|
if sgm:
|
|
timesteps = torch.linspace(start, end, steps + 1)[:-1]
|
|
else:
|
|
if math.isclose(float(s.sigma(end)), 0, abs_tol=0.00001):
|
|
steps += 1
|
|
append_zero = False
|
|
timesteps = torch.linspace(start, end, steps)
|
|
|
|
sigs = []
|
|
for x in range(len(timesteps)):
|
|
ts = timesteps[x]
|
|
sigs.append(float(s.sigma(ts)))
|
|
|
|
if append_zero:
|
|
sigs += [0.0]
|
|
|
|
return torch.FloatTensor(sigs)
|
|
|
|
# Implemented based on: https://arxiv.org/abs/2407.12173
|
|
def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6):
|
|
total_timesteps = (len(model_sampling.sigmas) - 1)
|
|
ts = 1 - numpy.linspace(0, 1, steps, endpoint=False)
|
|
ts = numpy.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps)
|
|
|
|
sigs = []
|
|
last_t = -1
|
|
for t in ts:
|
|
if t != last_t:
|
|
sigs += [float(model_sampling.sigmas[int(t)])]
|
|
last_t = t
|
|
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_multidim(conditions, dims, 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()
|
|
a = area[1:]
|
|
a_len = len(a) // 2
|
|
area = ()
|
|
for d in range(len(dims)):
|
|
area += (max(1, round(a[d] * dims[d])),)
|
|
for d in range(len(dims)):
|
|
area += (round(a[d + a_len] * dims[d]),)
|
|
|
|
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) == len(dims):
|
|
mask = mask.unsqueeze(0)
|
|
if mask.shape[1:] != dims:
|
|
mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=dims, mode='bilinear', align_corners=False).squeeze(1)
|
|
|
|
if modified.get("set_area_to_bounds", False): #TODO: handle dim != 2
|
|
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 resolve_areas_and_cond_masks(conditions, h, w, device):
|
|
logging.warning("WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.")
|
|
return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device)
|
|
|
|
def create_cond_with_same_area_if_none(conds, c): #TODO: handle dim != 2
|
|
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
|
|
default_width = None
|
|
if len(noise.shape) >= 4: #TODO: 8 multiple should be set by the model
|
|
default_width = noise.shape[3] * 8
|
|
params["width"] = params.get("width", default_width)
|
|
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
|
|
|
|
KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
|
|
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
|
|
"dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
|
|
"ipndm", "ipndm_v", "deis"]
|
|
|
|
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, sigmas)
|
|
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
|
|
|
|
noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas))
|
|
|
|
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)
|
|
|
|
samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
|
|
samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples)
|
|
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):
|
|
if len(sigmas) <= 1:
|
|
return noise
|
|
|
|
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, **extra_options):
|
|
if len(sigmas) <= 1:
|
|
return noise
|
|
|
|
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, **extra_options)
|
|
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 process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None):
|
|
for k in conds:
|
|
conds[k] = conds[k][:]
|
|
resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device)
|
|
|
|
for k in conds:
|
|
calculate_start_end_timesteps(model, conds[k])
|
|
|
|
if hasattr(model, 'extra_conds'):
|
|
for k in conds:
|
|
conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed)
|
|
|
|
#make sure each cond area has an opposite one with the same area
|
|
for k in conds:
|
|
for c in conds[k]:
|
|
for kk in conds:
|
|
if k != kk:
|
|
create_cond_with_same_area_if_none(conds[kk], c)
|
|
|
|
for k in conds:
|
|
pre_run_control(model, conds[k])
|
|
|
|
if "positive" in conds:
|
|
positive = conds["positive"]
|
|
for k in conds:
|
|
if k != "positive":
|
|
apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), conds[k], 'control', lambda cond_cnets, x: cond_cnets[x])
|
|
apply_empty_x_to_equal_area(positive, conds[k], 'gligen', lambda cond_cnets, x: cond_cnets[x])
|
|
|
|
return conds
|
|
|
|
class CFGGuider:
|
|
def __init__(self, model_patcher):
|
|
self.model_patcher = model_patcher
|
|
self.model_options = model_patcher.model_options
|
|
self.original_conds = {}
|
|
self.cfg = 1.0
|
|
|
|
def set_conds(self, positive, negative):
|
|
self.inner_set_conds({"positive": positive, "negative": negative})
|
|
|
|
def set_cfg(self, cfg):
|
|
self.cfg = cfg
|
|
|
|
def inner_set_conds(self, conds):
|
|
for k in conds:
|
|
self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k])
|
|
|
|
def __call__(self, *args, **kwargs):
|
|
return self.predict_noise(*args, **kwargs)
|
|
|
|
def predict_noise(self, x, timestep, model_options={}, seed=None):
|
|
return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)
|
|
|
|
def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed):
|
|
if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image.
