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import torch
from .scalers import *
from .targets import *
from .schedulers import *
from .noise_conditions import *
from .loss_weights import *
from .samplers import *
import torch.nn.functional as F
import math
class GDF():
def __init__(self, schedule, input_scaler, target, noise_cond, loss_weight, offset_noise=0):
self.schedule = schedule
self.input_scaler = input_scaler
self.target = target
self.noise_cond = noise_cond
self.loss_weight = loss_weight
self.offset_noise = offset_noise
def setup_limits(self, stretch_max=True, stretch_min=True, shift=1):
stretched_limits = self.input_scaler.setup_limits(self.schedule, self.input_scaler, stretch_max, stretch_min, shift)
return stretched_limits
def diffuse(self, x0, epsilon=None, t=None, shift=1, loss_shift=1, offset=None):
if epsilon is None:
epsilon = torch.randn_like(x0)
if self.offset_noise > 0:
if offset is None:
offset = torch.randn([x0.size(0), x0.size(1)] + [1]*(len(x0.shape)-2)).to(x0.device)
epsilon = epsilon + offset * self.offset_noise
logSNR = self.schedule(x0.size(0) if t is None else t, shift=shift).to(x0.device)
a, b = self.input_scaler(logSNR) # B
if len(a.shape) == 1:
a, b = a.view(-1, *[1]*(len(x0.shape)-1)), b.view(-1, *[1]*(len(x0.shape)-1)) # BxCxHxW
#print('in line 33 a b', a.shape, b.shape, x0.shape, logSNR.shape, logSNR, self.noise_cond(logSNR))
target = self.target(x0, epsilon, logSNR, a, b)
# noised, noise, logSNR, t_cond
#noised, noise, target, logSNR, noise_cond, loss_weight
return x0 * a + epsilon * b, epsilon, target, logSNR, self.noise_cond(logSNR), self.loss_weight(logSNR, shift=loss_shift)
def undiffuse(self, x, logSNR, pred):
a, b = self.input_scaler(logSNR)
if len(a.shape) == 1:
a, b = a.view(-1, *[1]*(len(x.shape)-1)), b.view(-1, *[1]*(len(x.shape)-1))
return self.target.x0(x, pred, logSNR, a, b), self.target.epsilon(x, pred, logSNR, a, b)
def sample(self, model, model_inputs, shape, unconditional_inputs=None, sampler=None, schedule=None, t_start=1.0, t_end=0.0, timesteps=20, x_init=None, cfg=3.0, cfg_t_stop=None, cfg_t_start=None, cfg_rho=0.7, sampler_params=None, shift=1, device="cpu"):
sampler_params = {} if sampler_params is None else sampler_params
if sampler is None:
sampler = DDPMSampler(self)
r_range = torch.linspace(t_start, t_end, timesteps+1)
schedule = self.schedule if schedule is None else schedule
logSNR_range = schedule(r_range, shift=shift)[:, None].expand(
-1, shape[0] if x_init is None else x_init.size(0)
).to(device)
x = sampler.init_x(shape).to(device) if x_init is None else x_init.clone()
if cfg is not None:
if unconditional_inputs is None:
unconditional_inputs = {k: torch.zeros_like(v) for k, v in model_inputs.items()}
model_inputs = {
k: torch.cat([v, v_u], dim=0) if isinstance(v, torch.Tensor)
else [torch.cat([vi, vi_u], dim=0) if isinstance(vi, torch.Tensor) and isinstance(vi_u, torch.Tensor) else None for vi, vi_u in zip(v, v_u)] if isinstance(v, list)
else {vk: torch.cat([v[vk], v_u.get(vk, torch.zeros_like(v[vk]))], dim=0) for vk in v} if isinstance(v, dict)
