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import re
from copy import deepcopy
from dataclasses import asdict, dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import torch
from numpy import exp, pi, sqrt
from torchvision.transforms.functional import resize
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
def preprocess_image(image):
from PIL import Image
"""Preprocess an input image
Same as
https://github.com/huggingface/diffusers/blob/1138d63b519e37f0ce04e027b9f4a3261d27c628/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L44
"""
w, h = image.size
w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
@dataclass
class CanvasRegion:
"""Class defining a rectangular region in the canvas"""
row_init: int # Region starting row in pixel space (included)
row_end: int # Region end row in pixel space (not included)
col_init: int # Region starting column in pixel space (included)
col_end: int # Region end column in pixel space (not included)
region_seed: int = None # Seed for random operations in this region
noise_eps: float = 0.0 # Deviation of a zero-mean gaussian noise to be applied over the latents in this region. Useful for slightly "rerolling" latents
def __post_init__(self):
# Initialize arguments if not specified
if self.region_seed is None:
self.region_seed = np.random.randint(9999999999)
# Check coordinates are non-negative
for coord in [self.row_init, self.row_end, self.col_init, self.col_end]:
if coord < 0:
raise ValueError(
f"A CanvasRegion must be defined with non-negative indices, found ({self.row_init}, {self.row_end}, {self.col_init}, {self.col_end})"
)
# Check coordinates are divisible by 8, else we end up with nasty rounding error when mapping to latent space
for coord in [self.row_init, self.row_end, self.col_init, self.col_end]:
if coord // 8 != coord / 8:
raise ValueError(
f"A CanvasRegion must be defined with locations divisible by 8, found ({self.row_init}-{self.row_end}, {self.col_init}-{self.col_end})"
)
# Check noise eps is non-negative
if self.noise_eps < 0:
raise ValueError(f"A CanvasRegion must be defined noises eps non-negative, found {self.noise_eps}")
# Compute coordinates for this region in latent space
self.latent_row_init = self.row_init // 8
self.latent_row_end = self.row_end // 8
self.latent_col_init = self.col_init // 8
self.latent_col_end = self.col_end // 8
@property
def width(self):
return self.col_end - self.col_init
@property
def height(self):
return self.row_end - self.row_init
def get_region_generator(self, device="cpu"):
"""Creates a torch.Generator based on the random seed of this region"""
# Initialize region generator
return torch.Generator(device).manual_seed(self.region_seed)
@property
def __dict__(self):
return asdict(self)
class MaskModes(Enum):
"""Modes in which the influence of diffuser is masked"""
CONSTANT = "constant"
GAUSSIAN = "gaussian"
QUARTIC = "quartic" # See https://en.wikipedia.org/wiki/Kernel_(statistics)
@dataclass
class DiffusionRegion(CanvasRegion):
"""Abstract class defining a region where some class of diffusion process is acting"""
pass
@dataclass
class Text2ImageRegion(DiffusionRegion):
"""Class defining a region where a text guided diffusion process is acting"""
prompt: str = "" # Text prompt guiding the diffuser in this region
guidance_scale: float = 7.5 # Guidance scale of the diffuser in this region. If None, randomize
mask_type: MaskModes = MaskModes.GAUSSIAN.value # Kind of weight mask applied to this region
mask_weight: float = 1.0 # Global weights multiplier of the mask
tokenized_prompt = None # Tokenized prompt
encoded_prompt = None # Encoded prompt
def __post_init__(self):
super().__post_init__()
# Mask weight cannot be negative
if self.mask_weight < 0:
raise ValueError(
f"A Text2ImageRegion must be defined with non-negative mask weight, found {self.mask_weight}"
)
# Mask type must be an actual known mask
if self.mask_type not in [e.value for e in MaskModes]:
raise ValueError(
f"A Text2ImageRegion was defined with mask {self.mask_type}, which is not an accepted mask ({[e.value for e in MaskModes]})"
)
# Randomize arguments if given as None
if self.guidance_scale is None:
self.guidance_scale = np.random.randint(5, 30)
# Clean prompt
self.prompt = re.sub(" +", " ", self.prompt).replace("\n", " ")
def tokenize_prompt(self, tokenizer):
"""Tokenizes the prompt for this diffusion region using a given tokenizer"""
self.tokenized_prompt = tokenizer(
