786 lines
33 KiB
Python
786 lines
33 KiB
Python
from .k_diffusion import sampling as k_diffusion_sampling
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from .k_diffusion import external as k_diffusion_external
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from .extra_samplers import uni_pc
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import torch
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from comfy import model_management
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from .ldm.models.diffusion.ddim import DDIMSampler
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from .ldm.modules.diffusionmodules.util import make_ddim_timesteps
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import math
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from comfy import model_base
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import comfy.utils
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def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
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return abs(a*b) // math.gcd(a, b)
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#The main sampling function shared by all the samplers
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#Returns predicted noise
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def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={}, seed=None):
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def get_area_and_mult(cond, x_in, cond_concat_in, timestep_in):
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area = (x_in.shape[2], x_in.shape[3], 0, 0)
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strength = 1.0
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if 'timestep_start' in cond[1]:
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timestep_start = cond[1]['timestep_start']
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if timestep_in[0] > timestep_start:
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return None
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if 'timestep_end' in cond[1]:
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timestep_end = cond[1]['timestep_end']
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if timestep_in[0] < timestep_end:
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return None
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if 'area' in cond[1]:
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area = cond[1]['area']
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if 'strength' in cond[1]:
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strength = cond[1]['strength']
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adm_cond = None
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if 'adm_encoded' in cond[1]:
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adm_cond = cond[1]['adm_encoded']
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input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
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if 'mask' in cond[1]:
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# Scale the mask to the size of the input
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# The mask should have been resized as we began the sampling process
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mask_strength = 1.0
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if "mask_strength" in cond[1]:
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mask_strength = cond[1]["mask_strength"]
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mask = cond[1]['mask']
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assert(mask.shape[1] == x_in.shape[2])
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assert(mask.shape[2] == x_in.shape[3])
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mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength
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mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)
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else:
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mask = torch.ones_like(input_x)
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mult = mask * strength
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if 'mask' not in cond[1]:
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rr = 8
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if area[2] != 0:
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for t in range(rr):
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mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1))
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if (area[0] + area[2]) < x_in.shape[2]:
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for t in range(rr):
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mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1))
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if area[3] != 0:
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for t in range(rr):
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mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1))
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if (area[1] + area[3]) < x_in.shape[3]:
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for t in range(rr):
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mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1))
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conditionning = {}
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conditionning['c_crossattn'] = cond[0]
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if cond_concat_in is not None and len(cond_concat_in) > 0:
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cropped = []
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for x in cond_concat_in:
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cr = x[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
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cropped.append(cr)
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conditionning['c_concat'] = torch.cat(cropped, dim=1)
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if adm_cond is not None:
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conditionning['c_adm'] = adm_cond
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control = None
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if 'control' in cond[1]:
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control = cond[1]['control']
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patches = None
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if 'gligen' in cond[1]:
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gligen = cond[1]['gligen']
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patches = {}
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gligen_type = gligen[0]
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gligen_model = gligen[1]
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if gligen_type == "position":
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gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device)
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else:
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gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device)
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patches['middle_patch'] = [gligen_patch]
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return (input_x, mult, conditionning, area, control, patches)
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def cond_equal_size(c1, c2):
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if c1 is c2:
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return True
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if c1.keys() != c2.keys():
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return False
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if 'c_crossattn' in c1:
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s1 = c1['c_crossattn'].shape
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s2 = c2['c_crossattn'].shape
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if s1 != s2:
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if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
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return False
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mult_min = lcm(s1[1], s2[1])
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diff = mult_min // min(s1[1], s2[1])
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if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
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return False
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if 'c_concat' in c1:
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if c1['c_concat'].shape != c2['c_concat'].shape:
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return False
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if 'c_adm' in c1:
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if c1['c_adm'].shape != c2['c_adm'].shape:
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return False
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return True
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def can_concat_cond(c1, c2):
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if c1[0].shape != c2[0].shape:
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return False
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#control