else None for (k, v), (k_u, v_u) in zip(model_inputs.items(), unconditional_inputs.items())
}
for i in range(0, timesteps):
noise_cond = self.noise_cond(logSNR_range[i])
if cfg is not None and (cfg_t_stop is None or r_range[i].item() >= cfg_t_stop) and (cfg_t_start is None or r_range[i].item() <= cfg_t_start):
cfg_val = cfg
if isinstance(cfg_val, (list, tuple)):
assert len(cfg_val) == 2, "cfg must be a float or a list/tuple of length 2"
cfg_val = cfg_val[0] * r_range[i].item() + cfg_val[1] * (1-r_range[i].item())
pred, pred_unconditional = model(torch.cat([x, x], dim=0), noise_cond.repeat(2), **model_inputs).chunk(2)
pred_cfg = torch.lerp(pred_unconditional, pred, cfg_val)
if cfg_rho > 0:
std_pos, std_cfg = pred.std(), pred_cfg.std()
pred = cfg_rho * (pred_cfg * std_pos/(std_cfg+1e-9)) + pred_cfg * (1-cfg_rho)
else:
pred = pred_cfg
else:
pred = model(x, noise_cond, **model_inputs)
x0, epsilon = self.undiffuse(x, logSNR_range[i], pred)
x = sampler(x, x0, epsilon, logSNR_range[i], logSNR_range[i+1], **sampler_params)
#print('in line 86', x0.shape, x.shape, i, )
altered_vars = yield (x0, x, pred)
# Update some running variables if the user wants
if altered_vars is not None:
cfg = altered_vars.get('cfg', cfg)
cfg_rho = altered_vars.get('cfg_rho', cfg_rho)
sampler = altered_vars.get('sampler', sampler)
model_inputs = altered_vars.get('model_inputs', model_inputs)
x = altered_vars.get('x', x)
x_init = altered_vars.get('x_init', x_init)
class GDF_dual_fixlrt(GDF):
def ref_noise(self, noised, x0, logSNR):
a, b = self.input_scaler(logSNR)
if len(a.shape) == 1:
a, b = a.view(-1, *[1]*(len(x0.shape)-1)), b.view(-1, *[1]*(len(x0.shape)-1))
#print('in line 210', a.shape, b.shape, x0.shape, noised.shape)
return self.target.noise_givenx0_noised(x0, noised, logSNR, a, b)
def sample(self, model, model_inputs, shape, shape_lr, unconditional_inputs=None, sampler=None,
schedule=None, t_start=1.0, t_end=0.0, timesteps=20, x_init=None, cfg=3.0, cfg_t_stop=None,
cfg_t_start=None, cfg_rho=0.7, sampler_params=None, shift=1, device="cpu"):
sampler_params = {} if sampler_params is None else sampler_params
if sampler is None:
sampler = DDPMSampler(self)
r_range = torch.linspace(t_start, t_end, timesteps+1)
schedule = self.schedule if schedule is None else schedule
logSNR_range = schedule(r_range, shift=shift)[:, None].expand(
-1, shape[0] if x_init is None else x_init.size(0)
).to(device)
x = sampler.init_x(shape).to(device) if x_init is None else x_init.clone()
x_lr = sampler.init_x(shape_lr).to(device) if x_init is None else x_init.clone()
if cfg is not None:
if unconditional_inputs is None:
unconditional_inputs = {k: torch.zeros_like(v) for k, v in model_inputs.items()}
model_inputs = {
k: torch.cat([v, v_u], dim=0) if isinstance(v, torch.Tensor)
else [torch.cat([vi, vi_u], dim=0) if isinstance(vi, torch.Tensor) and isinstance(vi_u, torch.Tensor) else None for vi, vi_u in zip(v, v_u)] if isinstance(v, list)
else {vk: torch.cat([v[vk], v_u.get(vk, torch.zeros_like(v[vk]))], dim=0) for vk in v} if isinstance(v, dict)
