# Copyright (c) 2024 Jaerin Lee
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from transformers import Blip2Processor, Blip2ForConditionalGeneration
from diffusers import DiffusionPipeline, LCMScheduler, EulerDiscreteScheduler, AutoencoderTiny
from huggingface_hub import hf_hub_download
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as T
from einops import rearrange
from collections import deque
from typing import Tuple, List, Literal, Optional, Union
from PIL import Image
from util import load_model, gaussian_lowpass, shift_to_mask_bbox_center
from data import BackgroundObject, LayerObject, BackgroundState #, LayerState
class StreamMultiDiffusion(nn.Module):
def __init__(
self,
device: torch.device,
dtype: torch.dtype = torch.float16,
sd_version: Literal['1.5'] = '1.5',
hf_key: Optional[str] = None,
lora_key: Optional[str] = None,
use_tiny_vae: bool = True,
t_index_list: List[int] = [0, 4, 12, 25, 37], # [0, 5, 16, 18, 20, 37], Magic number.
width: int = 512,
height: int = 512,
frame_buffer_size: int = 1,
num_inference_steps: int = 50,
guidance_scale: float = 1.2,
delta: float = 1.0,
cfg_type: Literal['none', 'full', 'self', 'initialize'] = 'none',
seed: int = 2024,
autoflush: bool = True,
default_mask_std: float = 8.0,
default_mask_strength: float = 1.0,
default_prompt_strength: float = 0.95,
bootstrap_steps: int = 1,
bootstrap_mix_steps: float = 1.0,
# bootstrap_leak_sensitivity: float = 0.2,
preprocess_mask_cover_alpha: float = 0.3, # TODO
prompt_queue_capacity: int = 256,
mask_type: Literal['discrete', 'semi-continuous', 'continuous'] = 'continuous',
use_xformers: bool = False,
) -> None:
super().__init__()
self.device = device
self.dtype = dtype
self.seed = seed
self.sd_version = sd_version
self.autoflush = autoflush
self.default_mask_std = default_mask_std
self.default_mask_strength = default_mask_strength
self.default_prompt_strength = default_prompt_strength
self.bootstrap_steps = (
bootstrap_steps > torch.arange(len(t_index_list))).to(dtype=self.dtype, device=self.device)
self.bootstrap_mix_steps = bootstrap_mix_steps
self.bootstrap_mix_ratios = (
bootstrap_mix_steps - torch.arange(len(t_index_list), device=self.device)).clip_(0, 1).to(self.dtype)
# self.bootstrap_leak_sensitivity = bootstrap_leak_sensitivity
self.preprocess_mask_cover_alpha = preprocess_mask_cover_alpha
self.mask_type = mask_type
### State definition
# [0. Start] -(prepare)-> [1. Initialized]
# [1. Initialized] -(update_background)-> [2. Background Registered] (len(self.prompts)==0)
# [2. Background Registered] -(update_layers)-> [3. Unflushed] (len(self.prompts)>0)
# [3. Unflushed] -(flush)-> [4. Ready]
# [4. Ready] -(any updates)-> [3. Unflushed]
# [4. Ready] -(__call__)-> [4. Ready], continuously returns generated image.
self.ready_checklist = {
'initialized': False,
'background_registered': False,
'layers_ready': False,
'flushed': False,
}
### Session state update queue: for lazy update policy for streaming applications.
self.update_buffer = {
'background': None, # Maintains a single instance of BackgroundObject
'layers': deque(maxlen=prompt_queue_capacity), # Maintains a queue of LayerObjects
}
print(f'[INFO] Loading Stable Diffusion...')
get_scheduler = lambda pipe: LCMScheduler.from_config(pipe.scheduler.config)
lora_weight_name = None
if self.sd_version == '1.5':
if hf_key is not None:
print(f'[INFO] Using custom model key: {hf_key}')
model_key = hf_key
else:
model_key = 'runwayml/stable-diffusion-v1-5'
lora_key = 'latent-consistency/lcm-lora-sdv1-5'
lora_weight_name = 'pytorch_lora_weights.safetensors'
# elif self.sd_version == 'xl':
# model_key = 'stabilityai/stable-diffusion-xl-base-1.0'
# lora_key = 'latent-consistency/lcm-lora-sdxl'
# lora_weight_name = 'pytorch_lora_weights.safetensors'
else:
raise ValueError(f'Stable Diffusion version {self.sd_version} not supported.')
### Internally stored "Session" states
self.state = {
'background': BackgroundState(), # Maintains a single instance of BackgroundState
# 'layers': LayerState(), # Maintains a single instance of LayerState
'model_key': model_key, # The Hugging Face model ID.
}
# Create model
self.i2t_processor = Blip2Processor.from_pretrained('Salesforce/blip2-opt-2.7b')
self.i2t_model = Blip2ForConditionalGeneration.from_pretrained('Salesforce/blip2-opt-2.7b')
self.pipe = load_model(model_key, self.sd_version, self.device, self.dtype)
self.pipe.load_lora_weights(lora_key, weight_name=lora_weight_name, adapter_name='lcm')
self.pipe.fuse_lora(
fuse_unet=True,
fuse_text_encoder=True,
lora_scale=1.0,
safe_fusing=False,
)
if use_xformers:
self.pipe.enable_xformers_memory_efficient_attention()
self.vae = (
AutoencoderTiny.from_pretrained('madebyollin/taesd').to(device=self.device, dtype=self.dtype)
if use_tiny_vae else self.pipe.vae
)
# self.tokenizer = self.pipe.tokenizer
self.text_encoder = self.pipe.text_encoder
self.unet = self.pipe.unet
self.vae_scale_factor = self.pipe.vae_scale_factor
self.scheduler = get_scheduler(self.pipe)
self.scheduler.set_timesteps(num_inference_steps)
self.generator = None
# Lock the canvas size--changing the canvas size can be implemented by reloading the module.
self.height = height
self.width = width
self.latent_height = int(height // self.pipe.vae_scale_factor)
self.latent_width = int(width // self.pipe.vae_scale_factor)
# For bootstrapping.
# self.white = self.encode_imgs(torch.ones(1, 3, height, width, dtype=self.dtype, device=self.device))
self.white = None
# StreamDiffusion setting.
self.t_list = t_index_list
assert len(self.t_list) > 1, 'Current version only supports diffusion models with multiple steps.'
self.frame_bff_size = frame_buffer_size # f
self.denoising_steps_num = len(self.t_list) # t=2
self.cfg_type = cfg_type
self.num_inference_steps = num_inference_steps
self.guidance_scale = 1.0 if self.cfg_type == 'none' else guidance_scale
self.delta = delta
self.batch_size = self.denoising_steps_num * frame_buffer_size # T = t*f
if self.cfg_type == 'initialize':
self.trt_unet_batch_size = (self.denoising_steps_num + 1) * self.frame_bff_size
elif self.cfg_type == 'full':
self.trt_unet_batch_size = 2 * self.denoising_steps_num * self.frame_bff_size
else:
self.trt_unet_batch_size = self.denoising_steps_num * frame_buffer_size
print(f'[INFO] Model is loaded!')
