Spaces:
Runtime error
Runtime error
File size: 13,574 Bytes
910b9ab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 |
"""SAMPLING ONLY."""
import torch
import ptp_scripts.ptp_scripts as ptp
import ptp_scripts.ptp_utils as ptp_utils
# from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
from scripts.dpm_solver_pytorch import NoiseScheduleVP, model_wrapper, DPM_Solver
from tqdm import tqdm
MODEL_TYPES = {
"eps": "noise",
"v": "v"
}
class DPMSolverSampler(object):
def __init__(self, model, **kwargs):
super().__init__()
self.model = model
to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != self.model.device:
attr = attr.to(self.model.device)
setattr(self, name, attr)
@torch.no_grad()
def sample(self,
steps,
batch_size,
shape,
conditioning=None,
inv_emb=None,
callback=None,
normals_sequence=None,
img_callback=None,
quantize_x0=False,
eta=0.,
mask=None,
x0=None,
temperature=1.,
noise_dropout=0.,
score_corrector=None,
corrector_kwargs=None,
verbose=True,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
t_start=None,
t_end=None,
DPMencode=False,
order=3,
width=None,
height=None,
ref=False,
top=None,
left=None,
bottom=None,
right=None,
segmentation_map=None,
param=None,
target_height=None,
target_width=None,
center_row_rm=None,
center_col_rm=None,
tau_a=0.4,
tau_b=0.8,
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
**kwargs
):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
else:
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
# sampling
C, H, W = shape
size = (batch_size, C, H, W)
# print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {steps}')
device = self.model.betas.device
if x_T is None:
x = torch.randn(size, device=device)
else:
x = x_T
ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
if DPMencode:
# x_T is not a list
model_fn = model_wrapper(
lambda x, t, c, DPMencode, controller, inject: self.model.apply_model(x, t, c, encode=DPMencode, controller=None, inject=inject),
ns,
model_type=MODEL_TYPES[self.model.parameterization],
guidance_type="classifier-free",
condition=inv_emb,
unconditional_condition=inv_emb,
guidance_scale=unconditional_guidance_scale,
)
dpm_solver = DPM_Solver(model_fn, ns)
data, _ = self.low_order_sample(x, dpm_solver, steps, order, t_start, t_end, device, DPMencode=DPMencode)
for step in range(order, steps + 1):
data = dpm_solver.sample_one_step(data, step, steps, order=order, DPMencode=DPMencode)
return data['x'].to(device), None
else:
# x_T is a list
model_fn_decode = model_wrapper(
lambda x, t, c, DPMencode, controller, inject: self.model.apply_model(x, t, c, encode=DPMencode, controller=controller, inject=inject),
ns,
model_type=MODEL_TYPES[self.model.parameterization],
guidance_type="classifier-free",
condition=inv_emb,
unconditional_condition=inv_emb,
guidance_scale=unconditional_guidance_scale,
)
model_fn_gen = model_wrapper(
lambda x, t, c, DPMencode, controller, inject: self.model.apply_model(x, t, c, encode=DPMencode, controller=controller, inject=inject),
ns,
model_type=MODEL_TYPES[self.model.parameterization],
guidance_type="classifier-free",
condition=conditioning,
unconditional_condition=unconditional_conditioning,
guidance_scale=unconditional_guidance_scale,
)
orig_controller = ptp.AttentionStore()
ref_controller = ptp.AttentionStore()
cross_controller = ptp.AttentionStore()
gen_controller = ptp.AttentionStore()
Inject_controller = ptp.AttentionStore()
dpm_solver_decode = DPM_Solver(model_fn_decode, ns)
dpm_solver_gen = DPM_Solver(model_fn_gen, ns)
# decoded background
ptp_utils.register_attention_control(self.model, orig_controller, center_row_rm, center_col_rm, target_height, target_width,
width, height, top, left, bottom, right, segmentation_map=segmentation_map[0, 0].clone())
orig, orig_controller = self.low_order_sample(x[0], dpm_solver_decode, steps, order, t_start, t_end, device, DPMencode=DPMencode, controller=orig_controller)
# decoded reference
ptp_utils.register_attention_control(self.model, ref_controller, center_row_rm, center_col_rm, target_height, target_width,
width, height, top, left, bottom, right, segmentation_map=segmentation_map[0, 0].clone())
ref, ref_controller = self.low_order_sample(x[3], dpm_solver_decode, steps, order, t_start, t_end, device, DPMencode=DPMencode, controller=ref_controller)
# decode for cross-attention
ptp_utils.register_attention_control(self.model, cross_controller, center_row_rm, center_col_rm, target_height, target_width,
width, height, top, left, bottom, right, segmentation_map=segmentation_map[0, 0].clone(), pseudo_cross=True)
cross, cross_controller = self.low_order_sample(x[2], dpm_solver_decode, steps, order, t_start, t_end, device, DPMencode=DPMencode,