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if (c1[4] is None) != (c2[4] is None):
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return False
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if c1[4] is not None:
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if c1[4] is not c2[4]:
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return False
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#patches
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if (c1[5] is None) != (c2[5] is None):
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return False
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if (c1[5] is not None):
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if c1[5] is not c2[5]:
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return False
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return cond_equal_size(c1[2], c2[2])
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def cond_cat(c_list):
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c_crossattn = []
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c_concat = []
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c_adm = []
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crossattn_max_len = 0
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for x in c_list:
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if 'c_crossattn' in x:
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c = x['c_crossattn']
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if crossattn_max_len == 0:
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crossattn_max_len = c.shape[1]
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else:
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crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
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c_crossattn.append(c)
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if 'c_concat' in x:
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c_concat.append(x['c_concat'])
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if 'c_adm' in x:
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c_adm.append(x['c_adm'])
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out = {}
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c_crossattn_out = []
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for c in c_crossattn:
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if c.shape[1] < crossattn_max_len:
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c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result
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c_crossattn_out.append(c)
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if len(c_crossattn_out) > 0:
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out['c_crossattn'] = torch.cat(c_crossattn_out)
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if len(c_concat) > 0:
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out['c_concat'] = torch.cat(c_concat)
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if len(c_adm) > 0:
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out['c_adm'] = torch.cat(c_adm)
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return out
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def calc_cond_uncond_batch(model_function, cond, uncond, x_in, timestep, max_total_area, cond_concat_in, model_options):
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out_cond = torch.zeros_like(x_in)
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out_count = torch.ones_like(x_in)/100000.0
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out_uncond = torch.zeros_like(x_in)
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out_uncond_count = torch.ones_like(x_in)/100000.0
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COND = 0
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UNCOND = 1
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to_run = []
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for x in cond:
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p = get_area_and_mult(x, x_in, cond_concat_in, timestep)
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if p is None:
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continue
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to_run += [(p, COND)]
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if uncond is not None:
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for x in uncond:
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p = get_area_and_mult(x, x_in, cond_concat_in, timestep)
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if p is None:
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continue
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to_run += [(p, UNCOND)]
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while len(to_run) > 0:
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first = to_run[0]
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first_shape = first[0][0].shape
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to_batch_temp = []
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for x in range(len(to_run)):
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if can_concat_cond(to_run[x][0], first[0]):
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to_batch_temp += [x]
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to_batch_temp.reverse()
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to_batch = to_batch_temp[:1]
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for i in range(1, len(to_batch_temp) + 1):
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batch_amount = to_batch_temp[:len(to_batch_temp)//i]
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if (len(batch_amount) * first_shape[0] * first_shape[2] * first_shape[3] < max_total_area):
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to_batch = batch_amount
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break
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input_x = []
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mult = []
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c = []
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cond_or_uncond = []
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area = []
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control = None
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patches = None
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for x in to_batch:
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o = to_run.pop(x)
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p = o[0]
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input_x += [p[0]]
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mult += [p[1]]
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c += [p[2]]
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area += [p[3]]
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cond_or_uncond += [o[1]]
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control = p[4]
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patches = p[5]
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batch_chunks = len(cond_or_uncond)
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input_x = torch.cat(input_x)
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c = cond_cat(c)
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timestep_ = torch.cat([timestep] * batch_chunks)
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if control is not None:
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c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond))
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transformer_options = {}
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if 'transformer_options' in model_options:
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transformer_options = model_options['transformer_options'].copy()
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if patches is not None:
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if "patches" in transformer_options:
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cur_patches = transformer_options["patches"].copy()
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for p in patches:
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if p in cur_patches:
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cur_patches[p] = cur_patches[p] + patches[p]
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else:
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cur_patches[p] = patches[p]
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else:
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transformer_options["patches"] = patches
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transformer_options["cond_or_uncond"] = cond_or_uncond[:]
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c['transformer_options'] = transformer_options
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if 'model_function_wrapper' in model_options:
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output = model_options['model_function_wrapper'](model_function, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
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else:
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output = model_function(input_x, timestep_, **c).chunk(batch_chunks)
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del input_x