self.prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
def encode_prompt(self, text_encoder, device):
"""Encodes the previously tokenized prompt for this diffusion region using a given encoder"""
assert self.tokenized_prompt is not None, ValueError(
"Prompt in diffusion region must be tokenized before encoding"
)
self.encoded_prompt = text_encoder(self.tokenized_prompt.input_ids.to(device))[0]
@dataclass
class Image2ImageRegion(DiffusionRegion):
"""Class defining a region where an image guided diffusion process is acting"""
reference_image: torch.Tensor = None
strength: float = 0.8 # Strength of the image
def __post_init__(self):
super().__post_init__()
if self.reference_image is None:
raise ValueError("Must provide a reference image when creating an Image2ImageRegion")
if self.strength < 0 or self.strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {self.strength}")
# Rescale image to region shape
self.reference_image = resize(self.reference_image, size=[self.height, self.width])
def encode_reference_image(self, encoder, device, generator, cpu_vae=False):
"""Encodes the reference image for this Image2Image region into the latent space"""
# Place encoder in CPU or not following the parameter cpu_vae
if cpu_vae:
# Note here we use mean instead of sample, to avoid moving also generator to CPU, which is troublesome
self.reference_latents = encoder.cpu().encode(self.reference_image).latent_dist.mean.to(device)
else:
self.reference_latents = encoder.encode(self.reference_image.to(device)).latent_dist.sample(
generator=generator
)
self.reference_latents = 0.18215 * self.reference_latents
@property
def __dict__(self):
# This class requires special casting to dict because of the reference_image tensor. Otherwise it cannot be casted to JSON
# Get all basic fields from parent class
super_fields = {key: getattr(self, key) for key in DiffusionRegion.__dataclass_fields__.keys()}
# Pack other fields
return {**super_fields, "reference_image": self.reference_image.cpu().tolist(), "strength": self.strength}
class RerollModes(Enum):
"""Modes in which the reroll regions operate"""
RESET = "reset" # Completely reset the random noise in the region
EPSILON = "epsilon" # Alter slightly the latents in the region
@dataclass
class RerollRegion(CanvasRegion):
"""Class defining a rectangular canvas region in which initial latent noise will be rerolled"""
reroll_mode: RerollModes = RerollModes.RESET.value
@dataclass
class MaskWeightsBuilder:
"""Auxiliary class to compute a tensor of weights for a given diffusion region"""
latent_space_dim: int # Size of the U-net latent space
nbatch: int = 1 # Batch size in the U-net
def compute_mask_weights(self, region: DiffusionRegion) -> torch.tensor:
"""Computes a tensor of weights for a given diffusion region"""
MASK_BUILDERS = {
MaskModes.CONSTANT.value: self._constant_weights,
MaskModes.GAUSSIAN.value: self._gaussian_weights,
MaskModes.QUARTIC.value: self._quartic_weights,
}
return MASK_BUILDERS[region.mask_type](region)
def _constant_weights(self, region: DiffusionRegion) -> torch.tensor:
"""Computes a tensor of constant for a given diffusion region"""
latent_width = region.latent_col_end - region.latent_col_init
latent_height = region.latent_row_end - region.latent_row_init
return torch.ones(self.nbatch, self.latent_space_dim, latent_height, latent_width) * region.mask_weight
def _gaussian_weights(self, region: DiffusionRegion) -> torch.tensor:
"""Generates a gaussian mask of weights for tile contributions"""
latent_width = region.latent_col_end - region.latent_col_init
latent_height = region.latent_row_end - region.latent_row_init
var = 0.01
midpoint = (latent_width - 1) / 2 # -1 because index goes from 0 to latent_width - 1
x_probs = [
exp(-(x - midpoint) * (x - midpoint) / (latent_width * latent_width) / (2 * var)) / sqrt(2 * pi * var)
for x in range(latent_width)
]
midpoint = (latent_height - 1) / 2
y_probs = [
exp(-(y - midpoint) * (y - midpoint) / (latent_height * latent_height) / (2 * var)) / sqrt(2 * pi * var)
for y in range(latent_height)
]
weights = np.outer(y_probs, x_probs) * region.mask_weight
return torch.tile(torch.tensor(weights), (self.nbatch, self.latent_space_dim, 1, 1))
def _quartic_weights(self, region: DiffusionRegion) -> torch.tensor:
"""Generates a quartic mask of weights for tile contributions
The quartic kernel has bounded support over the diffusion region, and a smooth decay to the region limits.
