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import os
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.models.attention_processor import AttnProcessor
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
import torch
import torch.nn.functional as F
import tqdm
import numpy as np
import safetensors
from PIL import Image
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import StableDiffusionPipeline
from argparse import ArgumentParser
import inspect
from utils.model_utils import get_img, slerp, do_replace_attn
from utils.lora_utils import train_lora, load_lora
from utils.alpha_scheduler import AlphaScheduler
class StoreProcessor():
def __init__(self, original_processor, value_dict, name):
self.original_processor = original_processor
self.value_dict = value_dict
self.name = name
self.value_dict[self.name] = dict()
self.id = 0
def __call__(self, attn, hidden_states, *args, encoder_hidden_states=None, attention_mask=None, **kwargs):
# Is self attention
if encoder_hidden_states is None:
self.value_dict[self.name][self.id] = hidden_states.detach()
self.id += 1
res = self.original_processor(attn, hidden_states, *args,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
**kwargs)
return res
class LoadProcessor():
def __init__(self, original_processor, name, img0_dict, img1_dict, alpha, beta=0, lamd=0.6):
super().__init__()
self.original_processor = original_processor
self.name = name
self.img0_dict = img0_dict
self.img1_dict = img1_dict
self.alpha = alpha
self.beta = beta
self.lamd = lamd
self.id = 0
def __call__(self, attn, hidden_states, *args, encoder_hidden_states=None, attention_mask=None, **kwargs):
# Is self attention
if encoder_hidden_states is None:
if self.id < 50 * self.lamd:
map0 = self.img0_dict[self.name][self.id]
map1 = self.img1_dict[self.name][self.id]
cross_map = self.beta * hidden_states + \
(1 - self.beta) * ((1 - self.alpha) * map0 + self.alpha * map1)
# cross_map = self.beta * hidden_states + \
# (1 - self.beta) * slerp(map0, map1, self.alpha)
# cross_map = slerp(slerp(map0, map1, self.alpha),
# hidden_states, self.beta)
# cross_map = hidden_states
# cross_map = torch.cat(
# ((1 - self.alpha) * map0, self.alpha * map1), dim=1)
res = self.original_processor(attn, hidden_states, *args,
encoder_hidden_states=cross_map,
attention_mask=attention_mask,
**kwargs)
else:
res = self.original_processor(attn, hidden_states, *args,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
**kwargs)
self.id += 1
# if self.id == len(self.img0_dict[self.name]):
if self.id == len(self.img0_dict[self.name]):
self.id = 0
else:
res = self.original_processor(attn, hidden_states, *args,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
**kwargs)
return res
class DiffMorpherPipeline(StableDiffusionPipeline):
def __init__(self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
image_encoder=None,
requires_safety_checker: bool = True,
):
sig = inspect.signature(super().__init__)
params = sig.parameters
if 'image_encoder' in params:
super().__init__(vae, text_encoder, tokenizer, unet, scheduler,
safety_checker, feature_extractor, image_encoder, requires_safety_checker)
else:
super().__init__(vae, text_encoder, tokenizer, unet, scheduler,
safety_checker, feature_extractor, requires_safety_checker)
self.img0_dict = dict()
self.img1_dict = dict()
def inv_step(
self,
model_output: torch.FloatTensor,
timestep: int,
x: torch.FloatTensor,
eta=0.,
verbose=False
):
"""
Inverse sampling for DDIM Inversion
"""
if verbose:
print("timestep: ", timestep)
next_step = timestep
timestep = min(timestep - self.scheduler.config.num_train_timesteps //
self.scheduler.num_inference_steps, 999)
