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import torch
import numpy as np
from einops import rearrange
from torch import autocast
from contextlib import nullcontext
from math import sqrt
from adapt import ScoreAdapter
from cldm.model import create_model, load_state_dict
from lora_util import *
import warnings
from transformers import logging
warnings.filterwarnings("ignore", category=DeprecationWarning)
logging.set_verbosity_error()
device = torch.device("cuda")
def _sqrt(x):
if isinstance(x, float):
return sqrt(x)
else:
assert isinstance(x, torch.Tensor)
return torch.sqrt(x)
def load_embedding(model,embedding):
length=len(embedding['string_to_param']['*'])
voc=[]
for i in range(length):
voc.append(f'<{str(i)}>')
print(f"Added Token: {voc}")
model.cond_stage_model.tokenizer._add_tokens(voc)
x=torch.nn.Embedding(model.cond_stage_model.tokenizer.__len__(),768)
for params in x.parameters():
params.requires_grad=False
x.weight[:-length]=model.cond_stage_model.transformer.text_model.embeddings.token_embedding.weight
x.weight[-length:]=embedding['string_to_param']['*']
model.cond_stage_model.transformer.text_model.embeddings.token_embedding=x
def load_3DFuse(control,dir,alpha):
######################LOADCONTROL###########################
model = create_model(control['control_yaml']).cpu()
model.load_state_dict(load_state_dict(control['control_weight'], location='cuda'))
state_dict, l = merge("runwayml/stable-diffusion-v1-5",dir,alpha)
#######################OVERRIDE#############################
model.load_state_dict(state_dict,strict=False)
#######################ADDEMBBEDDING########################
load_embedding(model,l)
###############################################################
return model
class StableDiffusion(ScoreAdapter):
def __init__(self, variant, v2_highres, prompt, scale, precision, dir, alpha=1.0):
model=load_3DFuse(self.checkpoint_root(),dir,alpha)
self.model = model.cuda()
H , W = (512, 512)
ae_resolution_f = 8
self._device = self.model._device
self.prompt = prompt
self.scale = scale
self.precision = precision
self.precision_scope = autocast if self.precision == "autocast" else nullcontext
self._data_shape = (4, H // ae_resolution_f, W // ae_resolution_f)
self.cond_func = self.model.get_learned_conditioning
self.M = 1000
noise_schedule = "linear"
self.noise_schedule = noise_schedule
self.us = self.linear_us(self.M)
def data_shape(self):
return self._data_shape
@property
def σ_max(self):
return self.us[0]
@property
def σ_min(self):
return self.us[-1]
@torch.no_grad()
def denoise(self, xs, σ,control, **model_kwargs):
with self.precision_scope("cuda"):
with self.model.ema_scope():
N = xs.shape[0]
c = model_kwargs.pop('c')
uc = model_kwargs.pop('uc')
conditional_conditioning = {"c_concat": [control], "c_crossattn": [c]}
unconditional_conditioning = {"c_concat": [control], "c_crossattn": [uc]}
cond_t, σ = self.time_cond_vec(N, σ)
unscaled_xs = xs
xs = xs / _sqrt(1 + σ**2)
if uc is None or self.scale == 1.:
output = self.model.apply_model(xs, cond_t, c)
else:
x_in = torch.cat([xs] * 2)
t_in = torch.cat([cond_t] * 2)
c_in = dict()
for k in conditional_conditioning:
if isinstance(conditional_conditioning[k], list):
c_in[k] = [torch.cat([
unconditional_conditioning[k][i],
conditional_conditioning[k][i]]) for i in range(len(conditional_conditioning[k]))]
else:
c_in[k] = torch.cat([
unconditional_conditioning[k],
conditional_conditioning[k]])
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
output = e_t_uncond + self.scale * (e_t - e_t_uncond)
if self.model.parameterization == "v":
output = self.model.predict_eps_from_z_and_v(xs, cond_t, output)
else:
output = output
Ds = unscaled_xs - σ * output
return Ds
def cond_info(self, batch_size):
prompts = batch_size * [self.prompt]
return self.prompts_emb(prompts)
@torch.no_grad()
def prompts_emb(self, prompts):
assert isinstance(prompts, list)
batch_size = len(prompts)
with self.precision_scope("cuda"):
with self.model.ema_scope():
cond = {}
c = self.cond_func(prompts)
cond['c'] = c
uc = None
if self.scale != 1.0:
uc = self.cond_func(batch_size * [""])
cond['uc'] = uc
return cond
def unet_is_cond(self):
return True
def use_cls_guidance(self):
return False
def snap_t_to_nearest_tick(self, t):
j = np.abs(t - self.us).argmin()
return self.us[j], j
def time_cond_vec(self, N, σ):
if isinstance(σ, float):
σ, j = self.snap_t_to_nearest_tick(σ) # σ might change due to snapping
cond_t = (self.M - 1) - j
cond_t = torch.tensor([cond_t] * N, device=self.device)
return cond_t, σ
else:
assert isinstance(σ, torch.Tensor)
σ = σ.reshape(-1).cpu().numpy()
σs = []
js = []
for elem in σ:
_σ, _j = self.snap_t_to_nearest_tick(elem)
σs.append(_σ)
js.append((self.M - 1) - _j)
cond_t = torch.tensor(js, device=self.device)
σs = torch.tensor(σs, device=self.device, dtype=torch.float32).reshape(-1, 1, 1, 1)
return cond_t, σs
@staticmethod
def linear_us(M=1000):
assert M == 1000
β_start = 0.00085
β_end = 0.0120
βs = np.linspace(β_start**0.5, β_end**0.5, M, dtype=np.float64)**2
αs = np.cumprod(1 - βs)
us = np.sqrt((1 - αs) / αs)
us = us[::-1]
return us
@torch.no_grad()
def encode(self, xs):
model = self.model
with self.precision_scope("cuda"):
with self.model.ema_scope():
zs = model.get_first_stage_encoding(
model.encode_first_stage(xs)
)
return zs
@torch.no_grad()
def decode(self, xs):
with self.precision_scope("cuda"):
with self.model.ema_scope():
xs = self.model.decode_first_stage(xs)
return xs
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