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for o in range(batch_chunks):
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if cond_or_uncond[o] == COND:
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out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
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out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
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else:
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out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
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out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
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del mult
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out_cond /= out_count
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del out_count
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out_uncond /= out_uncond_count
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del out_uncond_count
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return out_cond, out_uncond
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max_total_area = model_management.maximum_batch_area()
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if math.isclose(cond_scale, 1.0):
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uncond = None
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cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, timestep, max_total_area, cond_concat, model_options)
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if "sampler_cfg_function" in model_options:
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args = {"cond": cond, "uncond": uncond, "cond_scale": cond_scale, "timestep": timestep}
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return model_options["sampler_cfg_function"](args)
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else:
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return uncond + (cond - uncond) * cond_scale
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class CompVisVDenoiser(k_diffusion_external.DiscreteVDDPMDenoiser):
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def __init__(self, model, quantize=False, device='cpu'):
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super().__init__(model, model.alphas_cumprod, quantize=quantize)
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def get_v(self, x, t, cond, **kwargs):
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return self.inner_model.apply_model(x, t, cond, **kwargs)
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class CFGNoisePredictor(torch.nn.Module):
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def __init__(self, model):
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super().__init__()
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self.inner_model = model
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self.alphas_cumprod = model.alphas_cumprod
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def apply_model(self, x, timestep, cond, uncond, cond_scale, cond_concat=None, model_options={}, seed=None):
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out = sampling_function(self.inner_model.apply_model, x, timestep, uncond, cond, cond_scale, cond_concat, model_options=model_options, seed=seed)
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return out
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class KSamplerX0Inpaint(torch.nn.Module):
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def __init__(self, model):
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super().__init__()
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self.inner_model = model
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def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, cond_concat=None, model_options={}, seed=None):
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if denoise_mask is not None:
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latent_mask = 1. - denoise_mask
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x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask
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out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, cond_concat=cond_concat, model_options=model_options, seed=seed)
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if denoise_mask is not None:
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out *= denoise_mask
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if denoise_mask is not None:
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out += self.latent_image * latent_mask
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return out
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def simple_scheduler(model, steps):
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sigs = []
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ss = len(model.sigmas) / steps
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for x in range(steps):
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sigs += [float(model.sigmas[-(1 + int(x * ss))])]
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sigs += [0.0]
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return torch.FloatTensor(sigs)
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def ddim_scheduler(model, steps):
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sigs = []
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ddim_timesteps = make_ddim_timesteps(ddim_discr_method="uniform", num_ddim_timesteps=steps, num_ddpm_timesteps=model.inner_model.inner_model.num_timesteps, verbose=False)
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for x in range(len(ddim_timesteps) - 1, -1, -1):
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ts = ddim_timesteps[x]
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if ts > 999:
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ts = 999
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sigs.append(model.t_to_sigma(torch.tensor(ts)))
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sigs += [0.0]
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return torch.FloatTensor(sigs)
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def sgm_scheduler(model, steps):
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sigs = []
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timesteps = torch.linspace(model.inner_model.inner_model.num_timesteps - 1, 0, steps + 1)[:-1].type(torch.int)
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for x in range(len(timesteps)):
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ts = timesteps[x]
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if ts > 999:
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ts = 999
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sigs.append(model.t_to_sigma(torch.tensor(ts)))
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sigs += [0.0]
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return torch.FloatTensor(sigs)
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def blank_inpaint_image_like(latent_image):
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blank_image = torch.ones_like(latent_image)
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# these are the values for "zero" in pixel space translated to latent space
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blank_image[:,0] *= 0.8223
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blank_image[:,1] *= -0.6876
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blank_image[:,2] *= 0.6364
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blank_image[:,3] *= 0.1380
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return blank_image
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def get_mask_aabb(masks):
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if masks.numel() == 0:
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return torch.zeros((0, 4), device=masks.device, dtype=torch.int)
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b = masks.shape[0]
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bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int)
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is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool)
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for i in range(b):
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mask = masks[i]
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if mask.numel() == 0:
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continue
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if torch.max(mask != 0) == False:
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is_empty[i] = True
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continue
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y, x = torch.where(mask)
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bounding_boxes[i, 0] = torch.min(x)
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bounding_boxes[i, 1] = torch.min(y)
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bounding_boxes[i, 2] = torch.max(x)
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bounding_boxes[i, 3] = torch.max(y)
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return bounding_boxes, is_empty
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def resolve_areas_and_cond_masks(conditions, h, w, device):
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# We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes.