alpha_prod_t = self.scheduler.alphas_cumprod[
timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
alpha_prod_t_next = self.scheduler.alphas_cumprod[next_step]
beta_prod_t = 1 - alpha_prod_t
pred_x0 = (x - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
pred_dir = (1 - alpha_prod_t_next)**0.5 * model_output
x_next = alpha_prod_t_next**0.5 * pred_x0 + pred_dir
return x_next, pred_x0
@torch.no_grad()
def invert(
self,
image: torch.Tensor,
prompt,
num_inference_steps=50,
num_actual_inference_steps=None,
guidance_scale=1.,
eta=0.0,
**kwds):
"""
invert a real image into noise map with determinisc DDIM inversion
"""
DEVICE = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
batch_size = image.shape[0]
if isinstance(prompt, list):
if batch_size == 1:
image = image.expand(len(prompt), -1, -1, -1)
elif isinstance(prompt, str):
if batch_size > 1:
prompt = [prompt] * batch_size
# text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=77,
return_tensors="pt"
)
text_embeddings = self.text_encoder(text_input.input_ids.to(DEVICE))[0]
print("input text embeddings :", text_embeddings.shape)
# define initial latents
latents = self.image2latent(image)
# unconditional embedding for classifier free guidance
if guidance_scale > 1.:
max_length = text_input.input_ids.shape[-1]
unconditional_input = self.tokenizer(
[""] * batch_size,
padding="max_length",
max_length=77,
return_tensors="pt"
)
unconditional_embeddings = self.text_encoder(
unconditional_input.input_ids.to(DEVICE))[0]
text_embeddings = torch.cat(
[unconditional_embeddings, text_embeddings], dim=0)
print("latents shape: ", latents.shape)
# interative sampling
self.scheduler.set_timesteps(num_inference_steps)
print("Valid timesteps: ", reversed(self.scheduler.timesteps))
# print("attributes: ", self.scheduler.__dict__)
latents_list = [latents]
pred_x0_list = [latents]
for i, t in enumerate(tqdm.tqdm(reversed(self.scheduler.timesteps), desc="DDIM Inversion")):
if num_actual_inference_steps is not None and i >= num_actual_inference_steps:
continue
if guidance_scale > 1.:
model_inputs = torch.cat([latents] * 2)
else:
model_inputs = latents
# predict the noise
noise_pred = self.unet(
model_inputs, t, encoder_hidden_states=text_embeddings).sample
if guidance_scale > 1.:
noise_pred_uncon, noise_pred_con = noise_pred.chunk(2, dim=0)
noise_pred = noise_pred_uncon + guidance_scale * \
(noise_pred_con - noise_pred_uncon)
# compute the previous noise sample x_t-1 -> x_t
latents, pred_x0 = self.inv_step(noise_pred, t, latents)
latents_list.append(latents)
pred_x0_list.append(pred_x0)
return latents
@torch.no_grad()
def ddim_inversion(self, latent, cond):
timesteps = reversed(self.scheduler.timesteps)
with torch.autocast(device_type='cuda', dtype=torch.float32):
for i, t in enumerate(tqdm.tqdm(timesteps, desc="DDIM inversion")):
cond_batch = cond.repeat(latent.shape[0], 1, 1)
alpha_prod_t = self.scheduler.alphas_cumprod[t]
alpha_prod_t_prev = (
self.scheduler.alphas_cumprod[timesteps[i - 1]]
if i > 0 else self.scheduler.final_alpha_cumprod
)
mu = alpha_prod_t ** 0.5
mu_prev = alpha_prod_t_prev ** 0.5
sigma = (1 - alpha_prod_t) ** 0.5
sigma_prev = (1 - alpha_prod_t_prev) ** 0.5
eps = self.unet(
latent, t, encoder_hidden_states=cond_batch).sample
pred_x0 = (latent - sigma_prev * eps) / mu_prev
latent = mu * pred_x0 + sigma * eps
# if save_latents:
# torch.save(latent, os.path.join(save_path, f'noisy_latents_{t}.pt'))
# torch.save(latent, os.path.join(save_path, f'noisy_latents_{t}.pt'))
return latent
def step(
self,
model_output: torch.FloatTensor,
timestep: int,
x: torch.FloatTensor,
):
"""
predict the sample of the next step in the denoise process.