# self.reset_seed(self.generator, seed)
# self.reset_latent()
# self.prepare()
# print(f'[INFO] Parameters prepared!')
self.ready_checklist['initialized'] = True
@property
def background(self) -> BackgroundState:
return self.state['background']
# @property
# def layers(self) -> LayerState:
# return self.state['layers']
@property
def num_layers(self) -> int:
return len(self.prompts) if hasattr(self, 'prompts') else 0
@property
def is_ready_except_flush(self) -> bool:
return all(v for k, v in self.ready_checklist.items() if k != 'flushed')
@property
def is_flush_needed(self) -> bool:
return self.autoflush and not self.ready_checklist['flushed']
@property
def is_ready(self) -> bool:
return self.is_ready_except_flush and not self.is_flush_needed
@property
def is_dirty(self) -> bool:
return not (self.update_buffer['background'] is None and len(self.update_buffer['layers']) == 0)
@property
def has_background(self) -> bool:
return self.background.is_empty
# @property
# def has_layers(self) -> bool:
# return len(self.layers) > 0
def __repr__(self) -> str:
return (
f'{type(self).__name__}(\n\tbackground: {str(self.background)},\n\t'
f'model_key: {self.state["model_key"]}\n)'
# f'layers: {str(self.layers)},\n\tmodel_key: {self.state["model_key"]}\n)'
)
def check_integrity(self, throw_error: bool = True) -> bool:
p = len(self.prompts)
flag = (
p != len(self.negative_prompts) or
p != len(self.prompt_strengths) or
p != len(self.masks) or
p != len(self.mask_strengths) or
p != len(self.mask_stds) or
p != len(self.original_masks)
)
if flag and throw_error:
print(
f'LayerState(\n\tlen(prompts): {p},\n\tlen(negative_prompts): {len(self.negative_prompts)},\n\t'
f'len(prompt_strengths): {len(self.prompt_strengths)},\n\tlen(masks): {len(self.masks)},\n\t'
f'len(mask_stds): {len(self.mask_stds)},\n\tlen(mask_strengths): {len(self.mask_strengths)},\n\t'
f'len(original_masks): {len(self.original_masks)}\n)'
)
raise ValueError('[ERROR] LayerState is corrupted!')
return not flag
def check_ready(self) -> bool:
all_except_flushed = all(v for k, v in self.ready_checklist.items() if k != 'flushed')
if all_except_flushed:
if self.is_flush_needed:
self.flush()
return True
print('[WARNING] MagicDraw module is not ready yet! Complete the checklist:')
for k, v in self.ready_checklist.items():
prefix = ' [ v ] ' if v else ' [ x ] '
print(prefix + k.replace('_', ' '))
return False
def reset_seed(self, generator: Optional[torch.Generator] = None, seed: Optional[int] = None) -> None:
generator = torch.Generator(self.device) if generator is None else generator
seed = self.seed if seed is None else seed
self.generator = generator
self.generator.manual_seed(seed)
self.init_noise = torch.randn((self.batch_size, 4, self.latent_height, self.latent_width),
generator=generator, device=self.device, dtype=self.dtype)
self.stock_noise = torch.zeros_like(self.init_noise)
self.ready_checklist['flushed'] = False
def reset_latent(self) -> None:
# initialize x_t_latent (it can be any random tensor)
b = (self.denoising_steps_num - 1) * self.frame_bff_size
self.x_t_latent_buffer = torch.zeros(
(b, 4, self.latent_height, self.latent_width), dtype=self.dtype, device=self.device)
def reset_state(self) -> None:
# TODO Reset states for context switch between multiple users.
pass
def prepare(self) -> None:
# make sub timesteps list based on the indices in the t_list list and the values in the timesteps list
self.timesteps = self.scheduler.timesteps.to(self.device)
self.sub_timesteps = []
for t in self.t_list:
self.sub_timesteps.append(self.timesteps[t])
sub_timesteps_tensor = torch.tensor(self.sub_timesteps, dtype=torch.long, device=self.device)
self.sub_timesteps_tensor = sub_timesteps_tensor.repeat_interleave(self.frame_bff_size, dim=0)
c_skip_list = []
c_out_list = []
for timestep in self.sub_timesteps:
c_skip, c_out = self.scheduler.get_scalings_for_boundary_condition_discrete(timestep)
c_skip_list.append(c_skip)
c_out_list.append(c_out)
self.c_skip = torch.stack(c_skip_list).view(len(self.t_list), 1, 1, 1).to(dtype=self.dtype, device=self.device)
self.c_out = torch.stack(c_out_list).view(len(self.t_list), 1, 1, 1).to(dtype=self.dtype, device=self.device)
alpha_prod_t_sqrt_list = []
beta_prod_t_sqrt_list = []
for timestep in self.sub_timesteps:
alpha_prod_t_sqrt = self.scheduler.alphas_cumprod[timestep].sqrt()
beta_prod_t_sqrt = (1 - self.scheduler.alphas_cumprod[timestep]).sqrt()
alpha_prod_t_sqrt_list.append(alpha_prod_t_sqrt)
beta_prod_t_sqrt_list.append(beta_prod_t_sqrt)
alpha_prod_t_sqrt = (torch.stack(alpha_prod_t_sqrt_list).view(len(self.t_list), 1, 1, 1)
.to(dtype=self.dtype, device=self.device))
beta_prod_t_sqrt = (torch.stack(beta_prod_t_sqrt_list).view(len(self.t_list), 1, 1, 1)
.to(dtype=self.dtype, device=self.device))
self.alpha_prod_t_sqrt = alpha_prod_t_sqrt.repeat_interleave(self.frame_bff_size, dim=0)
self.beta_prod_t_sqrt = beta_prod_t_sqrt.repeat_interleave(self.frame_bff_size, dim=0)
noise_lvs = ((1 - self.scheduler.alphas_cumprod.to(self.device)[self.sub_timesteps_tensor]) ** 0.5)
self.noise_lvs = noise_lvs[None, :, None, None, None]
self.next_noise_lvs = torch.cat([noise_lvs[1:], noise_lvs.new_zeros(1)])[None, :, None, None, None]
@torch.no_grad()
def get_text_prompts(self, image: Image.Image) -> str:
r"""A convenient method to extract text prompt from an image.