"""
quartic_constant = 15.0 / 16.0
support = (np.array(range(region.latent_col_init, region.latent_col_end)) - region.latent_col_init) / (
region.latent_col_end - region.latent_col_init - 1
) * 1.99 - (1.99 / 2.0)
x_probs = quartic_constant * np.square(1 - np.square(support))
support = (np.array(range(region.latent_row_init, region.latent_row_end)) - region.latent_row_init) / (
region.latent_row_end - region.latent_row_init - 1
) * 1.99 - (1.99 / 2.0)
y_probs = quartic_constant * np.square(1 - np.square(support))
weights = np.outer(y_probs, x_probs) * region.mask_weight
return torch.tile(torch.tensor(weights), (self.nbatch, self.latent_space_dim, 1, 1))
class StableDiffusionCanvasPipeline(DiffusionPipeline, StableDiffusionMixin):
"""Stable Diffusion pipeline that mixes several diffusers in the same canvas"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
def decode_latents(self, latents, cpu_vae=False):
"""Decodes a given array of latents into pixel space"""
# scale and decode the image latents with vae
if cpu_vae:
lat = deepcopy(latents).cpu()
vae = deepcopy(self.vae).cpu()
else:
lat = latents
vae = self.vae
lat = 1 / 0.18215 * lat
image = vae.decode(lat).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
return self.numpy_to_pil(image)
def get_latest_timestep_img2img(self, num_inference_steps, strength):
"""Finds the latest timesteps where an img2img strength does not impose latents anymore"""
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * (1 - strength)) + offset
init_timestep = min(init_timestep, num_inference_steps)
t_start = min(max(num_inference_steps - init_timestep + offset, 0), num_inference_steps - 1)
latest_timestep = self.scheduler.timesteps[t_start]
return latest_timestep
@torch.no_grad()
def __call__(
self,
canvas_height: int,
canvas_width: int,
regions: List[DiffusionRegion],
num_inference_steps: Optional[int] = 50,
seed: Optional[int] = 12345,
reroll_regions: Optional[List[RerollRegion]] = None,
cpu_vae: Optional[bool] = False,
decode_steps: Optional[bool] = False,
):
if reroll_regions is None:
reroll_regions = []
batch_size = 1
if decode_steps:
steps_images = []
# Prepare scheduler
self.scheduler.set_timesteps(num_inference_steps, device=self.device)
# Split diffusion regions by their kind
text2image_regions = [region for region in regions if isinstance(region, Text2ImageRegion)]
image2image_regions = [region for region in regions if isinstance(region, Image2ImageRegion)]
# Prepare text embeddings
for region in text2image_regions:
region.tokenize_prompt(self.tokenizer)
region.encode_prompt(self.text_encoder, self.device)
# Create original noisy latents using the timesteps
latents_shape = (batch_size, self.unet.config.in_channels, canvas_height // 8, canvas_width // 8)
generator = torch.Generator(self.device).manual_seed(seed)
init_noise = torch.randn(latents_shape, generator=generator, device=self.device)
# Reset latents in seed reroll regions, if requested
for region in reroll_regions:
if region.reroll_mode == RerollModes.RESET.value:
region_shape = (
latents_shape[0],
latents_shape[1],
region.latent_row_end - region.latent_row_init,
region.latent_col_end - region.latent_col_init,
)
init_noise[
:,
:,
region.latent_row_init : region.latent_row_end,
region.latent_col_init : region.latent_col_end,
] = torch.randn(region_shape, generator=region.get_region_generator(self.device), device=self.device)
# Apply epsilon noise to regions: first diffusion regions, then reroll regions
all_eps_rerolls = regions + [r for r in reroll_regions if r.reroll_mode == RerollModes.EPSILON.value]
for region in all_eps_rerolls:
if region.noise_eps > 0:
region_noise = init_noise[
:,
:,
region.latent_row_init : region.latent_row_end,
region.latent_col_init : region.latent_col_end,
]
eps_noise = (