"""
prev_timestep = timestep - \
self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = self.scheduler.alphas_cumprod[
prev_timestep] if prev_timestep > 0 else self.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
pred_x0 = (x - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
pred_dir = (1 - alpha_prod_t_prev)**0.5 * model_output
x_prev = alpha_prod_t_prev**0.5 * pred_x0 + pred_dir
return x_prev, pred_x0
@torch.no_grad()
def image2latent(self, image):
DEVICE = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
if type(image) is Image:
image = np.array(image)
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(2, 0, 1).unsqueeze(0)
# input image density range [-1, 1]
latents = self.vae.encode(image.to(DEVICE))['latent_dist'].mean
latents = latents * 0.18215
return latents
@torch.no_grad()
def latent2image(self, latents, return_type='np'):
latents = 1 / 0.18215 * latents.detach()
image = self.vae.decode(latents)['sample']
if return_type == 'np':
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
image = (image * 255).astype(np.uint8)
elif return_type == "pt":
image = (image / 2 + 0.5).clamp(0, 1)
return image
def latent2image_grad(self, latents):
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents)['sample']
return image # range [-1, 1]
@torch.no_grad()
def cal_latent(self, num_inference_steps, guidance_scale, unconditioning, img_noise_0, img_noise_1, text_embeddings_0, text_embeddings_1, lora_0, lora_1, alpha, use_lora, fix_lora=None):
# latents = torch.cos(alpha * torch.pi / 2) * img_noise_0 + \
# torch.sin(alpha * torch.pi / 2) * img_noise_1
# latents = (1 - alpha) * img_noise_0 + alpha * img_noise_1
# latents = latents / ((1 - alpha) ** 2 + alpha ** 2)
latents = slerp(img_noise_0, img_noise_1, alpha, self.use_adain)
text_embeddings = (1 - alpha) * text_embeddings_0 + \
alpha * text_embeddings_1
self.scheduler.set_timesteps(num_inference_steps)
if use_lora:
if fix_lora is not None:
self.unet = load_lora(self.unet, lora_0, lora_1, fix_lora)
else:
self.unet = load_lora(self.unet, lora_0, lora_1, alpha)
for i, t in enumerate(tqdm.tqdm(self.scheduler.timesteps, desc=f"DDIM Sampler, alpha={alpha}")):
if guidance_scale > 1.:
model_inputs = torch.cat([latents] * 2)
else:
model_inputs = latents
if unconditioning is not None and isinstance(unconditioning, list):
_, text_embeddings = text_embeddings.chunk(2)
text_embeddings = torch.cat(
[unconditioning[i].expand(*text_embeddings.shape), text_embeddings])
# predict the noise
noise_pred = self.unet(
model_inputs, t, encoder_hidden_states=text_embeddings).sample
if guidance_scale > 1.0:
noise_pred_uncon, noise_pred_con = noise_pred.chunk(
2, dim=0)
noise_pred = noise_pred_uncon + guidance_scale * \
(noise_pred_con - noise_pred_uncon)
# compute the previous noise sample x_t -> x_t-1
latents = self.scheduler.step(
noise_pred, t, latents, return_dict=False)[0]
return latents
@torch.no_grad()
def get_text_embeddings(self, prompt, guidance_scale, neg_prompt, batch_size):
DEVICE = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
# text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=77,
return_tensors="pt"
)
text_embeddings = self.text_encoder(text_input.input_ids.cuda())[0]
if guidance_scale > 1.:
if neg_prompt:
uc_text = neg_prompt
else:
uc_text = ""
unconditional_input = self.tokenizer(
[uc_text] * batch_size,
padding="max_length",
max_length=77,
return_tensors="pt"
)
unconditional_embeddings = self.text_encoder(
unconditional_input.input_ids.to(DEVICE))[0]
text_embeddings = torch.cat(
[unconditional_embeddings, text_embeddings], dim=0)
return text_embeddings
def __call__(
self,
img_0=None,
img_1=None,
img_path_0=None,
img_path_1=None,
prompt_0="",
prompt_1="",
save_lora_dir="./lora",
load_lora_path_0=None,
load_lora_path_1=None,
lora_steps=200,
lora_lr=2e-4,
lora_rank=16,
batch_size=1,
height=512,
width=512,
num_inference_steps=50,
num_actual_inference_steps=None,
guidance_scale=1,
attn_beta=0,
lamd=0.6,
use_lora=True,
use_adain=True,
use_reschedule=True,
output_path="./results",
num_frames=50,
fix_lora=None,
progress=tqdm,
unconditioning=None,
neg_prompt=None,
save_intermediates=False,
**kwds):
# if isinstance(prompt, list):
# batch_size = len(prompt)
# elif isinstance(prompt, str):
# if batch_size > 1:
# prompt = [prompt] * batch_size
self.scheduler.set_timesteps(num_inference_steps)
self.use_lora = use_lora
self.use_adain = use_adain
self.use_reschedule = use_reschedule
self.output_path = output_path
if img_0 is None:
img_0 = Image.open(img_path_0).convert("RGB")
# else:
# img_0 = Image.fromarray(img_0).convert("RGB")
if img_1 is None:
img_1 = Image.open(img_path_1).convert("RGB")
# else:
# img_1 = Image.fromarray(img_1).convert("RGB")
if self.use_lora:
print("Loading lora...")