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# While we're doing this, we can also resolve the mask device and scaling for performance reasons
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for i in range(len(conditions)):
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c = conditions[i]
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if 'area' in c[1]:
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area = c[1]['area']
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if area[0] == "percentage":
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modified = c[1].copy()
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area = (max(1, round(area[1] * h)), max(1, round(area[2] * w)), round(area[3] * h), round(area[4] * w))
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modified['area'] = area
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c = [c[0], modified]
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conditions[i] = c
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if 'mask' in c[1]:
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mask = c[1]['mask']
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mask = mask.to(device=device)
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modified = c[1].copy()
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if len(mask.shape) == 2:
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mask = mask.unsqueeze(0)
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if mask.shape[1] != h or mask.shape[2] != w:
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mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(h, w), mode='bilinear', align_corners=False).squeeze(1)
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if modified.get("set_area_to_bounds", False):
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bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0)
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boxes, is_empty = get_mask_aabb(bounds)
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if is_empty[0]:
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# Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway)
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modified['area'] = (8, 8, 0, 0)
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else:
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box = boxes[0]
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H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0])
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H = max(8, H)
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W = max(8, W)
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area = (int(H), int(W), int(Y), int(X))
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modified['area'] = area
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modified['mask'] = mask
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conditions[i] = [c[0], modified]
|
|
|
|
def create_cond_with_same_area_if_none(conds, c):
|
|
if 'area' not in c[1]:
|
|
return
|
|
|
|
c_area = c[1]['area']
|
|
smallest = None
|
|
for x in conds:
|
|
if 'area' in x[1]:
|
|
a = x[1]['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[1]:
|
|
smallest = x
|
|
else:
|
|
if smallest[1]['area'][0] * smallest[1]['area'][1] > a[0] * a[1]:
|
|
smallest = x
|
|
else:
|
|
if smallest is None:
|
|
smallest = x
|
|
if smallest is None:
|
|
return
|
|
if 'area' in smallest[1]:
|
|
if smallest[1]['area'] == c_area:
|
|
return
|
|
n = c[1].copy()
|
|
conds += [[smallest[0], n]]
|
|
|
|
def calculate_start_end_timesteps(model, conds):
|
|
for t in range(len(conds)):
|
|
x = conds[t]
|
|
|
|
timestep_start = None
|
|
timestep_end = None
|
|