This is called if the user does not provide background prompt but only
the background image. We use BLIP-2 to automatically generate prompts.
Args:
image (Image.Image): A PIL image.
Returns:
A single string of text prompt.
"""
question = 'Question: What are in the image? Answer:'
inputs = self.i2t_processor(image, question, return_tensors='pt')
out = self.i2t_model.generate(**inputs, max_new_tokens=77)
prompt = self.i2t_processor.decode(out[0], skip_special_tokens=True).strip()
return prompt
@torch.no_grad()
def encode_imgs(
self,
imgs: torch.Tensor,
generator: Optional[torch.Generator] = None,
add_noise: bool = False,
) -> torch.Tensor:
r"""A wrapper function for VAE encoder of the latent diffusion model.
Args:
imgs (torch.Tensor): An image to get StableDiffusion latents.
Expected shape: (B, 3, H, W). Expected pixel scale: [0, 1].
generator (Optional[torch.Generator]): Seed for KL-Autoencoder.
add_noise (bool): Turn this on for a noisy latent.
Returns:
An image latent embedding with 1/8 size (depending on the auto-
encoder. Shape: (B, 4, H//8, W//8).
"""
def _retrieve_latents(
encoder_output: torch.Tensor,
generator: Optional[torch.Generator] = None,
sample_mode: str = 'sample',
):
if hasattr(encoder_output, 'latent_dist') and sample_mode == 'sample':
return encoder_output.latent_dist.sample(generator)
elif hasattr(encoder_output, 'latent_dist') and sample_mode == 'argmax':
return encoder_output.latent_dist.mode()
elif hasattr(encoder_output, 'latents'):
return encoder_output.latents
else:
raise AttributeError('[ERROR] Could not access latents of provided encoder_output')
imgs = 2 * imgs - 1
latents = self.vae.config.scaling_factor * _retrieve_latents(self.vae.encode(imgs), generator=generator)
if add_noise:
latents = self.alpha_prod_t_sqrt[0] * latents + self.beta_prod_t_sqrt[0] * self.init_noise[0]
return latents
@torch.no_grad()
def decode_latents(self, latents: torch.Tensor) -> torch.Tensor:
r"""A wrapper function for VAE decoder of the latent diffusion model.
Args:
latents (torch.Tensor): An image latent to get associated images.
Expected shape: (B, 4, H//8, W//8).
Returns:
An image latent embedding with 1/8 size (depending on the auto-
encoder. Shape: (B, 3, H, W).
"""
latents = 1 / self.vae.config.scaling_factor * latents
imgs = self.vae.decode(latents).sample
imgs = (imgs / 2 + 0.5).clip_(0, 1)
return imgs
@torch.no_grad()
def update_background(
self,
image: Optional[Image.Image] = None,
prompt: Optional[str] = None,
negative_prompt: Optional[str] = None,
) -> bool:
flag_changed = False
if image is not None:
image_ = image.resize((self.width, self.height))
prompt = self.get_text_prompts(image_) if prompt is None else prompt
negative_prompt = '' if negative_prompt is None else negative_prompt
embed = self.pipe.encode_prompt(
prompt=[prompt],
device=self.device,
num_images_per_prompt=1,
do_classifier_free_guidance=(self.guidance_scale > 1.0),
negative_prompt=[negative_prompt],
) # ((1, 77, 768): cond, (1, 77, 768): uncond)
self.state['background'].image = image
self.state['background'].latent = (
self.encode_imgs(T.ToTensor()(image_)[None].to(self.device, self.dtype))
) # (1, 3, H, W)
self.state['background'].prompt = prompt
self.state['background'].negative_prompt = negative_prompt
self.state['background'].embed = embed
if self.bootstrap_steps[0] > 0:
if self.white is None:
self.white = self.encode_imgs(torch.ones(1, 3, self.height, self.width, dtype=self.dtype, device=self.device))
mix_ratio = self.bootstrap_mix_ratios[:, None, None, None]
self.bootstrap_latent = mix_ratio * self.white + (1.0 - mix_ratio) * self.state['background'].latent
self.ready_checklist['background_registered'] = True
flag_changed = True
else:
if not self.ready_checklist['background_registered']:
print('[WARNING] Register background image first! Request ignored.')
return False
if prompt is not None:
self.background.prompt = prompt
flag_changed = True
if negative_prompt is not None:
self.background.negative_prompt = negative_prompt
flag_changed = True
if flag_changed:
self.background.embed = self.pipe.encode_prompt(
prompt=[self.background.prompt],
device=self.device,
num_images_per_prompt=1,
do_classifier_free_guidance=(self.guidance_scale > 1.0),
negative_prompt=[self.background.negative_prompt],
) # ((1, 77, 768): cond, (1, 77, 768): uncond)
self.ready_checklist['flushed'] = not flag_changed
return flag_changed
@torch.no_grad()
def process_mask(
self,
masks: Optional[Union[torch.Tensor, Image.Image, List[Image.Image]]] = None,
strength: Optional[Union[torch.Tensor, float]] = None,
std: Optional[Union[torch.Tensor, float]] = None,
) -> Tuple[torch.Tensor]:
r"""Fast preprocess of masks for region-based generation with fine-
grained controls.
Mask preprocessing is done in four steps:
1. Resizing: Resize the masks into the specified width and height by
nearest neighbor interpolation.
2. (Optional) Ordering: Masks with higher indices are considered to
cover the masks with smaller indices. Covered masks are decayed
in its alpha value by the specified factor of
`preprocess_mask_cover_alpha`.
3. Blurring: Gaussian blur is applied to the mask with the specified
standard deviation (isotropic). This results in gradual increase of
masked region as the timesteps evolve, naturally blending fore-
ground and the predesignated background. Not strictly required if
you want to produce images from scratch withoout background.