controller=cross_controller, ref_init=ref['x'].clone())
# generation
Inject_controller = [orig_controller, ref_controller, cross_controller]
ptp_utils.register_attention_control(self.model, gen_controller, center_row_rm, center_col_rm, target_height, target_width,
width, height, top, left, bottom, right, segmentation_map=segmentation_map[0, 0].clone(), inject_bg=True)
gen, _ = self.low_order_sample(x[4], dpm_solver_gen, steps, order, t_start, t_end, device,
DPMencode=DPMencode, controller=Inject_controller, inject=True)
for i in range(len(orig['model_prev_list'])):
blended = orig['model_prev_list'][i].clone()
blended[:, :, param[0] : param[1], param[2] : param[3]] \
= gen['model_prev_list'][i][:, :, param[0] : param[1], param[2] : param[3]].clone()
gen['model_prev_list'][i] = blended.clone()
del orig_controller, ref_controller, cross_controller, gen_controller, Inject_controller
orig_controller = ptp.AttentionStore()
ref_controller = ptp.AttentionStore()
cross_controller = ptp.AttentionStore()
gen_controller = ptp.AttentionStore()
for step in range(order, steps + 1):
# decoded background
ptp_utils.register_attention_control(self.model, orig_controller, center_row_rm, center_col_rm, target_height, target_width,
width, height, top, left, bottom, right, segmentation_map=segmentation_map[0, 0].clone())
orig = dpm_solver_decode.sample_one_step(orig, step, steps, order=order, DPMencode=DPMencode)
# decode for cross-attention
ptp_utils.register_attention_control(self.model, cross_controller, center_row_rm, center_col_rm, target_height, target_width,
width, height, top, left, bottom, right, segmentation_map=segmentation_map[0, 0].clone(), pseudo_cross=True)
cross['x'] = orig['x']
cross = dpm_solver_decode.sample_one_step(cross, step, steps, order=order, DPMencode=DPMencode, ref_init=ref['x'].clone())
if step < int(tau_a*(steps) + 1 - order):
inject = True
# decoded reference
ptp_utils.register_attention_control(self.model, ref_controller, center_row_rm, center_col_rm, target_height, target_width,
width, height, top, left, bottom, right, segmentation_map=segmentation_map[0, 0].clone())
ref = dpm_solver_decode.sample_one_step(ref, step, steps, order=order, DPMencode=DPMencode)
controller = [orig_controller, ref_controller, cross_controller]
else:
inject = False
controller = [orig_controller, None, cross_controller]
if step < int(0.4*(steps) + 1 - order):
inject_bg = True
else:
inject_bg = False
# generation
ptp_utils.register_attention_control(self.model, gen_controller, center_row_rm, center_col_rm, target_height, target_width, width, height,
top, left, bottom, right, segmentation_map=segmentation_map[0, 0].clone(), inject_bg=inject_bg)
gen = dpm_solver_gen.sample_one_step(gen, step, steps, order=order, DPMencode=DPMencode, controller=controller, inject=inject)
if step < int(tau_b*(steps) + 1 - order):
blended = orig['x'].clone()
blended[:, :, param[0] : param[1], param[2] : param[3]] \
= gen['x'][:, :, param[0] : param[1], param[2] : param[3]].clone()
gen['x'] = blended.clone()
del orig_controller, ref_controller, cross_controller, gen_controller, controller
return gen['x'].to(device), None
def low_order_sample(self, x, dpm_solver, steps, order, t_start, t_end, device, DPMencode=False, controller=None, inject=False, ref_init=None):
t_0 = 1. / dpm_solver.noise_schedule.total_N if t_end is None else t_end
t_T = dpm_solver.noise_schedule.T if t_start is None else t_start
total_controller = []
assert steps >= order
timesteps = dpm_solver.get_time_steps(skip_type="time_uniform", t_T=t_T, t_0=t_0, N=steps, device=device, DPMencode=DPMencode)
assert timesteps.shape[0] - 1 == steps
with torch.no_grad():
vec_t = timesteps[0].expand((x.shape[0]))
model_prev_list = [dpm_solver.model_fn(x, vec_t, DPMencode=DPMencode,
controller=[controller[0][0], controller[1][0], controller[2][0]] if isinstance(controller, list) else controller,
inject=inject, ref_init=ref_init)]
total_controller.append(controller)
t_prev_list = [vec_t]
# Init the first `order` values by lower order multistep DPM-Solver.
for init_order in range(1, order):
vec_t = timesteps[init_order].expand(x.shape[0])
x = dpm_solver.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order,
solver_type='dpmsolver', DPMencode=DPMencode)
model_prev_list.append(dpm_solver.model_fn(x, vec_t, DPMencode=DPMencode,
controller=[controller[0][init_order], controller[1][init_order], controller[2][init_order]] if isinstance(controller, list) else controller,
inject=inject, ref_init=ref_init))
total_controller.append(controller)
t_prev_list.append(vec_t)
return {'x': x, 'model_prev_list': model_prev_list, 't_prev_list': t_prev_list, 'timesteps':timesteps}, total_controller
|