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import inspect | |
import warnings | |
from itertools import repeat | |
from typing import Callable, List, Optional, Union | |
import torch | |
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer | |
from diffusers.image_processor import VaeImageProcessor | |
from diffusers.models import AutoencoderKL, UNet2DConditionModel | |
from diffusers.models.attention_processor import AttnProcessor, Attention | |
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker | |
from diffusers.schedulers import DDIMScheduler | |
from scheduling_dpmsolver_multistep_inject import DPMSolverMultistepSchedulerInject | |
# from diffusers.utils import logging, randn_tensor | |
from diffusers.utils import logging | |
from diffusers.utils.torch_utils import randn_tensor | |
from diffusers.pipelines.pipeline_utils import DiffusionPipeline | |
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipelineOutput | |
import numpy as np | |
from PIL import Image | |
from tqdm import tqdm | |
import torch.nn.functional as F | |
import math | |
from collections.abc import Iterable | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
class AttentionStore(): | |
def get_empty_store(): | |
return {"down_cross": [], "mid_cross": [], "up_cross": [], | |
"down_self": [], "mid_self": [], "up_self": []} | |
def __call__(self, attn, is_cross: bool, place_in_unet: str, editing_prompts, PnP=False): | |
# attn.shape = batch_size * head_size, seq_len query, seq_len_key | |
bs = 2 + int(PnP) + editing_prompts | |
skip = 2 if PnP else 1 # skip PnP & unconditional | |
head_size = int(attn.shape[0] / self.batch_size) | |
attn = torch.stack(attn.split(self.batch_size)).permute(1, 0, 2, 3) | |
source_batch_size = int(attn.shape[1] // bs) | |
self.forward( | |
attn[:, skip * source_batch_size:], | |
is_cross, | |
place_in_unet) | |
def forward(self, attn, is_cross: bool, place_in_unet: str): | |
key = f"{place_in_unet}_{'cross' if is_cross else 'self'}" | |
if attn.shape[1] <= 32 ** 2: # avoid memory overhead | |
self.step_store[key].append(attn) | |
def between_steps(self, store_step=True): | |
if store_step: | |
if self.average: | |
if len(self.attention_store) == 0: | |
self.attention_store = self.step_store | |
else: | |
for key in self.attention_store: | |
for i in range(len(self.attention_store[key])): | |
self.attention_store[key][i] += self.step_store[key][i] | |
else: | |
if len(self.attention_store) == 0: | |
self.attention_store = [self.step_store] | |
else: | |
self.attention_store.append(self.step_store) | |
self.cur_step += 1 | |
self.step_store = self.get_empty_store() | |
def get_attention(self, step: int): | |
if self.average: | |
attention = {key: [item / self.cur_step for item in self.attention_store[key]] for key in | |
self.attention_store} | |
else: | |
assert (step is not None) | |
attention = self.attention_store[step] | |
return attention | |
def aggregate_attention(self, attention_maps, prompts, res: int, | |
from_where: List[str], is_cross: bool, select: int | |
): | |
out = [[] for x in range(self.batch_size)] | |
num_pixels = res ** 2 | |
for location in from_where: | |
for bs_item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]: | |
for batch, item in enumerate(bs_item): | |
if item.shape[1] == num_pixels: | |
cross_maps = item.reshape(len(prompts), -1, res, res, item.shape[-1])[select] | |
out[batch].append(cross_maps) | |
out = torch.stack([torch.cat(x, dim=0) for x in out]) | |
# average over heads | |
out = out.sum(1) / out.shape[1] | |
return out | |
def __init__(self, average: bool, batch_size=1): | |
self.step_store = self.get_empty_store() | |
self.attention_store = [] | |
self.cur_step = 0 | |
self.average = average | |
self.batch_size = batch_size | |
class CrossAttnProcessor: | |
def __init__(self, attention_store, place_in_unet, PnP, editing_prompts): | |
self.attnstore = attention_store | |
self.place_in_unet = place_in_unet | |
self.editing_prompts = editing_prompts | |
self.PnP = PnP | |
def __call__( | |
self, | |
attn: Attention, | |
hidden_states, | |
encoder_hidden_states=None, | |
attention_mask=None, | |
temb=None, | |
): | |
assert (not attn.residual_connection) | |
assert (attn.spatial_norm is None) | |
assert (attn.group_norm is None) | |
assert (hidden_states.ndim != 4) | |
assert (encoder_hidden_states is not None) # is cross | |
batch_size, sequence_length, _ = ( | |
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape | |
) | |
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
query = attn.to_q(hidden_states) | |
if encoder_hidden_states is None: | |
encoder_hidden_states = hidden_states | |
elif attn.norm_cross: | |
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) | |
key = attn.to_k(encoder_hidden_states) | |
value = attn.to_v(encoder_hidden_states) | |
query = attn.head_to_batch_dim(query) | |
key = attn.head_to_batch_dim(key) | |
value = attn.head_to_batch_dim(value) | |
attention_probs = attn.get_attention_scores(query, key, attention_mask) | |
self.attnstore(attention_probs, | |
is_cross=True, | |
place_in_unet=self.place_in_unet, | |
editing_prompts=self.editing_prompts, | |
PnP=self.PnP) | |
hidden_states = torch.bmm(attention_probs, value) | |
hidden_states = attn.batch_to_head_dim(hidden_states) | |
# linear proj | |
hidden_states = attn.to_out[0](hidden_states) | |
# dropout | |
hidden_states = attn.to_out[1](hidden_states) | |
hidden_states = hidden_states / attn.rescale_output_factor | |
return hidden_states | |
# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionAttendAndExcitePipeline.GaussianSmoothing | |
class GaussianSmoothing(): | |
def __init__(self, device): | |
kernel_size = [3, 3] | |
sigma = [0.5, 0.5] | |
# The gaussian kernel is the product of the gaussian function of each dimension. | |
kernel = 1 | |
meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size]) | |
for size, std, mgrid in zip(kernel_size, sigma, meshgrids): | |
mean = (size - 1) / 2 | |
kernel *= 1 / (std * math.sqrt(2 * math.pi)) * torch.exp(-(((mgrid - mean) / (2 * std)) ** 2)) | |
# Make sure sum of values in gaussian kernel equals 1. | |
kernel = kernel / torch.sum(kernel) | |
# Reshape to depthwise convolutional weight | |
kernel = kernel.view(1, 1, *kernel.size()) | |
kernel = kernel.repeat(1, *[1] * (kernel.dim() - 1)) | |
self.weight = kernel.to(device) | |
def __call__(self, input): | |
""" | |