4. Quantization: Split the real-numbered masks of value between [0, 1]
into predefined noise levels for each quantized scheduling step of
the diffusion sampler. For example, if the diffusion model sampler
has noise level of [0.9977, 0.9912, 0.9735, 0.8499, 0.5840], which
is the default noise level of this module with schedule [0, 4, 12,
25, 37], the masks are split into binary masks whose values are
greater than these levels. This results in tradual increase of mask
region as the timesteps increase. Details are described in our
paper at https://arxiv.org/pdf/2403.09055.pdf.
On the Three Modes of `mask_type`:
`self.mask_type` is predefined at the initialization stage of this
pipeline. Three possible modes are available: 'discrete', 'semi-
continuous', and 'continuous'. These define the mask quantization
modes we use. Basically, this (subtly) controls the smoothness of
foreground-background blending. Continuous modes produces nonbinary
masks to further blend foreground and background latents by linear-
ly interpolating between them. Semi-continuous masks only applies
continuous mask at the last step of the LCM sampler. Due to the
large step size of the LCM scheduler, we find that our continuous
blending helps generating seamless inpainting and editing results.
Args:
masks (Union[torch.Tensor, Image.Image, List[Image.Image]]): Masks.
strength (Optional[Union[torch.Tensor, float]]): Mask strength that
overrides the default value. A globally multiplied factor to
the mask at the initial stage of processing. Can be applied
seperately for each mask.
std (Optional[Union[torch.Tensor, float]]): Mask blurring Gaussian
kernel's standard deviation. Overrides the default value. Can
be applied seperately for each mask.
Returns: A tuple of tensors.
- masks: Preprocessed (ordered, blurred, and quantized) binary/non-
binary masks (see the explanation on `mask_type` above) for
region-based image synthesis.
- strengths: Return mask strengths for caching.
- std: Return mask blur standard deviations for caching.
- original_masks: Return original masks for caching.
"""
if masks is None:
kwargs = {'dtype': self.dtype, 'device': self.device}
original_masks = torch.zeros((0, 1, self.latent_height, self.latent_width), dtype=self.dtype)
masks = torch.zeros((0, self.batch_size, 1, self.latent_height, self.latent_width), **kwargs)
strength = torch.zeros((0,), **kwargs)
std = torch.zeros((0,), **kwargs)
return masks, strength, std, original_masks
if isinstance(masks, Image.Image):
masks = [masks]
if isinstance(masks, (tuple, list)):
# Assumes white background for Image.Image;
# inverted boolean masks with shape (1, 1, H, W) for torch.Tensor.
masks = torch.cat([
# (T.ToTensor()(mask.resize((self.width, self.height), Image.NEAREST)) < 0.5)[None, :1]
(1.0 - T.ToTensor()(mask.resize((self.width, self.height), Image.BILINEAR)))[None, :1]
for mask in masks
], dim=0).float().clip_(0, 1)
original_masks = masks
masks = masks.float().to(self.device)
# Background mask alpha is decayed by the specified factor where foreground masks covers it.
if self.preprocess_mask_cover_alpha > 0:
masks = torch.stack([
torch.where(
masks[i + 1:].sum(dim=0) > 0,
mask * self.preprocess_mask_cover_alpha,
mask,
) if i < len(masks) - 1 else mask
for i, mask in enumerate(masks)
], dim=0)
if std is None:
std = self.default_mask_std
if isinstance(std, (int, float)):
std = [std] * len(masks)
if isinstance(std, (list, tuple)):
std = torch.as_tensor(std, dtype=torch.float, device=self.device)
# Mask preprocessing parameters are fetched from the default settings.
if strength is None:
strength = self.default_mask_strength
if isinstance(strength, (int, float)):
strength = [strength] * len(masks)
if isinstance(strength, (list, tuple)):
strength = torch.as_tensor(strength, dtype=torch.float, device=self.device)
if (std > 0).any():
std = torch.where(std > 0, std, 1e-5)
masks = gaussian_lowpass(masks, std)
# NOTE: This `strength` aligns with `denoising strength`. However, with LCM, using strength < 0.96
# gives unpleasant results.
masks = masks * strength[:, None, None, None]
masks = masks.unsqueeze(1).repeat(1, self.noise_lvs.shape[1], 1, 1, 1)
if self.mask_type == 'discrete':
# Discrete mode.
masks = masks > self.noise_lvs
elif self.mask_type == 'semi-continuous':
# Semi-continuous mode (continuous at the last step only).
masks = torch.cat((
masks[:, :-1] > self.noise_lvs[:, :-1],
(
(masks[:, -1:] - self.next_noise_lvs[:, -1:])
/ (self.noise_lvs[:, -1:] - self.next_noise_lvs[:, -1:])
).clip_(0, 1),
), dim=1)
elif self.mask_type == 'continuous':
# Continuous mode: Have the exact same `1` coverage with discrete mode, but the mask gradually
# decreases continuously after the discrete mode boundary to become `0` at the
# next lower threshold.
masks = ((masks - self.next_noise_lvs) / (self.noise_lvs - self.next_noise_lvs)).clip_(0, 1)
# NOTE: Post processing mask strength does not align with conventional 'denoising_strength'. However,
# fine-grained mask alpha channel tuning is available with this form.
# masks = masks * strength[None, :, None, None, None]
masks = rearrange(masks.float(), 'p t () h w -> (p t) () h w')
masks = F.interpolate(masks, size=(self.latent_height, self.latent_width), mode='nearest')
masks = rearrange(masks.to(self.dtype), '(p t) () h w -> p t () h w', p=len(std))
return masks, strength, std, original_masks
@torch.no_grad()
def update_layers(
self,
prompts: Union[str, List[str]],
negative_prompts: Optional[Union[str, List[str]]] = None,
suffix: Optional[str] = None, #', background is ',
prompt_strengths: Optional[Union[torch.Tensor, float, List[float]]] = None,
masks: Optional[Union[torch.Tensor, Image.Image, List[Image.Image]]] = None,
mask_strengths: Optional[Union[torch.Tensor, float, List[float]]] = None,
mask_stds: Optional[Union[torch.Tensor, float, List[float]]] = None,
) -> None:
if not self.ready_checklist['background_registered']:
print('[WARNING] Register background image first! Request ignored.')