torch.randn(
region_noise.shape, generator=region.get_region_generator(self.device), device=self.device
)
* region.noise_eps
)
init_noise[
:,
:,
region.latent_row_init : region.latent_row_end,
region.latent_col_init : region.latent_col_end,
] += eps_noise
# scale the initial noise by the standard deviation required by the scheduler
latents = init_noise * self.scheduler.init_noise_sigma
# Get unconditional embeddings for classifier free guidance in text2image regions
for region in text2image_regions:
max_length = region.tokenized_prompt.input_ids.shape[-1]
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
region.encoded_prompt = torch.cat([uncond_embeddings, region.encoded_prompt])
# Prepare image latents
for region in image2image_regions:
region.encode_reference_image(self.vae, device=self.device, generator=generator)
# Prepare mask of weights for each region
mask_builder = MaskWeightsBuilder(latent_space_dim=self.unet.config.in_channels, nbatch=batch_size)
mask_weights = [mask_builder.compute_mask_weights(region).to(self.device) for region in text2image_regions]
# Diffusion timesteps
for i, t in tqdm(enumerate(self.scheduler.timesteps)):
# Diffuse each region
noise_preds_regions = []
# text2image regions
for region in text2image_regions:
region_latents = latents[
:,
:,
region.latent_row_init : region.latent_row_end,
region.latent_col_init : region.latent_col_end,
]
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([region_latents] * 2)
# scale model input following scheduler rules
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=region.encoded_prompt)["sample"]
# perform guidance
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred_region = noise_pred_uncond + region.guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_preds_regions.append(noise_pred_region)
# Merge noise predictions for all tiles
noise_pred = torch.zeros(latents.shape, device=self.device)
contributors = torch.zeros(latents.shape, device=self.device)
# Add each tile contribution to overall latents
for region, noise_pred_region, mask_weights_region in zip(
text2image_regions, noise_preds_regions, mask_weights
):
noise_pred[
:,
:,
region.latent_row_init : region.latent_row_end,
region.latent_col_init : region.latent_col_end,
] += noise_pred_region * mask_weights_region
contributors[
:,
:,
region.latent_row_init : region.latent_row_end,
region.latent_col_init : region.latent_col_end,
] += mask_weights_region
# Average overlapping areas with more than 1 contributor
noise_pred /= contributors
noise_pred = torch.nan_to_num(
noise_pred
) # Replace NaNs by zeros: NaN can appear if a position is not covered by any DiffusionRegion
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents).prev_sample
# Image2Image regions: override latents generated by the scheduler
for region in image2image_regions:
influence_step = self.get_latest_timestep_img2img(num_inference_steps, region.strength)
# Only override in the timesteps before the last influence step of the image (given by its strength)
if t > influence_step:
timestep = t.repeat(batch_size)
region_init_noise = init_noise[
:,
:,
region.latent_row_init : region.latent_row_end,
region.latent_col_init : region.latent_col_end,
]
region_latents = self.scheduler.add_noise(region.reference_latents, region_init_noise, timestep)
latents[
:,
:,
region.latent_row_init : region.latent_row_end,
region.latent_col_init : region.latent_col_end,
] = region_latents
if decode_steps:
steps_images.append(self.decode_latents(latents, cpu_vae))
# scale and decode the image latents with vae
image = self.decode_latents(latents, cpu_vae)
output = {"images": image}
if decode_steps:
output = {**output, "steps_images": steps_images}
return output
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