Arguments: | |
Apply gaussian filter to input. | |
input (torch.Tensor): Input to apply gaussian filter on. | |
Returns: | |
filtered (torch.Tensor): Filtered output. | |
""" | |
return F.conv2d(input, weight=self.weight.to(input.dtype)) | |
def load_512(image_path, size, left=0, right=0, top=0, bottom=0, device=None, dtype=None): | |
def pre_process(im, size, left=0, right=0, top=0, bottom=0): | |
if type(im) is str: | |
image = np.array(Image.open(im).convert('RGB'))[:, :, :3] | |
elif isinstance(im, Image.Image): | |
image = np.array((im).convert('RGB'))[:, :, :3] | |
else: | |
image = im | |
h, w, c = image.shape | |
left = min(left, w - 1) | |
right = min(right, w - left - 1) | |
top = min(top, h - left - 1) | |
bottom = min(bottom, h - top - 1) | |
image = image[top:h - bottom, left:w - right] | |
h, w, c = image.shape | |
if h < w: | |
offset = (w - h) // 2 | |
image = image[:, offset:offset + h] | |
elif w < h: | |
offset = (h - w) // 2 | |
image = image[offset:offset + w] | |
image = np.array(Image.fromarray(image).resize((size, size))) | |
image = torch.from_numpy(image).float().permute(2, 0, 1) | |
return image | |
tmps = [] | |
if isinstance(image_path, list): | |
for item in image_path: | |
tmps.append(pre_process(item, size, left, right, top, bottom)) | |
else: | |
tmps.append(pre_process(image_path, size, left, right, top, bottom)) | |
image = torch.stack(tmps) / 127.5 - 1 | |
image = image.to(device=device, dtype=dtype) | |
return image | |
# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionAttendAndExcitePipeline.GaussianSmoothing | |
def reset_dpm(scheduler): | |
if isinstance(scheduler, DPMSolverMultistepSchedulerInject): | |
scheduler.model_outputs = [ | |
None, | |
] * scheduler.config.solver_order | |
scheduler.lower_order_nums = 0 | |
class SemanticStableDiffusionImg2ImgPipeline_DPMSolver(DiffusionPipeline): | |
r""" | |
Pipeline for text-to-image generation with latent editing. | |
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the | |
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) | |
This model builds on the implementation of ['StableDiffusionPipeline'] | |
Args: | |
vae ([`AutoencoderKL`]): | |
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. | |
text_encoder ([`CLIPTextModel`]): | |
Frozen text-encoder. Stable Diffusion uses the text portion of | |
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically | |
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. | |
tokenizer (`CLIPTokenizer`): | |
Tokenizer of class | |
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). | |
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. | |
scheduler ([`SchedulerMixin`]): | |
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of | |
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. | |
safety_checker ([`Q16SafetyChecker`]): | |
Classification module that estimates whether generated images could be considered offensive or harmful. | |
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details. | |
feature_extractor ([`CLIPImageProcessor`]): | |
Model that extracts features from generated images to be used as inputs for the `safety_checker`. | |
""" | |
_optional_components = ["safety_checker", "feature_extractor"] | |
def __init__( | |
self, | |
vae: AutoencoderKL, | |
text_encoder: CLIPTextModel, | |
tokenizer: CLIPTokenizer, | |
unet: UNet2DConditionModel, | |
scheduler: Union[DDIMScheduler,DPMSolverMultistepSchedulerInject], | |
safety_checker: StableDiffusionSafetyChecker, | |
feature_extractor: CLIPImageProcessor, | |
requires_safety_checker: bool = True, | |
): | |
super().__init__() | |
if not isinstance(scheduler, DDIMScheduler) or not isinstance(scheduler, DPMSolverMultistepSchedulerInject): | |
scheduler = DPMSolverMultistepSchedulerInject.from_config(scheduler.config, algorithm_type="sde-dpmsolver++", solver_order=2) | |
logger.warning("This pipeline only supports DDIMScheduler and DPMSolverMultistepSchedulerInject. " | |
"The scheduler has been changed to DPMSolverMultistepSchedulerInject.") | |
if safety_checker is None and requires_safety_checker: | |
logger.warning( | |
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" | |
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" | |
" results in services or applications open to the public. Both the diffusers team and Hugging Face" | |
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" | |
" it only for use-cases that involve analyzing network behavior or auditing its results. For more" | |
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." | |
) | |
if safety_checker is not None and feature_extractor is None: | |
raise ValueError( | |
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" | |
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." | |
) | |
self.register_modules( | |
vae=vae, | |
text_encoder=text_encoder, | |
tokenizer=tokenizer, | |
unet=unet, | |
scheduler=scheduler, | |
safety_checker=safety_checker, | |
feature_extractor=feature_extractor, | |
) | |
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) | |
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) | |
self.register_to_config(requires_safety_checker=requires_safety_checker) | |
def progress_bar(self, iterable=None, total=None, verbose=True): | |
if not hasattr(self, "_progress_bar_config"): | |
self._progress_bar_config = {} | |
elif not isinstance(self._progress_bar_config, dict): | |
raise ValueError( | |
f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}." | |
) | |
if not verbose: | |
return iterable | |
elif iterable is not None: | |
return tqdm(iterable, **self._progress_bar_config) | |
elif total is not None: | |
return tqdm(total=total, **self._progress_bar_config) | |
else: | |
raise ValueError("Either `total` or `iterable` has to be defined.") | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker | |
def run_safety_checker(self, image, device, dtype): | |
if self.safety_checker is None: | |
has_nsfw_concept = None | |
else: | |
if torch.is_tensor(image): | |
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") | |
else: | |
feature_extractor_input = self.image_processor.numpy_to_pil(image) | |
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) | |
image, has_nsfw_concept = self.safety_checker( | |
images=image, clip_input=safety_checker_input.pixel_values.to(dtype) | |
) | |
return image, has_nsfw_concept | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents | |
def decode_latents(self, latents): | |
warnings.warn( | |
"The decode_latents method is deprecated and will be removed in a future version. Please" | |
" use VaeImageProcessor instead", | |
FutureWarning, | |
) | |
latents = 1 / self.vae.config.scaling_factor * latents | |
image = self.vae.decode(latents, return_dict=False)[0] | |
image = (image / 2 + 0.5).clamp(0, 1) | |
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 | |
image = image.cpu().permute(0, 2, 3, 1).float().numpy() | |
return image | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs | |
def prepare_extra_step_kwargs(self, eta): | |
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature | |
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. | |
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 | |
# and should be between [0, 1] | |
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) | |
extra_step_kwargs = {} | |
if accepts_eta: | |
extra_step_kwargs["eta"] = eta | |
return extra_step_kwargs | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs | |
def check_inputs( | |
self, | |
prompt, | |
height, | |
width, | |
callback_steps, | |
negative_prompt=None, | |
prompt_embeds=None, | |
negative_prompt_embeds=None, | |
): | |
if height % 8 != 0 or width % 8 != 0: | |
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") | |
if (callback_steps is None) or ( | |
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) | |
): | |
raise ValueError( | |
f"`callback_steps` has to be a positive integer but is {callback_steps} of type" | |
f" {type(callback_steps)}." | |
) | |
if prompt is not None and prompt_embeds is not None: | |
raise ValueError( | |
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" | |
" only forward one of the two." | |
) | |
elif prompt is None and prompt_embeds is None: | |
raise ValueError( | |
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." | |
) | |
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): | |
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | |
if negative_prompt is not None and negative_prompt_embeds is not None: | |
raise ValueError( | |
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" | |
f" {negative_prompt_embeds}. Please make sure to only forward one of the two." | |
) | |
if prompt_embeds is not None and negative_prompt_embeds is not None: | |
if prompt_embeds.shape != negative_prompt_embeds.shape: | |
raise ValueError( | |
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" | |
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" | |
f" {negative_prompt_embeds.shape}." | |
) | |
# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents | |
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, latents): | |
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) | |
if latents.shape != shape: | |
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") | |
latents = latents.to(device) | |
# scale the initial noise by the standard deviation required by the scheduler | |
latents = latents * self.scheduler.init_noise_sigma | |
return latents | |
def prepare_unet(self, attention_store, PnP: bool = False): | |
attn_procs = {} | |
for name in self.unet.attn_processors.keys(): | |
if name.startswith("mid_block"): | |
place_in_unet = "mid" | |
elif name.startswith("up_blocks"): | |
place_in_unet = "up" | |
elif name.startswith("down_blocks"): | |
place_in_unet = "down" | |
else: | |
continue | |
if "attn2" in name: | |
attn_procs[name] = CrossAttnProcessor( | |
attention_store=attention_store, | |
place_in_unet=place_in_unet, | |
PnP=PnP, | |
editing_prompts=self.enabled_editing_prompts) | |
else: | |
attn_procs[name] = AttnProcessor() | |
self.unet.set_attn_processor(attn_procs) | |
def __call__( | |
self, | |
prompt: Union[str, List[str]] = "", | |
height: Optional[int] = None, | |
width: Optional[int] = None, | |
# num_inference_steps: int = 50, | |
guidance_scale: float = 7.5, | |
negative_prompt: Optional[Union[str, List[str]]] = None, | |
# num_images_per_prompt: int = 1, | |
eta: float = 1.0, | |
# generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, | |
# latents: Optional[torch.FloatTensor] = None, | |
output_type: Optional[str] = "pil", | |
return_dict: bool = True, | |
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, | |
callback_steps: int = 1, | |
editing_prompt: Optional[Union[str, List[str]]] = None, | |
editing_prompt_embeddings: Optional[torch.Tensor] = None, | |
reverse_editing_direction: Optional[Union[bool, List[bool]]] = False, | |
edit_guidance_scale: Optional[Union[float, List[float]]] = 5, | |
edit_warmup_steps: Optional[Union[int, List[int]]] = 10, | |
edit_cooldown_steps: Optional[Union[int, List[int]]] = None, | |
edit_threshold: Optional[Union[float, List[float]]] = 0.9, | |
user_mask: Optional[torch.FloatTensor] = None, | |
edit_momentum_scale: Optional[float] = 0.1, | |
edit_mom_beta: Optional[float] = 0.4, | |
edit_weights: Optional[List[float]] = None, | |
sem_guidance: Optional[List[torch.Tensor]] = None, | |
verbose=True, | |
use_cross_attn_mask: bool = False, | |
# Attention store (just for visualization purposes) | |
attention_store = None, | |
text_cross_attention_maps = None, | |
attn_store_steps: Optional[List[int]] = [], | |
store_averaged_over_steps: bool = True, | |
use_intersect_mask: bool = False, | |
init_latents = None, | |
zs = None, | |
): | |
r""" | |
Function invoked when calling the pipeline for generation. | |
Args: | |
prompt (`str` or `List[str]`): | |
The prompt or prompts to guide the image generation. | |
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): | |
The height in pixels of the generated image. | |
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): | |
The width in pixels of the generated image. | |
num_inference_steps (`int`, *optional*, defaults to 50): | |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the | |
expense of slower inference. | |
guidance_scale (`float`, *optional*, defaults to 7.5): | |
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). | |
`guidance_scale` is defined as `w` of equation 2. of [Imagen | |
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > | |
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, | |
usually at the expense of lower image quality. | |
negative_prompt (`str` or `List[str]`, *optional*): | |
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored | |
if `guidance_scale` is less than `1`). | |
num_images_per_prompt (`int`, *optional*, defaults to 1): | |
The number of images to generate per prompt. | |
eta (`float`, *optional*, defaults to 0.0): | |
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to | |
[`schedulers.DDIMScheduler`], will be ignored for others. | |
generator (`torch.Generator`, *optional*): | |
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) | |
to make generation deterministic. | |
latents (`torch.FloatTensor`, *optional*): | |
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image | |
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents | |
tensor will ge generated by sampling using the supplied random `generator`. | |
output_type (`str`, *optional*, defaults to `"pil"`): | |
The output format of the generate image. Choose between | |
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. | |
return_dict (`bool`, *optional*, defaults to `True`): | |
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a | |
plain tuple. | |
callback (`Callable`, *optional*): | |
A function that will be called every `callback_steps` steps during inference. The function will be | |
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. | |
callback_steps (`int`, *optional*, defaults to 1): | |
The frequency at which the `callback` function will be called. If not specified, the callback will be | |
called at every step. | |
editing_prompt (`str` or `List[str]`, *optional*): | |
The prompt or prompts to use for Semantic guidance. Semantic guidance is disabled by setting | |
`editing_prompt = None`. Guidance direction of prompt should be specified via | |
`reverse_editing_direction`. | |
editing_prompt_embeddings (`torch.Tensor>`, *optional*): | |
Pre-computed embeddings to use for semantic guidance. Guidance direction of embedding should be | |
specified via `reverse_editing_direction`. | |
reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`): | |
Whether the corresponding prompt in `editing_prompt` should be increased or decreased. | |
edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5): | |
Guidance scale for semantic guidance. If provided as list values should correspond to `editing_prompt`. | |
`edit_guidance_scale` is defined as `s_e` of equation 6 of [SEGA | |
Paper](https://arxiv.org/pdf/2301.12247.pdf). | |
edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10): | |
Number of diffusion steps (for each prompt) for which semantic guidance will not be applied. Momentum | |
will still be calculated for those steps and applied once all warmup periods are over. | |
`edit_warmup_steps` is defined as `delta` (δ) of [SEGA Paper](https://arxiv.org/pdf/2301.12247.pdf). | |
edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`): | |
Number of diffusion steps (for each prompt) after which semantic guidance will no longer be applied. | |
edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9): | |
Threshold of semantic guidance. | |
edit_momentum_scale (`float`, *optional*, defaults to 0.1): | |
Scale of the momentum to be added to the semantic guidance at each diffusion step. If set to 0.0 | |
momentum will be disabled. Momentum is already built up during warmup, i.e. for diffusion steps smaller | |
than `sld_warmup_steps`. Momentum will only be added to latent guidance once all warmup periods are | |
finished. `edit_momentum_scale` is defined as `s_m` of equation 7 of [SEGA | |
Paper](https://arxiv.org/pdf/2301.12247.pdf). | |
edit_mom_beta (`float`, *optional*, defaults to 0.4): | |
Defines how semantic guidance momentum builds up. `edit_mom_beta` indicates how much of the previous | |
momentum will be kept. Momentum is already built up during warmup, i.e. for diffusion steps smaller | |
than `edit_warmup_steps`. `edit_mom_beta` is defined as `beta_m` (β) of equation 8 of [SEGA | |
Paper](https://arxiv.org/pdf/2301.12247.pdf). | |
edit_weights (`List[float]`, *optional*, defaults to `None`): | |
Indicates how much each individual concept should influence the overall guidance. If no weights are | |
provided all concepts are applied equally. `edit_mom_beta` is defined as `g_i` of equation 9 of [SEGA | |
Paper](https://arxiv.org/pdf/2301.12247.pdf). | |
sem_guidance (`List[torch.Tensor]`, *optional*): | |
List of pre-generated guidance vectors to be applied at generation. Length of the list has to | |
correspond to `num_inference_steps`. | |
Returns: | |
[`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] or `tuple`: | |
[`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] if `return_dict` is True, | |
otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the | |
second element is a list of `bool`s denoting whether the corresponding generated image likely represents | |
"not-safe-for-work" (nsfw) content, according to the `safety_checker`. | |
""" | |
self.unet.set_attn_processor(AttnProcessor()) | |
# eta = self.eta | |
num_images_per_prompt = 1 | |
# latents = self.init_latents | |
latents = init_latents | |
use_ddpm = True | |
# zs = self.zs | |
reset_dpm(self.scheduler) | |
if use_intersect_mask: | |
use_cross_attn_mask = True | |
if use_cross_attn_mask: | |
self.smoothing = GaussianSmoothing(self.device) | |
# 0. Default height and width to unet | |
height = height or self.unet.config.sample_size * self.vae_scale_factor | |
width = width or self.unet.config.sample_size * self.vae_scale_factor | |
# 1. Check inputs. Raise error if not correct | |
self.check_inputs(prompt, height, width, callback_steps) | |
org_prompt = prompt | |
if isinstance(prompt, list): | |
assert len(prompt) == self.batch_size | |
elif isinstance(prompt, str): | |
prompt = list(repeat(prompt, self.batch_size)) | |
# 2. Define call parameters | |
batch_size = self.batch_size | |
if editing_prompt: | |
enable_edit_guidance = True | |
if isinstance(editing_prompt, str): | |
editing_prompt = [editing_prompt] | |
self.enabled_editing_prompts = len(editing_prompt) | |
elif editing_prompt_embeddings is not None: | |
enable_edit_guidance = True | |
self.enabled_editing_prompts = editing_prompt_embeddings.shape[0] | |
else: | |
self.enabled_editing_prompts = 0 | |
enable_edit_guidance = False | |
# get prompt text embeddings | |
text_inputs = self.tokenizer( | |
prompt, | |
padding="max_length", | |
max_length=self.tokenizer.model_max_length, | |
truncation=True, | |
return_tensors="pt", | |
) | |
text_input_ids = text_inputs.input_ids | |