return
### Register prompts
if isinstance(prompts, str):
prompts = [prompts]
if negative_prompts is None:
negative_prompts = ''
if isinstance(negative_prompts, str):
negative_prompts = [negative_prompts]
fg_prompt = [p + suffix + self.background.prompt if suffix is not None else p for p in prompts]
self.prompts = fg_prompt
self.negative_prompts = negative_prompts
p = self.num_layers
e = self.pipe.encode_prompt(
prompt=fg_prompt,
device=self.device,
num_images_per_prompt=1,
do_classifier_free_guidance=(self.guidance_scale > 1.0),
negative_prompt=negative_prompts,
) # (p, 77, 768)
if prompt_strengths is None:
prompt_strengths = self.default_prompt_strength
if isinstance(prompt_strengths, (int, float)):
prompt_strengths = [prompt_strengths] * p
if isinstance(prompt_strengths, (list, tuple)):
prompt_strengths = torch.as_tensor(prompt_strengths, dtype=self.dtype, device=self.device)
self.prompt_strengths = prompt_strengths
s = prompt_strengths[:, None, None]
self.prompt_embeds = torch.lerp(self.background.embed[0], e[0], s).repeat(self.batch_size, 1, 1) # (T * p, 77, 768)
if self.guidance_scale > 1.0 and self.cfg_type in ('initialize', 'full'):
b = self.batch_size if self.cfg_type == 'full' else self.frame_bff_size
uncond_prompt_embeds = torch.lerp(self.background.embed[1], e[1], s).repeat(b, 1, 1) # (T * p, 77, 768)
self.prompt_embeds = torch.cat([uncond_prompt_embeds, self.prompt_embeds], dim=0) # (2 * T * p, 77, 768)
self.sub_timesteps_tensor_ = self.sub_timesteps_tensor.repeat_interleave(p) # (T * p,)
self.init_noise_ = self.init_noise.repeat_interleave(p, dim=0) # (T * p, 77, 768)
self.stock_noise_ = self.stock_noise.repeat_interleave(p, dim=0) # (T * p, 77, 768)
self.c_out_ = self.c_out.repeat_interleave(p, dim=0) # (T * p, 1, 1, 1)
self.c_skip_ = self.c_skip.repeat_interleave(p, dim=0) # (T * p, 1, 1, 1)
self.beta_prod_t_sqrt_ = self.beta_prod_t_sqrt.repeat_interleave(p, dim=0) # (T * p, 1, 1, 1)
self.alpha_prod_t_sqrt_ = self.alpha_prod_t_sqrt.repeat_interleave(p, dim=0) # (T * p, 1, 1, 1)
### Register new masks
if isinstance(masks, Image.Image):
masks = [masks]
n = len(masks) if masks is not None else 0
# Modificiation.
masks, mask_strengths, mask_stds, original_masks = self.process_mask(masks, mask_strengths, mask_stds)
self.counts = masks.sum(dim=0) # (T, 1, h, w)
self.bg_mask = (1 - self.counts).clip_(0, 1) # (T, 1, h, w)
self.masks = masks # (p, T, 1, h, w)
self.mask_strengths = mask_strengths # (p,)
self.mask_stds = mask_stds # (p,)
self.original_masks = original_masks # (p, 1, h, w)
if p > n:
# Add more masks: counts and bg_masks are not changed, but only masks are changed.
self.masks = torch.cat((
self.masks,
torch.zeros(
(p - n, self.batch_size, 1, self.latent_height, self.latent_width),
dtype=self.dtype,
device=self.device,
),
), dim=0)
print(f'[WARNING] Detected more prompts ({p}) than masks ({n}). '
'Automatically adds blank masks for the additional prompts.')
elif p < n:
# Warns user to add more prompts.
print(f'[WARNING] Detected more masks ({n}) than prompts ({p}). '
'Additional masks are ignored until more prompts are provided.')
self.ready_checklist['layers_ready'] = True
self.ready_checklist['flushed'] = False
@torch.no_grad()
def update_single_layer(
self,
idx: Optional[int] = None,
prompt: Optional[str] = None,
negative_prompt: Optional[str] = None,
suffix: Optional[str] = None, #', background is ',
prompt_strength: Optional[float] = None,
mask: Optional[Union[torch.Tensor, Image.Image]] = None,
mask_strength: Optional[float] = None,
mask_std: Optional[float] = None,
) -> None:
### Possible input combinations and expected behaviors
# The module will consider a layer, a pair of (prompt, mask), to be 'active' only if a prompt
# is registered. A blank mask will be assigned if no mask is provided for the 'active' layer.
# The layers should be in either of ('active', 'inactive') states. 'inactive' layers will not
# receive any input unless equipped with prompt. 'active' layers receive any input and modify
# their states accordingly. In the actual implementation, only the 'active' layers are stored
# and can be accessed by the fields. Values len(self.prompts) = self.num_layers is the number
# of 'active' layers.
# If no background is registered. The layers should be all 'inactive'.
if not self.ready_checklist['background_registered']:
print('[WARNING] Register background image first! Request ignored.')
return
# The first layer create request should be carrying a prompt. If only mask is drawn without a
# prompt, it just ignores the request--the user will update her request soon.
if self.num_layers == 0:
if prompt is not None:
self.update_layers(
prompts=prompt,
negative_prompts=negative_prompt,
suffix=suffix,
prompt_strengths=prompt_strength,
masks=mask,
mask_strengths=mask_strength,
mask_stds=mask_std,
)
return
# Invalid request indices -> considered as a layer add request.
if idx is None or idx > self.num_layers or idx < 0:
idx = self.num_layers
# Two modes for the layer edits: 'append mode' and 'edit mode'. 'append mode' appends a new
# layer at the end of the layers list. 'edit mode' modifies internal variables for the given
# index. 'append mode' is defined by the request index and strictly requires a prompt input.
is_appending = idx == self.num_layers
if is_appending and prompt is None:
print(f'[WARNING] Creating a new prompt at index ({idx}) but found no prompt. Request ignored.')