if not load_lora_path_0:
weight_name = f"{output_path.split('/')[-1]}_lora_0.ckpt"
load_lora_path_0 = save_lora_dir + "/" + weight_name
if not os.path.exists(load_lora_path_0):
train_lora(img_0, prompt_0, save_lora_dir, None, self.tokenizer, self.text_encoder,
self.vae, self.unet, self.scheduler, lora_steps, lora_lr, lora_rank, weight_name=weight_name)
print(f"Load from {load_lora_path_0}.")
if load_lora_path_0.endswith(".safetensors"):
lora_0 = safetensors.torch.load_file(
load_lora_path_0, device="cpu")
else:
lora_0 = torch.load(load_lora_path_0, map_location="cpu")
if not load_lora_path_1:
weight_name = f"{output_path.split('/')[-1]}_lora_1.ckpt"
load_lora_path_1 = save_lora_dir + "/" + weight_name
if not os.path.exists(load_lora_path_1):
train_lora(img_1, prompt_1, save_lora_dir, None, self.tokenizer, self.text_encoder,
self.vae, self.unet, self.scheduler, lora_steps, lora_lr, lora_rank, weight_name=weight_name)
print(f"Load from {load_lora_path_1}.")
if load_lora_path_1.endswith(".safetensors"):
lora_1 = safetensors.torch.load_file(
load_lora_path_1, device="cpu")
else:
lora_1 = torch.load(load_lora_path_1, map_location="cpu")
else:
lora_0 = lora_1 = None
text_embeddings_0 = self.get_text_embeddings(
prompt_0, guidance_scale, neg_prompt, batch_size)
text_embeddings_1 = self.get_text_embeddings(
prompt_1, guidance_scale, neg_prompt, batch_size)
img_0 = get_img(img_0)
img_1 = get_img(img_1)
if self.use_lora:
self.unet = load_lora(self.unet, lora_0, lora_1, 0)
img_noise_0 = self.ddim_inversion(
self.image2latent(img_0), text_embeddings_0)
if self.use_lora:
self.unet = load_lora(self.unet, lora_0, lora_1, 1)
img_noise_1 = self.ddim_inversion(
self.image2latent(img_1), text_embeddings_1)
print("latents shape: ", img_noise_0.shape)
original_processor = list(self.unet.attn_processors.values())[0]
def morph(alpha_list, progress, desc):
images = []
if attn_beta is not None:
if self.use_lora:
self.unet = load_lora(
self.unet, lora_0, lora_1, 0 if fix_lora is None else fix_lora)
attn_processor_dict = {}
for k in self.unet.attn_processors.keys():
if do_replace_attn(k):
if self.use_lora:
attn_processor_dict[k] = StoreProcessor(self.unet.attn_processors[k],
self.img0_dict, k)
else:
attn_processor_dict[k] = StoreProcessor(original_processor,
self.img0_dict, k)
else:
attn_processor_dict[k] = self.unet.attn_processors[k]
self.unet.set_attn_processor(attn_processor_dict)
latents = self.cal_latent(
num_inference_steps,
guidance_scale,
unconditioning,
img_noise_0,
img_noise_1,
text_embeddings_0,
text_embeddings_1,
lora_0,
lora_1,
alpha_list[0],
False,
fix_lora
)
first_image = self.latent2image(latents)
first_image = Image.fromarray(first_image)
if save_intermediates:
first_image.save(f"{self.output_path}/{0:02d}.png")
if self.use_lora:
self.unet = load_lora(
self.unet, lora_0, lora_1, 1 if fix_lora is None else fix_lora)