if 'start_percent' in x[1]:
|
|
timestep_start = model.sigma_to_t(model.t_to_sigma(torch.tensor(x[1]['start_percent'] * 999.0)))
|
|
if 'end_percent' in x[1]:
|
|
timestep_end = model.sigma_to_t(model.t_to_sigma(torch.tensor(x[1]['end_percent'] * 999.0)))
|
|
|
|
if (timestep_start is not None) or (timestep_end is not None):
|
|
n = x[1].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] = [x[0], n]
|
|
|
|
def pre_run_control(model, conds):
|
|
for t in range(len(conds)):
|
|
x = conds[t]
|
|
|
|
timestep_start = None
|
|
timestep_end = None
|
|
percent_to_timestep_function = lambda a: model.sigma_to_t(model.t_to_sigma(torch.tensor(a) * 999.0))
|
|
if 'control' in x[1]:
|
|
x[1]['control'].pre_run(model.inner_model.inner_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[1]:
|
|
if name in x[1] and x[1][name] is not None:
|
|
cond_cnets.append(x[1][name])
|
|
else:
|
|
cond_other.append((x, t))
|
|
for t in range(len(uncond)):
|
|
x = uncond[t]
|
|
if 'area' not in x[1]:
|
|
if name in x[1] and x[1][name] is not None:
|
|
uncond_cnets.append(x[1][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[1] and o[1][name] is not None:
|
|
n = o[1].copy()
|
|
n[name] = uncond_fill_func(cond_cnets, x)
|
|
uncond += [[o[0], n]]
|
|
else:
|
|
n = o[1].copy()
|
|
n[name] = uncond_fill_func(cond_cnets, x)
|
|
uncond[temp[1]] = [o[0], n]
|
|
|
|
def encode_adm(model, conds, batch_size, width, height, device, prompt_type):
|
|
for t in range(len(conds)):
|
|
x = conds[t]
|
|
adm_out = None
|
|
if 'adm' in x[1]:
|
|
adm_out = x[1]["adm"]
|
|
else:
|
|
params = x[1].copy()
|
|
params["width"] = params.get("width", width * 8)
|
|
params["height"] = params.get("height", height * 8)
|
|
params["prompt_type"] = params.get("prompt_type", prompt_type)
|
|
adm_out = model.encode_adm(device=device, **params)
|
|
|
|
if adm_out is not None:
|
|
x[1] = x[1].copy()
|
|
x[1]["adm_encoded"] = comfy.utils.repeat_to_batch_size(adm_out, batch_size).to(device)
|
|
|
|
return conds
|
|
|
|
|
|
class Sampler:
|
|
def sample(self):
|
|
pass
|
|
|
|
def max_denoise(self, model_wrap, sigmas):
|
|
return math.isclose(float(model_wrap.sigma_max), float(sigmas[0]), rel_tol=1e-05)
|
|
|
|
class DDIM(Sampler):
|
|
def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
|
|
timesteps = []
|
|
for s in range(sigmas.shape[0]):
|
|
timesteps.insert(0, model_wrap.sigma_to_discrete_timestep(sigmas[s]))
|
|
noise_mask = None
|
|
if denoise_mask is not None:
|
|
noise_mask = 1.0 - denoise_mask
|
|
|
|
ddim_callback = None
|
|
if callback is not None:
|
|
total_steps = len(timesteps) - 1
|
|
ddim_callback = lambda pred_x0, i: callback(i, pred_x0, None, total_steps)
|
|
|
|
max_denoise = self.max_denoise(model_wrap, sigmas)
|
|
|
|
ddim_sampler = DDIMSampler(model_wrap.inner_model.inner_model, device=noise.device)
|
|
ddim_sampler.make_schedule_timesteps(ddim_timesteps=timesteps, verbose=False)
|
|
z_enc = ddim_sampler.stochastic_encode(latent_image, torch.tensor([len(timesteps) - 1] * noise.shape[0]).to(noise.device), noise=noise, max_denoise=max_denoise)
|
|
samples, _ = ddim_sampler.sample_custom(ddim_timesteps=timesteps,
|
|
batch_size=noise.shape[0],
|
|
shape=noise.shape[1:],
|
|
verbose=False,
|
|
eta=0.0,
|
|
x_T=z_enc,
|
|
x0=latent_image,
|
|
img_callback=ddim_callback,
|
|
denoise_function=model_wrap.predict_eps_discrete_timestep,
|
|
extra_args=extra_args,
|
|
mask=noise_mask,
|
|
to_zero=sigmas[-1]==0,
|
|
end_step=sigmas.shape[0] - 1,
|
|
disable_pbar=disable_pbar)
|
|
return samples
|
|
|
|
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, sampling_function=sampling_function, 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, sampling_function=sampling_function, 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", "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"]