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids | |
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( | |
text_input_ids, untruncated_ids | |
): | |
removed_text = self.tokenizer.batch_decode( | |
untruncated_ids[:, self.tokenizer.model_max_length - 1: -1] | |
) | |
logger.warning( | |
"The following part of your input was truncated because CLIP can only handle sequences up to" | |
f" {self.tokenizer.model_max_length} tokens: {removed_text}" | |
) | |
text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0] | |
# duplicate text embeddings for each generation per prompt, using mps friendly method | |
bs_embed, seq_len, _ = text_embeddings.shape | |
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1) | |
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) | |
if enable_edit_guidance: | |
# get safety text embeddings | |
if editing_prompt_embeddings is None: | |
edit_concepts_input = self.tokenizer( | |
[x for item in editing_prompt for x in repeat(item, batch_size)], | |
padding="max_length", | |
max_length=self.tokenizer.model_max_length, | |
truncation=True, | |
return_tensors="pt", | |
return_length=True | |
) | |
num_edit_tokens = edit_concepts_input.length - 2 # not counting startoftext and endoftext | |
edit_concepts_input_ids = edit_concepts_input.input_ids | |
untruncated_ids = self.tokenizer( | |
[x for item in editing_prompt for x in repeat(item, batch_size)], | |
padding="longest", | |
return_tensors="pt").input_ids | |
if untruncated_ids.shape[-1] >= edit_concepts_input_ids.shape[-1] and not torch.equal( | |
edit_concepts_input_ids, untruncated_ids | |
): | |
removed_text = self.tokenizer.batch_decode( | |
untruncated_ids[:, self.tokenizer.model_max_length - 1: -1] | |
) | |
logger.warning( | |
"The following part of your input was truncated because CLIP can only handle sequences up to" | |
f" {self.tokenizer.model_max_length} tokens: {removed_text}" | |
) | |
edit_concepts = self.text_encoder(edit_concepts_input_ids.to(self.device))[0] | |
else: | |
edit_concepts = editing_prompt_embeddings.to(self.device).repeat(batch_size, 1, 1) | |
# duplicate text embeddings for each generation per prompt, using mps friendly method | |
bs_embed_edit, seq_len_edit, _ = edit_concepts.shape | |
edit_concepts = edit_concepts.repeat(1, num_images_per_prompt, 1) | |
edit_concepts = edit_concepts.view(bs_embed_edit * num_images_per_prompt, seq_len_edit, -1) | |
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) | |
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` | |
# corresponds to doing no classifier free guidance. | |
do_classifier_free_guidance = guidance_scale > 1.0 | |
# get unconditional embeddings for classifier free guidance | |
if do_classifier_free_guidance: | |
uncond_tokens: List[str] | |
if negative_prompt is None: | |
uncond_tokens = [""] | |
elif type(prompt) is not type(negative_prompt): | |
raise TypeError( | |
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" | |
f" {type(prompt)}." | |
) | |
elif isinstance(negative_prompt, str): | |
uncond_tokens = [negative_prompt] | |
elif batch_size != len(negative_prompt): | |
raise ValueError( | |
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" | |
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" | |
" the batch size of `prompt`." | |
) | |
else: | |
uncond_tokens = negative_prompt | |
max_length = text_input_ids.shape[-1] | |
uncond_input = self.tokenizer( | |
uncond_tokens, | |
padding="max_length", | |
max_length=max_length, | |
truncation=True, | |
return_tensors="pt", | |
) | |
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] | |
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method | |
seq_len = uncond_embeddings.shape[1] | |
uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1) | |
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1) | |
# 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 | |
text_cross_attention_maps = [org_prompt] if isinstance(org_prompt, str) else org_prompt | |
if enable_edit_guidance: | |
text_embeddings = torch.cat([uncond_embeddings, text_embeddings, edit_concepts]) | |
text_cross_attention_maps += \ | |
([editing_prompt] if isinstance(editing_prompt, str) else editing_prompt) | |
else: | |
text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) | |
# 4. Prepare timesteps | |
#self.scheduler.set_timesteps(num_inference_steps, device=self.device) | |
timesteps = self.scheduler.timesteps | |
if use_ddpm: | |
t_to_idx = {int(v): k for k, v in enumerate(timesteps[-zs.shape[0]:])} | |
timesteps = timesteps[-zs.shape[0]:] | |
if use_cross_attn_mask: | |
attention_store = AttentionStore(average=store_averaged_over_steps, batch_size=batch_size) | |
self.prepare_unet(attention_store, PnP=False) | |
# 5. Prepare latent variables | |
num_channels_latents = self.unet.config.in_channels | |
latents = self.prepare_latents( | |
batch_size * num_images_per_prompt, | |
num_channels_latents, | |
height, | |
width, | |
text_embeddings.dtype, | |
self.device, | |
latents, | |
) | |
# 6. Prepare extra step kwargs. | |
extra_step_kwargs = self.prepare_extra_step_kwargs(eta) | |
# Initialize edit_momentum to None | |
edit_momentum = None | |
self.uncond_estimates = None | |
self.text_estimates = None | |
self.edit_estimates = None | |
self.sem_guidance = None | |
self.activation_mask = None | |
for i, t in enumerate(self.progress_bar(timesteps, verbose=verbose)): | |
idx = t_to_idx[int(t)] | |
# expand the latents if we are doing classifier free guidance | |
if do_classifier_free_guidance: | |
latent_model_input = torch.cat([latents] * (2 + self.enabled_editing_prompts)) | |
else: | |
latent_model_input = latents | |
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | |
text_embed_input = text_embeddings | |
# predict the noise residual | |
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embed_input).sample | |
# perform guidance | |
if do_classifier_free_guidance: | |
noise_pred_out = noise_pred.chunk(2 + self.enabled_editing_prompts) # [b,4, 64, 64] | |
noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1] | |
noise_pred_edit_concepts = noise_pred_out[2:] | |
# default text guidance | |
noise_guidance = guidance_scale * (noise_pred_text - noise_pred_uncond) | |
if self.uncond_estimates is None: | |
self.uncond_estimates = torch.zeros((len(timesteps), *noise_pred_uncond.shape)) | |
self.uncond_estimates[i] = noise_pred_uncond.detach().cpu() | |