return
### Register prompts
# | prompt | neg_prompt | append mode (idx==len) | edit mode (idx<len) |
# | --------- | ---------- | ----------------------- | -------------------- |
# | given | given | append new prompt embed | replace prompt embed |
# | given | not given | append new prompt embed | replace prompt embed |
# | not given | given | NOT ALLOWED | replace prompt embed |
# | not given | not given | NOT ALLOWED | do nothing |
# | prompt_strength | append mode (idx==len) | edit mode (idx<len) |
# | --------------- | ---------------------- | ---------------------------------------------- |
# | given | use given strength | use given strength |
# | not given | use default strength | replace strength / if no existing, use default |
p = self.num_layers
flag_prompt_edited = (
prompt is not None or
negative_prompt is not None or
prompt_strength is not None
)
if flag_prompt_edited:
is_double_cond = self.guidance_scale > 1.0 and self.cfg_type in ('initialize', 'full')
# Synchonize the internal state.
# We have asserted that prompt is not None if the mode is 'appending'.
if prompt is not None:
if suffix is not None:
prompt = prompt + suffix + self.background.prompt
if is_appending:
self.prompts.append(prompt)
else:
self.prompts[idx] = prompt
if negative_prompt is not None:
if is_appending:
self.negative_prompts.append(negative_prompt)
else:
self.negative_prompts[idx] = negative_prompt
elif is_appending:
# Make sure that negative prompts are well specified.
self.negative_prompts.append('')
if is_appending:
if prompt_strength is None:
prompt_strength = self.default_prompt_strength
self.prompt_strengths = torch.cat((
self.prompt_strengths,
torch.as_tensor([prompt_strength], dtype=self.dtype, device=self.device),
), dim=0)
elif prompt_strength is not None:
self.prompt_strengths[idx] = prompt_strength
# Edit currently stored prompt embeddings.
if is_double_cond:
uncond_prompt_embed_, prompt_embed_ = torch.chunk(self.prompt_embeds, 2, dim=0)
uncond_prompt_embed_ = rearrange(uncond_prompt_embed_, '(t p) c1 c2 -> t p c1 c2', p=p)
prompt_embed_ = rearrange(prompt_embed_, '(t p) c1 c2 -> t p c1 c2', p=p)
else:
uncond_prompt_embed_ = None
prompt_embed_ = rearrange(self.prompt_embeds, '(t p) c1 c2 -> t p c1 c2', p=p)
e = self.pipe.encode_prompt(
prompt=self.prompts[idx],
device=self.device,
num_images_per_prompt=1,
do_classifier_free_guidance=(self.guidance_scale > 1.0),
negative_prompt=self.negative_prompts[idx],
) # (1, 77, 768), (1, 77, 768)
s = self.prompt_strengths[idx]
t = prompt_embed_.shape[0]
prompt_embed = torch.lerp(self.background.embed[0], e[0], s)[None].repeat(t, 1, 1, 1) # (1, 77, 768)
if is_double_cond:
uncond_prompt_embed = torch.lerp(self.background.embed[1], e[1], s)[None].repeat(t, 1, 1, 1) # (1, 77, 768)
if is_appending:
prompt_embed_ = torch.cat((prompt_embed_, prompt_embed), dim=1)
if is_double_cond:
uncond_prompt_embed_ = torch.cat((uncond_prompt_embed_, uncond_prompt_embed), dim=1)
else:
prompt_embed_[:, idx:(idx + 1)] = prompt_embed
if is_double_cond:
uncond_prompt_embed_[:, idx:(idx + 1)] = uncond_prompt_embed
self.prompt_embeds = rearrange(prompt_embed_, 't p c1 c2 -> (t p) c1 c2')
if is_double_cond:
uncond_prompt_embeds = rearrange(uncond_prompt_embed_, 't p c1 c2 -> (t p) c1 c2')
self.prompt_embeds = torch.cat([uncond_prompt_embeds, self.prompt_embeds], dim=0) # (2 * T * p, 77, 768)
self.ready_checklist['flushed'] = False
if is_appending:
p = self.num_layers
self.sub_timesteps_tensor_ = self.sub_timesteps_tensor.repeat_interleave(p) # (T * p,)
self.init_noise_ = self.init_noise.repeat_interleave(p, dim=0) # (T * p, 77, 768)
self.stock_noise_ = self.stock_noise.repeat_interleave(p, dim=0) # (T * p, 77, 768)
self.c_out_ = self.c_out.repeat_interleave(p, dim=0) # (T * p, 1, 1, 1)
self.c_skip_ = self.c_skip.repeat_interleave(p, dim=0) # (T * p, 1, 1, 1)
self.beta_prod_t_sqrt_ = self.beta_prod_t_sqrt.repeat_interleave(p, dim=0) # (T * p, 1, 1, 1)
self.alpha_prod_t_sqrt_ = self.alpha_prod_t_sqrt.repeat_interleave(p, dim=0) # (T * p, 1, 1, 1)
### Register new masks
# | mask | std / str | append mode (idx==len) | edit mode (idx<len) |
# | --------- | --------- | ---------------------------- | ----------------------------- |
# | given | given | create mask with given val | create mask with given val |
# | given | not given | create mask with default val | create mask with existing val |
# | not given | given | create blank mask | replace mask with given val |
# | not given | not given | create blank mask | do nothing |
flag_nonzero_mask = False
if mask is not None:
# Mask image is given -> create mask.
mask, strength, std, original_mask = self.process_mask(mask, mask_strength, mask_std)
flag_nonzero_mask = True
elif is_appending:
# No given mask & append mode -> create white mask.
mask = torch.zeros(
(1, self.batch_size, 1, self.latent_height, self.latent_width),
dtype=self.dtype,
device=self.device,
)
strength = torch.as_tensor([self.default_mask_strength], dtype=self.dtype, device=self.device)
std = torch.as_tensor([self.default_mask_std], dtype=self.dtype, device=self.device)
original_mask = torch.zeros((1, 1, self.latent_height, self.latent_width), dtype=self.dtype)
elif mask_std is not None or mask_strength is not None:
# No given mask & edit mode & given std / str -> replace existing mask with given std / str.
if mask_std is None:
mask_std = self.mask_stds[idx:(idx + 1)]
if mask_strength is None:
mask_strength = self.mask_strengths[idx:(idx + 1)]
mask, strength, std, original_mask = self.process_mask(
self.original_masks[idx:(idx + 1)], mask_strength, mask_std)
flag_nonzero_mask = True
else:
# No given mask & no given std & edit mode -> Do nothing.
return
if is_appending:
# Append mode.
self.masks = torch.cat((self.masks, mask), dim=0) # (p, T, 1, h, w)
self.mask_strengths = torch.cat((self.mask_strengths, strength), dim=0) # (p,)
self.mask_stds = torch.cat((self.mask_stds, std), dim=0) # (p,)
self.original_masks = torch.cat((self.original_masks, original_mask), dim=0) # (p, 1, h, w)
if flag_nonzero_mask:
self.counts = self.counts + mask[0] if hasattr(self, 'counts') else mask[0] # (T, 1, h, w)
self.bg_mask = (1 - self.counts).clip_(0, 1) # (T, 1, h, w)
else:
# Edit mode.
if flag_nonzero_mask:
self.counts = self.counts - self.masks[idx] + mask[0] # (T, 1, h, w)
self.bg_mask = (1 - self.counts).clip_(0, 1) # (T, 1, h, w)
self.masks[idx:(idx + 1)] = mask # (p, T, 1, h, w)
self.mask_strengths[idx:(idx + 1)] = strength # (p,)
self.mask_stds[idx:(idx + 1)] = std # (p,)
self.original_masks[idx:(idx + 1)] = original_mask # (p, 1, h, w)
# if flag_nonzero_mask:
# self.ready_checklist['flushed'] = False
@torch.no_grad()
def register_all(
self,
prompts: Union[str, List[str]],
masks: Union[Image.Image, List[Image.Image]],
background: Image.Image,
background_prompt: Optional[str] = None,
background_negative_prompt: str = '',
negative_prompts: Union[str, List[str]] = '',
suffix: Optional[str] = None, #', background is ',
prompt_strengths: float = 1.0,
mask_strengths: float = 1.0,
mask_stds: Union[torch.Tensor, float] = 10.0,
) -> None:
# The order of this registration should not be changed!
self.update_background(background, background_prompt, background_negative_prompt)
self.update_layers(prompts, negative_prompts, suffix, prompt_strengths, masks, mask_strengths, mask_stds)
def update(
self,
background: Optional[Image.Image] = None,
background_prompt: Optional[str] = None,
background_negative_prompt: Optional[str] = None,
idx: Optional[int] = None,
prompt: Optional[str] = None,
negative_prompt: Optional[str] = None,
suffix: Optional[str] = None,
prompt_strength: Optional[float] = None,
mask: Optional[Union[torch.Tensor, Image.Image]] = None,
mask_strength: Optional[float] = None,
mask_std: Optional[float] = None,
) -> None:
# For lazy update (to solve minor synchonization problem with gradio).
bq = BackgroundObject(
image=background,
prompt=background_prompt,
negative_prompt=background_negative_prompt,
)
if not bq.is_empty:
self.update_buffer['background'] = bq
lq = LayerObject(
idx=idx,
prompt=prompt,
negative_prompt=negative_prompt,
suffix=suffix,
prompt_strength=prompt_strength,
mask=mask,
mask_strength=mask_strength,
mask_std=mask_std,
)
if not lq.is_empty:
limit = self.update_buffer['layers'].maxlen
# Optimize the prompt queue: Overrride uncommitted layers with the same idx.
new_q = deque(maxlen=limit)
for _ in range(len(self.update_buffer['layers'])):
# Check from the newest to the oldest.
# Copy old requests only if the current query does not carry those requests.
query = self.update_buffer['layers'].pop()
overriden = lq.merge(query)
if not overriden:
new_q.appendleft(query)
self.update_buffer['layers'] = new_q
if len(self.update_buffer['layers']) == limit:
print(f'[WARNING] Maximum prompt change query limit ({limit}) is reached. '
f'Current query {lq} will be ignored.')
else:
self.update_buffer['layers'].append(lq)
@torch.no_grad()
def commit(self) -> None:
flag_changed = self.is_dirty
bq = self.update_buffer['background']
lq = self.update_buffer['layers']
count_bq_req = int(bq is not None and not bq.is_empty)
count_lq_req = len(lq)
if flag_changed:
print(f'[INFO] Requests found: {count_bq_req} background requests '
f'& {count_lq_req} layer requests:\n{str(bq)}, {", ".join([str(l) for l in lq])}')
bq = self.update_buffer['background']
if bq is not None:
self.update_background(**vars(bq))
self.update_buffer['background'] = None
while len(lq) > 0:
l = lq.popleft()
self.update_single_layer(**vars(l))
if flag_changed:
print(f'[INFO] Requests resolved: {count_bq_req} background requests '
f'& {count_lq_req} layer requests.')
def scheduler_step_batch(
self,
model_pred_batch: torch.Tensor,
x_t_latent_batch: torch.Tensor,
idx: Optional[int] = None,
) -> torch.Tensor:
r"""Denoise-only step for reverse diffusion scheduler.
Args:
model_pred_batch (torch.Tensor): Noise prediction results.
x_t_latent_batch (torch.Tensor): Noisy latent.
idx (Optional[int]): Instead of timesteps (in [0, 1000]-scale) use
indices for the timesteps tensor (ranged in
[0, len(timesteps)-1]). Specify only if a single-index, not
stream-batched inference is what you want.
Returns:
A denoised tensor with the same size as latent.
"""
if idx is None:
F_theta = (x_t_latent_batch - self.beta_prod_t_sqrt_ * model_pred_batch) / self.alpha_prod_t_sqrt_
denoised_batch = self.c_out_ * F_theta + self.c_skip_ * x_t_latent_batch
else:
F_theta = (x_t_latent_batch - self.beta_prod_t_sqrt[idx] * model_pred_batch) / self.alpha_prod_t_sqrt[idx]
denoised_batch = self.c_out[idx] * F_theta + self.c_skip[idx] * x_t_latent_batch
return denoised_batch
def unet_step(
self,
x_t_latent: torch.Tensor, # (T, 4, h, w)
idx: Optional[int] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
p = self.num_layers
x_t_latent = x_t_latent.repeat_interleave(p, dim=0) # (T * p, 4, h, w)
if self.bootstrap_steps[0] > 0:
# Background bootstrapping.
bootstrap_latent = self.scheduler.add_noise(
self.bootstrap_latent,
self.stock_noise,
torch.tensor(self.sub_timesteps_tensor, device=self.device),
)
x_t_latent = rearrange(x_t_latent, '(t p) c h w -> p t c h w', p=p)
bootstrap_mask = (
self.masks * self.bootstrap_steps[None, :, None, None, None]
+ (1.0 - self.bootstrap_steps[None, :, None, None, None])
) # (p, t, c, h, w)
x_t_latent = (1.0 - bootstrap_mask) * bootstrap_latent[None] + bootstrap_mask * x_t_latent
x_t_latent = rearrange(x_t_latent, 'p t c h w -> (t p) c h w')
