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import torch.nn as nn
import torchvision
from scipy.spatial import Delaunay
import torch
import numpy as np
from torch.nn import functional as nnf
from easydict import EasyDict
from shapely.geometry import Point
from shapely.geometry.polygon import Polygon
from diffusers import StableDiffusionPipeline
class SDSLoss(nn.Module):
def __init__(self, cfg, device):
super(SDSLoss, self).__init__()
self.cfg = cfg
self.device = device
self.fp16 = device.type == "cuda"
dtype = torch.float16 if self.fp16 else torch.float32
self.pipe = StableDiffusionPipeline.from_pretrained(
cfg.diffusion.model,
torch_dtype=dtype,
token=cfg.token,
).to(device)
self.pipe = StableDiffusionPipeline.from_pretrained(
cfg.diffusion.model,
torch_dtype=torch.float32,
token=cfg.token,
).to("cpu")
if self.fp16:
# self.pipe.enable_xformers_memory_efficient_attention()
self.pipe.enable_attention_slicing(slice_size=1)
self.pipe.enable_vae_slicing()
self.pipe.enable_vae_tiling()
self.pipe.unet.enable_gradient_checkpointing()
alphas_cumprod = torch.tensor(self.pipe.scheduler.alphas_cumprod)
self.alphas = alphas_cumprod.to(device)
self.sigmas = torch.sqrt(1 - self.alphas)
# 1️⃣ embed text while all weights are still real tensors
self.embed_text()
# 2️⃣ NOW turn on off-loading (only UNet & VAE get meta tensors)
#self.pipe.enable_model_cpu_offload()
# text-encoder is no longer needed
#del self.pipe.text_encoder, self.pipe.tokenizer
def embed_text(self):
tok = self.pipe.tokenizer
txt = tok(self.cfg.caption, padding="max_length",
max_length=tok.model_max_length, truncation=True,
return_tensors="pt")
un = tok([""], padding="max_length",
max_length=tok.model_max_length, return_tensors="pt")
with torch.no_grad():
te = self.pipe.text_encoder.eval()
em_txt = te(txt.input_ids).last_hidden_state.to(torch.float32)
em_un = te(un .input_ids).last_hidden_state.to(torch.float32)
self.text_embeddings = (
torch.cat([em_un, em_txt])
.repeat_interleave(self.cfg.batch_size, 0)
.to(self.device)
)
def forward(self, x_aug: torch.Tensor) -> torch.Tensor:
# ---------------------------------------------------- encode
x = (x_aug * 2.0 - 1.0).to(self.device, dtype=torch.float32)
if self.fp16:
with torch.cuda.amp.autocast():
latents = self.pipe.vae.encode(x).latent_dist.sample()
else:
latents = self.pipe.vae.encode(x).latent_dist.sample()
latents = 0.18215 * latents
torch.cuda.empty_cache()
# ---------------------------------------------------- add noise
t = torch.randint(
50,
min(950, self.cfg.diffusion.timesteps) - 1,
(latents.size(0),),
device=self.device,
)
eps = torch.randn_like(latents)
z_t = self.pipe.scheduler.add_noise(latents, eps, t)
# ---------------------------------------------------- sequential CFG
emb_u, emb_c = self.text_embeddings.chunk(2)
with torch.cuda.amp.autocast():
eps_u = self.pipe.unet(z_t, t, encoder_hidden_states=emb_u).sample
torch.cuda.empty_cache() # release ~500 MB
with torch.cuda.amp.autocast():
eps_c = self.pipe.unet(z_t, t, encoder_hidden_states=emb_c).sample
# UNet already ran in fp16 under autocast – avoid duplicating tensors
eps_t = eps_u + self.cfg.diffusion.guidance_scale * (eps_c - eps_u)
# ---------------------------------------------------- SDS grad & loss
alpha_t = self.alphas[t].to(self.device)
sigma_t = self.sigmas[t].to(self.device)
grad = (alpha_t**0.5 * sigma_t * (eps_t - eps)).nan_to_num_()
return (grad * latents).sum(1).mean()
class ToneLoss(nn.Module):
def __init__(self, cfg):
super(ToneLoss, self).__init__()
self.dist_loss_weight = cfg.loss.tone.dist_loss_weight
self.im_init = None
self.cfg = cfg
self.mse_loss = nn.MSELoss()
self.blurrer = torchvision.transforms.GaussianBlur(kernel_size=(cfg.loss.tone.pixel_dist_kernel_blur,
cfg.loss.tone.pixel_dist_kernel_blur), sigma=(cfg.loss.tone.pixel_dist_sigma))
def set_image_init(self, im_init):
self.im_init = im_init.permute(2, 0, 1).unsqueeze(0)
self.init_blurred = self.blurrer(self.im_init)
def get_scheduler(self, step=None):
if step is not None:
return self.dist_loss_weight * np.exp(-(1/5)*((step-300)/(20)) ** 2)
else:
return self.dist_loss_weight
def forward(self, cur_raster, step=None):
blurred_cur = self.blurrer(cur_raster)
return self.mse_loss(self.init_blurred.detach(), blurred_cur) * self.get_scheduler(step)
class ConformalLoss:
def __init__(self, parameters: EasyDict, device: torch.device, target_letter: str, shape_groups):
self.parameters = parameters
self.target_letter = target_letter
self.shape_groups = shape_groups
self.faces = self.init_faces(device)
self.faces_roll_a = [torch.roll(self.faces[i], 1, 1) for i in range(len(self.faces))]
with torch.no_grad():
self.angles = []
self.reset()
def get_angles(self, points: torch.Tensor) -> torch.Tensor:
angles_ = []
for i in range(len(self.faces)):
triangles = points[self.faces[i]]
triangles_roll_a = points[self.faces_roll_a[i]]
edges = triangles_roll_a - triangles
length = edges.norm(dim=-1)
edges = edges / (length + 1e-1)[:, :, None]
edges_roll = torch.roll(edges, 1, 1)
cosine = torch.einsum('ned,ned->ne', edges, edges_roll)
angles = torch.arccos(cosine)
angles_.append(angles)
return angles_
def get_letter_inds(self, letter_to_insert):
for group, l in zip(self.shape_groups, self.target_letter):
if l == letter_to_insert:
letter_inds = group.shape_ids
return letter_inds[0], letter_inds[-1], len(letter_inds)
def reset(self):
points = torch.cat([point.clone().detach() for point in self.parameters.point]).to(self.faces[0].device)
self.angles = self.get_angles(points)
def init_faces(self, device: torch.device) -> torch.tensor:
faces_ = []
for j, c in enumerate(self.target_letter):
points_np = [self.parameters.point[i].clone().detach().cpu().numpy() for i in range(len(self.parameters.point))]
start_ind, end_ind, shapes_per_letter = self.get_letter_inds(c)
print(c, start_ind, end_ind)
holes = []
if shapes_per_letter > 1:
holes = points_np[start_ind+1:end_ind]
poly = Polygon(points_np[start_ind], holes=holes)
poly = poly.buffer(0)
points_np = np.concatenate(points_np)
faces = Delaunay(points_np).simplices
is_intersect = np.array([poly.contains(Point(points_np[face].mean(0))) for face in faces], dtype=bool)
faces_.append(torch.from_numpy(faces[is_intersect]).to(device, dtype=torch.int64))
return faces_
def __call__(self) -> torch.Tensor:
loss_angles = 0
points = torch.cat(self.parameters.point).to(self.faces[0].device)
angles = self.get_angles(points)
for i in range(len(self.faces)):
loss_angles += (nnf.mse_loss(angles[i], self.angles[i]))
return loss_angles
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