if self.text_estimates is None: | |
self.text_estimates = torch.zeros((len(timesteps), *noise_pred_text.shape)) | |
self.text_estimates[i] = noise_pred_text.detach().cpu() | |
if edit_momentum is None: | |
edit_momentum = torch.zeros_like(noise_guidance) | |
if sem_guidance is not None and len(sem_guidance) > i: | |
edit_guidance = sem_guidance[i].to(self.device) | |
edit_momentum = edit_mom_beta * edit_momentum + (1 - edit_mom_beta) * edit_guidance | |
noise_guidance = noise_guidance + edit_guidance | |
elif enable_edit_guidance: | |
if self.activation_mask is None: | |
self.activation_mask = torch.zeros( | |
(len(timesteps), len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape) | |
) | |
if self.edit_estimates is None and enable_edit_guidance: | |
self.edit_estimates = torch.zeros( | |
(len(timesteps), len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape) | |
) | |
if self.sem_guidance is None: | |
self.sem_guidance = torch.zeros((len(timesteps), *noise_pred_text.shape)) | |
concept_weights = torch.zeros( | |
(len(noise_pred_edit_concepts), noise_guidance.shape[0]), | |
device=self.device, | |
dtype=noise_guidance.dtype, | |
) | |
noise_guidance_edit = torch.zeros( | |
(len(noise_pred_edit_concepts), *noise_guidance.shape), | |
device=self.device, | |
dtype=noise_guidance.dtype, | |
) | |
# noise_guidance_edit = torch.zeros_like(noise_guidance) | |
warmup_inds = [] | |
for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts): | |
self.edit_estimates[i, c] = noise_pred_edit_concept | |
if isinstance(edit_guidance_scale, list): | |
edit_guidance_scale_c = edit_guidance_scale[c] | |
else: | |
edit_guidance_scale_c = edit_guidance_scale | |
if isinstance(edit_threshold, list): | |
edit_threshold_c = edit_threshold[c] | |
else: | |
edit_threshold_c = edit_threshold | |
if isinstance(reverse_editing_direction, list): | |
reverse_editing_direction_c = reverse_editing_direction[c] | |
else: | |
reverse_editing_direction_c = reverse_editing_direction | |
if edit_weights: | |
edit_weight_c = edit_weights[c] | |
else: | |
edit_weight_c = 1.0 | |
if isinstance(edit_warmup_steps, list): | |
edit_warmup_steps_c = edit_warmup_steps[c] | |
else: | |
edit_warmup_steps_c = edit_warmup_steps | |
if isinstance(edit_cooldown_steps, list): | |
edit_cooldown_steps_c = edit_cooldown_steps[c] | |
elif edit_cooldown_steps is None: | |
edit_cooldown_steps_c = i + 1 | |
else: | |
edit_cooldown_steps_c = edit_cooldown_steps | |
if i >= edit_warmup_steps_c: | |
warmup_inds.append(c) | |
if i >= edit_cooldown_steps_c: | |
noise_guidance_edit[c, :, :, :, :] = torch.zeros_like(noise_pred_edit_concept) | |
continue | |
noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond | |
# tmp_weights = (noise_pred_text - noise_pred_edit_concept).sum(dim=(1, 2, 3)) | |
tmp_weights = (noise_guidance - noise_pred_edit_concept).sum(dim=(1, 2, 3)) | |
tmp_weights = torch.full_like(tmp_weights, edit_weight_c) # * (1 / enabled_editing_prompts) | |
if reverse_editing_direction_c: | |
noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1 | |
concept_weights[c, :] = tmp_weights | |
noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c | |
if user_mask is not None: | |
noise_guidance_edit_tmp = noise_guidance_edit_tmp * user_mask | |
if use_cross_attn_mask: | |
out = attention_store.aggregate_attention( | |
attention_maps=attention_store.step_store, | |
prompts=text_cross_attention_maps, | |
res=16, | |
from_where=["up", "down"], | |
is_cross=True, | |
select=text_cross_attention_maps.index(editing_prompt[c]), | |
) | |
attn_map = out[:, :, :, 1:1 + num_edit_tokens[c]] # 0 -> startoftext | |
# average over all tokens | |
assert (attn_map.shape[3] == num_edit_tokens[c]) | |
attn_map = torch.sum(attn_map, dim=3) | |
# gaussian_smoothing | |
attn_map = F.pad(attn_map.unsqueeze(1), (1, 1, 1, 1), mode="reflect") | |
attn_map = self.smoothing(attn_map).squeeze(1) | |
# create binary mask | |
if attn_map.dtype == torch.float32: | |
tmp = torch.quantile(attn_map.flatten(start_dim=1), edit_threshold_c, dim=1) | |
else: | |
tmp = torch.quantile(attn_map.flatten(start_dim=1).to(torch.float32), edit_threshold_c, dim=1).to(attn_map.dtype) | |
attn_mask = torch.where(attn_map >= tmp.unsqueeze(1).unsqueeze(1).repeat(1,16,16), 1.0, 0.0) | |
# resolution must match latent space dimension | |
attn_mask = F.interpolate( | |
attn_mask.unsqueeze(1), | |
noise_guidance_edit_tmp.shape[-2:] # 64,64 | |
).repeat(1, 4, 1, 1) | |
self.activation_mask[i, c] = attn_mask.detach().cpu() | |
if not use_intersect_mask: | |
noise_guidance_edit_tmp = noise_guidance_edit_tmp * attn_mask | |
if use_intersect_mask: | |
noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp) | |
noise_guidance_edit_tmp_quantile = torch.sum(noise_guidance_edit_tmp_quantile, dim=1, | |
keepdim=True) | |
noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat(1, 4, 1, 1) | |
# torch.quantile function expects float32 | |
if noise_guidance_edit_tmp_quantile.dtype == torch.float32: | |
tmp = torch.quantile( | |
noise_guidance_edit_tmp_quantile.flatten(start_dim=2), | |
edit_threshold_c, | |
dim=2, | |
keepdim=False, | |
) | |
else: | |
tmp = torch.quantile( | |
noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32), | |
edit_threshold_c, | |
dim=2, | |
keepdim=False, | |
).to(noise_guidance_edit_tmp_quantile.dtype) | |
intersect_mask = torch.where( | |
noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None], | |
torch.ones_like(noise_guidance_edit_tmp), | |
torch.zeros_like(noise_guidance_edit_tmp), | |
) * attn_mask | |
self.activation_mask[i, c] = intersect_mask.detach().cpu() | |
noise_guidance_edit_tmp = noise_guidance_edit_tmp * intersect_mask | |
elif not use_cross_attn_mask: | |
# calculate quantile | |
noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp) | |
noise_guidance_edit_tmp_quantile = torch.sum(noise_guidance_edit_tmp_quantile, dim=1, | |
keepdim=True) | |
noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat(1, 4, 1, 1) | |
# torch.quantile function expects float32 | |
if noise_guidance_edit_tmp_quantile.dtype == torch.float32: | |
tmp = torch.quantile( | |
noise_guidance_edit_tmp_quantile.flatten(start_dim=2), | |
edit_threshold_c, | |
dim=2, | |
keepdim=False, | |
) | |
else: | |
tmp = torch.quantile( | |
noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32), | |
edit_threshold_c, | |
dim=2, | |
keepdim=False, | |