|
|
latent_image = self.inner_model.process_latent_in(latent_image)
|
|
|
|
self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed)
|
|
|
|
extra_args = {"model_options": self.model_options, "seed":seed}
|
|
|
|
samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
|
|
return self.inner_model.process_latent_out(samples.to(torch.float32))
|
|
|
|
def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
|
|
if sigmas.shape[-1] == 0:
|
|
return latent_image
|
|
|
|
self.conds = {}
|
|
for k in self.original_conds:
|
|
self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k]))
|
|
|
|
self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds)
|
|
device = self.model_patcher.load_device
|
|
|
|
if denoise_mask is not None:
|
|
denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device)
|
|
|
|
noise = noise.to(device)
|
|
latent_image = latent_image.to(device)
|
|
sigmas = sigmas.to(device)
|
|
|
|
output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
|
|
|
|
comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models)
|
|
del self.inner_model
|
|
del self.conds
|
|
del self.loaded_models
|
|
return output
|
|
|
|
|
|
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):
|
|
cfg_guider = CFGGuider(model)
|
|
cfg_guider.set_conds(positive, negative)
|
|
cfg_guider.set_cfg(cfg)
|
|
return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
|
|
|
|
|
|
SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta"]
|
|
SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
|
|
|
|
def calculate_sigmas(model_sampling, scheduler_name, steps):
|
|
if scheduler_name == "karras":
|
|
sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
|
|
elif scheduler_name == "exponential":
|
|
sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
|
|
elif scheduler_name == "normal":
|
|
sigmas = normal_scheduler(model_sampling, steps)
|
|
elif scheduler_name == "simple":
|
|
sigmas = simple_scheduler(model_sampling, steps)
|
|
elif scheduler_name == "ddim_uniform":
|
|
sigmas = ddim_scheduler(model_sampling, steps)
|
|
elif scheduler_name == "sgm_uniform":
|
|
sigmas = normal_scheduler(model_sampling, steps, sgm=True)
|
|
elif scheduler_name == "beta":
|
|
sigmas = beta_scheduler(model_sampling, steps)
|
|
else:
|
|
logging.error("error invalid scheduler {}".format(scheduler_name))
|
|
return sigmas
|
|
|
|
def sampler_object(name):
|
|
if name == "uni_pc":
|
|
sampler = KSAMPLER(uni_pc.sample_unipc)
|
|
elif name == "uni_pc_bh2":
|
|
sampler = KSAMPLER(uni_pc.sample_unipc_bh2)
|
|
elif name == "ddim":
|
|
sampler = ksampler("euler", inpaint_options={"random": True})
|
|
else:
|
|
sampler = ksampler(name)
|
|
return sampler
|
|
|
|
class KSampler:
|
|
SCHEDULERS = SCHEDULER_NAMES
|
|
SAMPLERS = SAMPLER_NAMES
|
|
DISCARD_PENULTIMATE_SIGMA_SAMPLERS = set(('dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2'))
|
|
|
|
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 self.DISCARD_PENULTIMATE_SIGMA_SAMPLERS:
|
|
steps += 1
|
|
discard_penultimate_sigma = True
|
|
|
|
sigmas = calculate_sigmas(self.model.get_model_object("model_sampling"), 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:
|
|
if denoise <= 0.0:
|
|
self.sigmas = torch.FloatTensor([])
|
|
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)
|