# Centering.
x_t_latent = shift_to_mask_bbox_center(x_t_latent, rearrange(self.masks, 'p t c h w -> (t p) c h w'), reverse=True)
t_list = self.sub_timesteps_tensor_ # (T * p,)
if self.guidance_scale > 1.0 and self.cfg_type == 'initialize':
x_t_latent_plus_uc = torch.concat([x_t_latent[:p], x_t_latent], dim=0) # (T * p + 1, 4, h, w)
t_list = torch.concat([t_list[:p], t_list], dim=0) # (T * p + 1, 4, h, w)
elif self.guidance_scale > 1.0 and self.cfg_type == 'full':
x_t_latent_plus_uc = torch.concat([x_t_latent, x_t_latent], dim=0) # (2 * T * p, 4, h, w)
t_list = torch.concat([t_list, t_list], dim=0) # (2 * T * p,)
else:
x_t_latent_plus_uc = x_t_latent # (T * p, 4, h, w)
model_pred = self.unet(
x_t_latent_plus_uc, # (B, 4, h, w)
t_list, # (B,)
encoder_hidden_states=self.prompt_embeds, # (B, 77, 768)
return_dict=False,
# TODO: Add SDXL Support.
# added_cond_kwargs={'text_embeds': add_text_embeds, 'time_ids': add_time_ids},
)[0] # (B, 4, h, w)
if self.bootstrap_steps[0] > 0:
# Uncentering.
bootstrap_mask = rearrange(self.masks, 'p t c h w -> (t p) c h w')
if self.guidance_scale > 1.0 and self.cfg_type == 'initialize':
bootstrap_mask_ = torch.concat([bootstrap_mask[:p], bootstrap_mask], dim=0)
elif self.guidance_scale > 1.0 and self.cfg_type == 'full':
bootstrap_mask_ = torch.concat([bootstrap_mask, bootstrap_mask], dim=0)
else:
bootstrap_mask_ = bootstrap_mask
model_pred = shift_to_mask_bbox_center(model_pred, bootstrap_mask_)
x_t_latent = shift_to_mask_bbox_center(x_t_latent, bootstrap_mask)
# # Remove leakage (optional).
# leak = (latent_ - bg_latent_).pow(2).mean(dim=1, keepdim=True)
# leak_sigmoid = torch.sigmoid(leak / self.bootstrap_leak_sensitivity) * 2 - 1
# fg_mask_ = fg_mask_ * leak_sigmoid
### noise_pred_text, noise_pred_uncond: (T * p, 4, h, w)
### self.stock_noise, init_noise: (T, 4, h, w)
if self.guidance_scale > 1.0 and self.cfg_type == 'initialize':
noise_pred_text = model_pred[p:]
self.stock_noise_ = torch.concat([model_pred[:p], self.stock_noise_[p:]], dim=0)
elif self.guidance_scale > 1.0 and self.cfg_type == 'full':
noise_pred_uncond, noise_pred_text = model_pred.chunk(2)
else:
noise_pred_text = model_pred
if self.guidance_scale > 1.0 and self.cfg_type in ('self', 'initialize'):
noise_pred_uncond = self.stock_noise_ * self.delta
if self.guidance_scale > 1.0 and self.cfg_type != 'none':
model_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
else:
model_pred = noise_pred_text
# compute the previous noisy sample x_t -> x_t-1
denoised_batch = self.scheduler_step_batch(model_pred, x_t_latent, idx)
if self.cfg_type in ('self' , 'initialize'):
scaled_noise = self.beta_prod_t_sqrt_ * self.stock_noise_
delta_x = self.scheduler_step_batch(model_pred, scaled_noise, idx)
# Do mask edit.
alpha_next = torch.concat([self.alpha_prod_t_sqrt_[p:], torch.ones_like(self.alpha_prod_t_sqrt_[:p])], dim=0)
delta_x = alpha_next * delta_x
beta_next = torch.concat([self.beta_prod_t_sqrt_[p:], torch.ones_like(self.beta_prod_t_sqrt_[:p])], dim=0)
delta_x = delta_x / beta_next
init_noise = torch.concat([self.init_noise_[p:], self.init_noise_[:p]], dim=0)
self.stock_noise_ = init_noise + delta_x
p2 = len(self.t_list) - 1
background = torch.concat([
self.scheduler.add_noise(
self.background.latent.repeat(p2, 1, 1, 1),
self.stock_noise[1:],
torch.tensor(self.t_list[1:], device=self.device),
),
self.background.latent,
], dim=0)
denoised_batch = rearrange(denoised_batch, '(t p) c h w -> p t c h w', p=p)
latent = (self.masks * denoised_batch).sum(dim=0) # (T, 4, h, w)
latent = torch.where(self.counts > 0, latent / self.counts, latent)
# latent = (
# (1 - self.bg_mask) * self.mask_strengths * latent
# + ((1 - self.bg_mask) * (1.0 - self.mask_strengths) + self.bg_mask) * background
# )
latent = (1 - self.bg_mask) * latent + self.bg_mask * background
return latent
@torch.no_grad()
def __call__(
self,
no_decode: bool = False,
ignore_check_ready: bool = False,
) -> Optional[Union[torch.Tensor, Image.Image]]:
if not ignore_check_ready and not self.check_ready():
return
if not ignore_check_ready and self.is_dirty:
print("I'm so dirty now!")
self.commit()
self.flush()
latent = torch.randn((1, self.unet.config.in_channels, self.latent_height, self.latent_width),
dtype=self.dtype, device=self.device) # (1, 4, h, w)
latent = torch.cat((latent, self.x_t_latent_buffer), dim=0) # (t, 4, h, w)
self.stock_noise = torch.cat((self.init_noise[:1], self.stock_noise[:-1]), dim=0) # (t, 4, h, w)
if self.cfg_type in ('self', 'initialize'):
self.stock_noise_ = self.stock_noise.repeat_interleave(self.num_layers, dim=0) # (T * p, 77, 768)
x_0_pred_batch = self.unet_step(latent)
latent = x_0_pred_batch[-1:]
self.x_t_latent_buffer = (
self.alpha_prod_t_sqrt[1:] * x_0_pred_batch[:-1]
+ self.beta_prod_t_sqrt[1:] * self.init_noise[1:]
)
# For pipeline flushing.
if no_decode:
return latent
imgs = self.decode_latents(latent.half()) # (1, 3, H, W)
img = T.ToPILImage()(imgs[0].cpu())
return img
def flush(self) -> None:
for _ in self.t_list:
self(True, True)
self.ready_checklist['flushed'] = True