attn_processor_dict = {}
for k in self.unet.attn_processors.keys():
if do_replace_attn(k):
if self.use_lora:
attn_processor_dict[k] = StoreProcessor(self.unet.attn_processors[k],
self.img1_dict, k)
else:
attn_processor_dict[k] = StoreProcessor(original_processor,
self.img1_dict, k)
else:
attn_processor_dict[k] = self.unet.attn_processors[k]
self.unet.set_attn_processor(attn_processor_dict)
latents = self.cal_latent(
num_inference_steps,
guidance_scale,
unconditioning,
img_noise_0,
img_noise_1,
text_embeddings_0,
text_embeddings_1,
lora_0,
lora_1,
alpha_list[-1],
False,
fix_lora
)
last_image = self.latent2image(latents)
last_image = Image.fromarray(last_image)
if save_intermediates:
last_image.save(
f"{self.output_path}/{num_frames - 1:02d}.png")
for i in progress.tqdm(range(1, num_frames - 1), desc=desc):
alpha = alpha_list[i]
if self.use_lora:
self.unet = load_lora(
self.unet, lora_0, lora_1, alpha if fix_lora is None else fix_lora)
attn_processor_dict = {}
for k in self.unet.attn_processors.keys():
if do_replace_attn(k):
if self.use_lora:
attn_processor_dict[k] = LoadProcessor(
self.unet.attn_processors[k], k, self.img0_dict, self.img1_dict, alpha, attn_beta, lamd)
else:
attn_processor_dict[k] = LoadProcessor(
original_processor, k, self.img0_dict, self.img1_dict, alpha, attn_beta, lamd)
else:
attn_processor_dict[k] = self.unet.attn_processors[k]
self.unet.set_attn_processor(attn_processor_dict)
latents = self.cal_latent(
num_inference_steps,
guidance_scale,
unconditioning,
img_noise_0,
img_noise_1,
text_embeddings_0,
text_embeddings_1,
lora_0,
lora_1,
alpha_list[i],
False,
fix_lora
)
image = self.latent2image(latents)
image = Image.fromarray(image)
if save_intermediates:
image.save(f"{self.output_path}/{i:02d}.png")
images.append(image)
images = [first_image] + images + [last_image]
else:
for k, alpha in enumerate(alpha_list):
latents = self.cal_latent(
num_inference_steps,
guidance_scale,
unconditioning,
img_noise_0,
img_noise_1,
text_embeddings_0,
text_embeddings_1,
lora_0,
lora_1,
alpha_list[k],
self.use_lora,
fix_lora
)
image = self.latent2image(latents)
image = Image.fromarray(image)
if save_intermediates:
image.save(f"{self.output_path}/{k:02d}.png")
images.append(image)
return images
with torch.no_grad():
if self.use_reschedule:
alpha_scheduler = AlphaScheduler()
alpha_list = list(torch.linspace(0, 1, num_frames))
images_pt = morph(alpha_list, progress, "Sampling...")
images_pt = [transforms.ToTensor()(img).unsqueeze(0)
for img in images_pt]
alpha_scheduler.from_imgs(images_pt)
alpha_list = alpha_scheduler.get_list()
print(alpha_list)
images = morph(alpha_list, progress, "Reschedule..."
)
else:
alpha_list = list(torch.linspace(0, 1, num_frames))
print(alpha_list)
images = morph(alpha_list, progress, "Sampling...")
return images
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