|
|
|
|
def ksampler(sampler_name, extra_options={}):
|
|
class KSAMPLER(Sampler):
|
|
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
|
|
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)
|
|
|
|
sigma_min = sigmas[-1]
|
|
if sigma_min == 0:
|
|
sigma_min = sigmas[-2]
|
|
|
|
if latent_image is not None:
|
|
noise += latent_image
|
|
if sampler_name == "dpm_fast":
|
|
samples = k_diffusion_sampling.sample_dpm_fast(model_k, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=k_callback, disable=disable_pbar)
|
|
elif sampler_name == "dpm_adaptive":
|
|
samples = k_diffusion_sampling.sample_dpm_adaptive(model_k, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=k_callback, disable=disable_pbar)
|
|
else:
|
|
samples = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **extra_options)
|
|
return samples
|
|
return KSAMPLER
|
|
|
|
def wrap_model(model):
|
|
model_denoise = CFGNoisePredictor(model)
|
|
if model.model_type == model_base.ModelType.V_PREDICTION:
|
|
model_wrap = CompVisVDenoiser(model_denoise, quantize=True)
|
|
else:
|
|
model_wrap = k_diffusion_external.CompVisDenoiser(model_denoise, quantize=True)
|
|
return model_wrap
|
|
|
|
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_wrap, negative)
|
|
calculate_start_end_timesteps(model_wrap, positive)
|
|
|
|
#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_wrap, negative + positive)
|
|
|
|
apply_empty_x_to_equal_area(list(filter(lambda c: c[1].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])
|
|
|
|
if model.is_adm():
|
|
positive = encode_adm(model, positive, noise.shape[0], noise.shape[3], noise.shape[2], device, "positive")
|
|
negative = encode_adm(model, negative, noise.shape[0], noise.shape[3], noise.shape[2], device, "negative")
|
|
|
|
if latent_image is not None:
|
|
latent_image = model.process_latent_in(latent_image)
|
|
|
|
extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed}
|
|
|
|
cond_concat = None
|
|
if hasattr(model, 'concat_keys'): #inpaint
|
|
cond_concat = []
|
|
for ck in model.concat_keys:
|
|
if denoise_mask is not None:
|
|
if ck == "mask":
|
|
cond_concat.append(denoise_mask[:,:1])
|
|
elif ck == "masked_image":
|
|
cond_concat.append(latent_image) #NOTE: the latent_image should be masked by the mask in pixel space
|
|
else:
|
|
if ck == "mask":
|
|
cond_concat.append(torch.ones_like(noise)[:,:1])
|
|
elif ck == "masked_image":
|
|
cond_concat.append(blank_inpaint_image_like(noise))
|
|
extra_args["cond_concat"] = cond_concat
|
|
|
|
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):
|
|
model_wrap = wrap_model(model)
|
|
if scheduler_name == "karras":
|
|
sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model_wrap.sigma_min), sigma_max=float(model_wrap.sigma_max))
|
|
elif scheduler_name == "exponential":
|
|
sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model_wrap.sigma_min), sigma_max=float(model_wrap.sigma_max))
|
|
elif scheduler_name == "normal":
|
|
sigmas = model_wrap.get_sigmas(steps)
|
|
elif scheduler_name == "simple":
|
|
sigmas = simple_scheduler(model_wrap, steps)
|
|
elif scheduler_name == "ddim_uniform":
|
|
sigmas = ddim_scheduler(model_wrap, steps)
|
|
elif scheduler_name == "sgm_uniform":
|
|
sigmas = sgm_scheduler(model_wrap, steps)
|
|
else:
|
|
print("error invalid scheduler", self.scheduler)
|
|
return sigmas
|
|
|
|
def sampler_class(name):
|
|
if name == "uni_pc":
|
|
sampler = UNIPC
|
|
elif name == "uni_pc_bh2":
|
|
sampler = UNIPCBH2
|
|
elif name == "ddim":
|
|
sampler = DDIM
|
|
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']:
|
|
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_class(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)
|