).to(noise_guidance_edit_tmp_quantile.dtype) | |
self.activation_mask[i, c] = torch.where( | |
noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None], | |
torch.ones_like(noise_guidance_edit_tmp), | |
torch.zeros_like(noise_guidance_edit_tmp), | |
).detach().cpu() | |
noise_guidance_edit_tmp = torch.where( | |
noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None], | |
noise_guidance_edit_tmp, | |
torch.zeros_like(noise_guidance_edit_tmp), | |
) | |
noise_guidance_edit[c, :, :, :, :] = noise_guidance_edit_tmp | |
warmup_inds = torch.tensor(warmup_inds).to(self.device) | |
if len(noise_pred_edit_concepts) > warmup_inds.shape[0] > 0: | |
concept_weights = concept_weights.to("cpu") # Offload to cpu | |
noise_guidance_edit = noise_guidance_edit.to("cpu") | |
concept_weights_tmp = torch.index_select(concept_weights.to(self.device), 0, warmup_inds) | |
concept_weights_tmp = torch.where( | |
concept_weights_tmp < 0, torch.zeros_like(concept_weights_tmp), concept_weights_tmp | |
) | |
concept_weights_tmp = concept_weights_tmp / concept_weights_tmp.sum(dim=0) | |
# concept_weights_tmp = torch.nan_to_num(concept_weights_tmp) | |
noise_guidance_edit_tmp = torch.index_select( | |
noise_guidance_edit.to(self.device), 0, warmup_inds | |
) | |
noise_guidance_edit_tmp = torch.einsum( | |
"cb,cbijk->bijk", concept_weights_tmp, noise_guidance_edit_tmp | |
) | |
noise_guidance_edit_tmp = noise_guidance_edit_tmp | |
noise_guidance = noise_guidance + noise_guidance_edit_tmp | |
self.sem_guidance[i] = noise_guidance_edit_tmp.detach().cpu() | |
del noise_guidance_edit_tmp | |
del concept_weights_tmp | |
concept_weights = concept_weights.to(self.device) | |
noise_guidance_edit = noise_guidance_edit.to(self.device) | |
concept_weights = torch.where( | |
concept_weights < 0, torch.zeros_like(concept_weights), concept_weights | |
) | |
concept_weights = torch.nan_to_num(concept_weights) | |
noise_guidance_edit = torch.einsum("cb,cbijk->bijk", concept_weights, noise_guidance_edit) | |
noise_guidance_edit = noise_guidance_edit + edit_momentum_scale * edit_momentum | |
edit_momentum = edit_mom_beta * edit_momentum + (1 - edit_mom_beta) * noise_guidance_edit | |
if warmup_inds.shape[0] == len(noise_pred_edit_concepts): | |
noise_guidance = noise_guidance + noise_guidance_edit | |
self.sem_guidance[i] = noise_guidance_edit.detach().cpu() | |
noise_pred = noise_pred_uncond + noise_guidance | |
# compute the previous noisy sample x_t -> x_t-1 | |
if use_ddpm: | |
idx = t_to_idx[int(t)] | |
latents = self.scheduler.step(noise_pred, t, latents, variance_noise=zs[idx], | |
**extra_step_kwargs).prev_sample | |
else: #if not use_ddpm: | |
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample | |
# step callback | |
if use_cross_attn_mask: | |
store_step = i in attn_store_steps | |
if store_step: | |
print(f"storing attention for step {i}") | |
attention_store.between_steps(store_step) | |
# call the callback, if provided | |
if callback is not None and i % callback_steps == 0: | |
callback(i, t, latents) | |
# 8. Post-processing | |
if not output_type == "latent": | |
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] | |
image, has_nsfw_concept = self.run_safety_checker(image, self.device, text_embeddings.dtype) | |
else: | |
image = latents | |
has_nsfw_concept = None | |
if has_nsfw_concept is None: | |
do_denormalize = [True] * image.shape[0] | |
else: | |
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] | |
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) | |
if not return_dict: | |
return (image, has_nsfw_concept), attention_store | |
return SemanticStableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept), attention_store, text_cross_attention_maps | |
def encode_text(self, prompts): | |
text_inputs = self.tokenizer( | |
prompts, | |
padding="max_length", | |
max_length=self.tokenizer.model_max_length, | |
return_tensors="pt", | |
) | |
text_input_ids = text_inputs.input_ids | |
if text_input_ids.shape[-1] > self.tokenizer.model_max_length: | |
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length:]) | |
logger.warning( | |
"The following part of your input was truncated because CLIP can only handle sequences up to" | |
f" {self.tokenizer.model_max_length} tokens: {removed_text}" | |
) | |
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length] | |
text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0] | |
return text_embeddings | |
def invert(self, | |
image_path: str, | |
source_prompt: str = "", | |
source_guidance_scale=3.5, | |
num_inversion_steps: int = 30, | |
skip: float = 0.15, | |
eta: float = 1.0, | |
generator: Optional[torch.Generator] = None, | |
verbose=True, | |
): | |
""" | |
Inverts a real image according to Algorihm 1 in https://arxiv.org/pdf/2304.06140.pdf, | |
based on the code in https://github.com/inbarhub/DDPM_inversion | |
returns: | |
zs - noise maps | |
xts - intermediate inverted latents | |
""" | |
# self.eta = eta | |
# assert (self.eta > 0) | |
skip = skip/100 | |
print("YOOOOOOOOOOOOOOOOO ", skip, num_inversion_steps) | |
train_steps = self.scheduler.config.num_train_timesteps | |
timesteps = torch.from_numpy( | |
np.linspace(train_steps - skip * train_steps - 1, 1, num_inversion_steps).astype(np.int64)).to(self.device) | |
self.num_inversion_steps = timesteps.shape[0] | |
self.scheduler.num_inference_steps = timesteps.shape[0] | |
self.scheduler.timesteps = timesteps | |
# Reset attn processor, we do not want to store attn maps during inversion | |
# self.unet.set_default_attn_processor() | |
self.unet.set_attn_processor(AttnProcessor()) | |
# 1. get embeddings | |
uncond_embedding = self.encode_text("") | |
# 2. encode image | |
x0 = self.encode_image(image_path, dtype=uncond_embedding.dtype) | |
self.batch_size = x0.shape[0] | |
if not source_prompt == "": | |
text_embeddings = self.encode_text(source_prompt).repeat((self.batch_size, 1, 1)) | |
uncond_embedding = uncond_embedding.repeat((self.batch_size, 1, 1)) | |
# autoencoder reconstruction | |
# image_rec = self.vae.decode(x0 / self.vae.config.scaling_factor, return_dict=False)[0] | |
# image_rec = self.image_processor.postprocess(image_rec, output_type="pil") | |
# 3. find zs and xts | |
variance_noise_shape = ( | |
self.num_inversion_steps, | |
self.batch_size, | |
self.unet.config.in_channels, | |
self.unet.sample_size, | |
self.unet.sample_size) | |