else None for (k, v), (k_u, v_u) in zip(model_inputs.items(), unconditional_inputs.items())
}
###############################################lr sampling
guide_feas = [None] * timesteps
for i in range(0, timesteps):
noise_cond = self.noise_cond(logSNR_range[i])
if cfg is not None and (cfg_t_stop is None or r_range[i].item() >= cfg_t_stop) and (cfg_t_start is None or r_range[i].item() <= cfg_t_start):
cfg_val = cfg
if isinstance(cfg_val, (list, tuple)):
assert len(cfg_val) == 2, "cfg must be a float or a list/tuple of length 2"
cfg_val = cfg_val[0] * r_range[i].item() + cfg_val[1] * (1-r_range[i].item())
if i == timesteps -1 :
output, guide_lr_enc, guide_lr_dec = model(torch.cat([x_lr, x_lr], dim=0), noise_cond.repeat(2), reuire_f=True, **model_inputs)
guide_feas[i] = ([f.chunk(2)[0].repeat(2, 1, 1, 1) for f in guide_lr_enc], [f.chunk(2)[0].repeat(2, 1, 1, 1) for f in guide_lr_dec])
else:
output, _, _ = model(torch.cat([x_lr, x_lr], dim=0), noise_cond.repeat(2), reuire_f=True, **model_inputs)
pred, pred_unconditional = output.chunk(2)
pred_cfg = torch.lerp(pred_unconditional, pred, cfg_val)
if cfg_rho > 0:
std_pos, std_cfg = pred.std(), pred_cfg.std()
pred = cfg_rho * (pred_cfg * std_pos/(std_cfg+1e-9)) + pred_cfg * (1-cfg_rho)
else:
pred = pred_cfg
else:
pred = model(x_lr, noise_cond, **model_inputs)
x0_lr, epsilon_lr = self.undiffuse(x_lr, logSNR_range[i], pred)
x_lr = sampler(x_lr, x0_lr, epsilon_lr, logSNR_range[i], logSNR_range[i+1], **sampler_params)
###############################################hr HR sampling
for i in range(0, timesteps):
noise_cond = self.noise_cond(logSNR_range[i])
if cfg is not None and (cfg_t_stop is None or r_range[i].item() >= cfg_t_stop) and (cfg_t_start is None or r_range[i].item() <= cfg_t_start):
cfg_val = cfg
if isinstance(cfg_val, (list, tuple)):
assert len(cfg_val) == 2, "cfg must be a float or a list/tuple of length 2"
cfg_val = cfg_val[0] * r_range[i].item() + cfg_val[1] * (1-r_range[i].item())
out_pred, t_emb = model(torch.cat([x, x], dim=0), noise_cond.repeat(2), \
lr_guide=guide_feas[timesteps -1] if i <=19 else None , **model_inputs, require_t=True, guide_weight=1 - i/timesteps)
pred, pred_unconditional = out_pred.chunk(2)
pred_cfg = torch.lerp(pred_unconditional, pred, cfg_val)
if cfg_rho > 0:
std_pos, std_cfg = pred.std(), pred_cfg.std()
pred = cfg_rho * (pred_cfg * std_pos/(std_cfg+1e-9)) + pred_cfg * (1-cfg_rho)
else:
pred = pred_cfg
else:
pred = model(x, noise_cond, guide_lr=(guide_lr_enc, guide_lr_dec), **model_inputs)
x0, epsilon = self.undiffuse(x, logSNR_range[i], pred)
x = sampler(x, x0, epsilon, logSNR_range[i], logSNR_range[i+1], **sampler_params)
altered_vars = yield (x0, x, pred, x_lr)
# Update some running variables if the user wants
if altered_vars is not None:
cfg = altered_vars.get('cfg', cfg)
cfg_rho = altered_vars.get('cfg_rho', cfg_rho)
sampler = altered_vars.get('sampler', sampler)
model_inputs = altered_vars.get('model_inputs', model_inputs)
x = altered_vars.get('x', x)
x_init = altered_vars.get('x_init', x_init)
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