# intermediate latents | |
t_to_idx = {int(v): k for k, v in enumerate(timesteps)} | |
xts = torch.zeros(size=variance_noise_shape, device=self.device, dtype=uncond_embedding.dtype) | |
for t in reversed(timesteps): | |
idx = self.num_inversion_steps-t_to_idx[int(t)] - 1 | |
noise = randn_tensor(shape=x0.shape, generator=generator, device=self.device, dtype=x0.dtype) | |
xts[idx] = self.scheduler.add_noise(x0, noise, t) | |
xts = torch.cat([x0.unsqueeze(0), xts], dim=0) | |
reset_dpm(self.scheduler) | |
# noise maps | |
zs = torch.zeros(size=variance_noise_shape, device=self.device, dtype=uncond_embedding.dtype) | |
for t in self.progress_bar(timesteps, verbose=verbose): | |
idx = self.num_inversion_steps-t_to_idx[int(t)]-1 | |
# 1. predict noise residual | |
xt = xts[idx+1] | |
noise_pred = self.unet(xt, timestep=t, encoder_hidden_states=uncond_embedding).sample | |
if not source_prompt == "": | |
noise_pred_cond = self.unet(xt, timestep=t, encoder_hidden_states=text_embeddings).sample | |
noise_pred = noise_pred + source_guidance_scale * (noise_pred_cond - noise_pred) | |
xtm1 = xts[idx] | |
z, xtm1_corrected = compute_noise(self.scheduler, xtm1, xt, t, noise_pred, eta) | |
zs[idx] = z | |
# correction to avoid error accumulation | |
xts[idx] = xtm1_corrected | |
# TODO: I don't think that the noise map for the last step should be discarded ?! | |
# if not zs is None: | |
# zs[-1] = torch.zeros_like(zs[-1]) | |
# self.init_latents = xts[-1].expand(self.batch_size, -1, -1, -1) | |
zs = zs.flip(0) | |
# self.zs = zs | |
return zs, xts | |
# return zs, xts, image_rec | |
def encode_image(self, image_path, dtype=None): | |
image = load_512(image_path, | |
size=self.unet.sample_size * self.vae_scale_factor, | |
device=self.device, | |
dtype=dtype) | |
x0 = self.vae.encode(image).latent_dist.mode() | |
x0 = self.vae.config.scaling_factor * x0 | |
return x0 | |
def compute_noise_ddim(scheduler, prev_latents, latents, timestep, noise_pred, eta): | |
# 1. get previous step value (=t-1) | |
prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps | |
# 2. compute alphas, betas | |
alpha_prod_t = scheduler.alphas_cumprod[timestep] | |
alpha_prod_t_prev = ( | |
scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod | |
) | |
beta_prod_t = 1 - alpha_prod_t | |
# 3. compute predicted original sample from predicted noise also called | |
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf | |
pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5) | |
# 4. Clip "predicted x_0" | |
if scheduler.config.clip_sample: | |
pred_original_sample = torch.clamp(pred_original_sample, -1, 1) | |
# 5. compute variance: "sigma_t(η)" -> see formula (16) | |
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) | |
variance = scheduler._get_variance(timestep, prev_timestep) | |
std_dev_t = eta * variance ** (0.5) | |
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf | |
pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t ** 2) ** (0.5) * noise_pred | |
# modifed so that updated xtm1 is returned as well (to avoid error accumulation) | |
mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction | |
noise = (prev_latents - mu_xt) / (variance ** (0.5) * eta) | |
return noise, mu_xt + (eta * variance ** 0.5) * noise | |
# Copied from pipelines.StableDiffusion.CycleDiffusionPipeline.compute_noise | |
def compute_noise_sde_dpm_pp_2nd(scheduler, prev_latents, latents, timestep, noise_pred, eta): | |
def first_order_update(model_output, timestep, prev_timestep, sample): | |
lambda_t, lambda_s = scheduler.lambda_t[prev_timestep], scheduler.lambda_t[timestep] | |
alpha_t, alpha_s = scheduler.alpha_t[prev_timestep], scheduler.alpha_t[timestep] | |
sigma_t, sigma_s = scheduler.sigma_t[prev_timestep], scheduler.sigma_t[timestep] | |
h = lambda_t - lambda_s | |
mu_xt = ( | |
(sigma_t / sigma_s * torch.exp(-h)) * sample | |
+ (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output | |
) | |
sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) | |
noise = (prev_latents - mu_xt) / sigma | |
prev_sample = mu_xt + sigma * noise | |
return noise, prev_sample | |
def second_order_update(model_output_list, timestep_list, prev_timestep, sample): | |
t, s0, s1 = prev_timestep, timestep_list[-1], timestep_list[-2] | |
m0, m1 = model_output_list[-1], model_output_list[-2] | |
lambda_t, lambda_s0, lambda_s1 = scheduler.lambda_t[t], scheduler.lambda_t[s0], scheduler.lambda_t[s1] | |
alpha_t, alpha_s0 = scheduler.alpha_t[t], scheduler.alpha_t[s0] | |
sigma_t, sigma_s0 = scheduler.sigma_t[t], scheduler.sigma_t[s0] | |
h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1 | |
r0 = h_0 / h | |
D0, D1 = m0, (1.0 / r0) * (m0 - m1) | |
mu_xt = ( | |
(sigma_t / sigma_s0 * torch.exp(-h)) * sample | |
+ (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 | |
+ 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1 | |
) | |
sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) | |
noise = (prev_latents - mu_xt) / sigma | |
prev_sample = mu_xt + sigma * noise | |
return noise, prev_sample | |
step_index = (scheduler.timesteps == timestep).nonzero() | |
if len(step_index) == 0: | |
step_index = len(scheduler.timesteps) - 1 | |
else: | |
step_index = step_index.item() | |
prev_timestep = 0 if step_index == len(scheduler.timesteps) - 1 else scheduler.timesteps[step_index + 1] | |
model_output = scheduler.convert_model_output(noise_pred, timestep, latents) | |
for i in range(scheduler.config.solver_order - 1): | |
scheduler.model_outputs[i] = scheduler.model_outputs[i + 1] | |
scheduler.model_outputs[-1] = model_output | |
if scheduler.lower_order_nums < 1: | |
noise, prev_sample = first_order_update(model_output, timestep, prev_timestep, latents) | |
else: | |
timestep_list = [scheduler.timesteps[step_index - 1], timestep] | |
noise, prev_sample = second_order_update(scheduler.model_outputs, timestep_list, prev_timestep, latents) | |
if scheduler.lower_order_nums < scheduler.config.solver_order: | |
scheduler.lower_order_nums += 1 | |
return noise, prev_sample | |
def compute_noise(scheduler, *args): | |
if isinstance(scheduler, DDIMScheduler): | |
return compute_noise_ddim(scheduler, *args) | |
elif isinstance(scheduler, DPMSolverMultistepSchedulerInject) and scheduler.config.algorithm_type == 'sde-dpmsolver++'\ | |
and scheduler.config.solver_order == 2: | |
return compute_noise_sde_dpm_pp_2nd(scheduler, *args) | |
else: | |
raise NotImplementedError |