InstantIR / diffusers /pipelines /ledits_pp /pipeline_leditspp_stable_diffusion_xl.py
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# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import math
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionModelWithProjection,
)
from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import (
FromSingleFileMixin,
IPAdapterMixin,
StableDiffusionXLLoraLoaderMixin,
TextualInversionLoaderMixin,
)
from ...models import AutoencoderKL, UNet2DConditionModel
from ...models.attention_processor import (
Attention,
AttnProcessor,
AttnProcessor2_0,
LoRAAttnProcessor2_0,
LoRAXFormersAttnProcessor,
XFormersAttnProcessor,
)
from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import DDIMScheduler, DPMSolverMultistepScheduler
from ...utils import (
USE_PEFT_BACKEND,
is_invisible_watermark_available,
is_torch_xla_available,
logging,
replace_example_docstring,
scale_lora_layers,
unscale_lora_layers,
)
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline
from .pipeline_output import LEditsPPDiffusionPipelineOutput, LEditsPPInversionPipelineOutput
if is_invisible_watermark_available():
from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
if is_torch_xla_available():
import torch_xla.core.xla_model as xm
XLA_AVAILABLE = True
else:
XLA_AVAILABLE = False
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> import PIL
>>> import requests
>>> from io import BytesIO
>>> from diffusers import LEditsPPPipelineStableDiffusionXL
>>> pipe = LEditsPPPipelineStableDiffusionXL.from_pretrained(
... "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> def download_image(url):
... response = requests.get(url)
... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
>>> img_url = "https://www.aiml.informatik.tu-darmstadt.de/people/mbrack/tennis.jpg"
>>> image = download_image(img_url)
>>> _ = pipe.invert(image=image, num_inversion_steps=50, skip=0.2)
>>> edited_image = pipe(
... editing_prompt=["tennis ball", "tomato"],
... reverse_editing_direction=[True, False],
... edit_guidance_scale=[5.0, 10.0],
... edit_threshold=[0.9, 0.85],
... ).images[0]
```
"""
# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LeditsAttentionStore
class LeditsAttentionStore:
@staticmethod
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
if attn.shape[1] <= self.max_size:
bs = 1 + int(PnP) + editing_prompts
skip = 2 if PnP else 1 # skip PnP & unconditional
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'}"
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: Union[int, Tuple[int]], from_where: List[str], is_cross: bool, select: int
):
out = [[] for x in range(self.batch_size)]
if isinstance(res, int):
num_pixels = res**2
resolution = (res, res)
else:
num_pixels = res[0] * res[1]
resolution = 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, *resolution, 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, max_resolution=16, max_size: int = None):
self.step_store = self.get_empty_store()
self.attention_store = []
self.cur_step = 0
self.average = average
self.batch_size = batch_size
if max_size is None:
self.max_size = max_resolution**2
elif max_size is not None and max_resolution is None:
self.max_size = max_size
else:
raise ValueError("Only allowed to set one of max_resolution or max_size")
# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LeditsGaussianSmoothing
class LeditsGaussianSmoothing:
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))
# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEDITSCrossAttnProcessor
class LEDITSCrossAttnProcessor:
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,
attention_mask=None,
temb=None,
):
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
class LEditsPPPipelineStableDiffusionXL(
DiffusionPipeline,
FromSingleFileMixin,
StableDiffusionXLLoraLoaderMixin,
TextualInversionLoaderMixin,
IPAdapterMixin,
):
"""
Pipeline for textual image editing using LEDits++ with Stable Diffusion XL.
This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionXLPipeline`]. Check the
superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a
particular device, etc.).
In addition the pipeline inherits the following loading methods:
- *LoRA*: [`LEditsPPPipelineStableDiffusionXL.load_lora_weights`]
- *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]
as well as the following saving methods:
- *LoRA*: [`loaders.StableDiffusionXLPipeline.save_lora_weights`]
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion XL 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.
text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]):
Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
[CLIP](https://huggingface.co./docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
specifically the
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co./laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
variant.
tokenizer ([`~transformers.CLIPTokenizer`]):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co./docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 ([`~transformers.CLIPTokenizer`]):
Second 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 ([`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
[`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]. If any other scheduler is passed it will
automatically be set to [`DPMSolverMultistepScheduler`].
force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
`stabilityai/stable-diffusion-xl-base-1-0`.
add_watermarker (`bool`, *optional*):
Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
watermark output images. If not defined, it will default to True if the package is installed, otherwise no
watermarker will be used.
"""
model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
_optional_components = [
"tokenizer",
"tokenizer_2",
"text_encoder",
"text_encoder_2",
"image_encoder",
"feature_extractor",
]
_callback_tensor_inputs = [
"latents",
"prompt_embeds",
"negative_prompt_embeds",
"add_text_embeds",
"add_time_ids",
"negative_pooled_prompt_embeds",
"negative_add_time_ids",
]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
text_encoder_2: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
tokenizer_2: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DPMSolverMultistepScheduler, DDIMScheduler],
image_encoder: CLIPVisionModelWithProjection = None,
feature_extractor: CLIPImageProcessor = None,
force_zeros_for_empty_prompt: bool = True,
add_watermarker: Optional[bool] = None,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
unet=unet,
scheduler=scheduler,
image_encoder=image_encoder,
feature_extractor=feature_extractor,
)
self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
if not isinstance(scheduler, DDIMScheduler) and not isinstance(scheduler, DPMSolverMultistepScheduler):
self.scheduler = DPMSolverMultistepScheduler.from_config(
scheduler.config, algorithm_type="sde-dpmsolver++", solver_order=2
)
logger.warning(
"This pipeline only supports DDIMScheduler and DPMSolverMultistepScheduler. "
"The scheduler has been changed to DPMSolverMultistepScheduler."
)
self.default_sample_size = self.unet.config.sample_size
add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
if add_watermarker:
self.watermark = StableDiffusionXLWatermarker()
else:
self.watermark = None
self.inversion_steps = None
def encode_prompt(
self,
device: Optional[torch.device] = None,
num_images_per_prompt: int = 1,
negative_prompt: Optional[str] = None,
negative_prompt_2: Optional[str] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
enable_edit_guidance: bool = True,
editing_prompt: Optional[str] = None,
editing_prompt_embeds: Optional[torch.FloatTensor] = None,
editing_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
) -> object:
r"""
Encodes the prompt into text encoder hidden states.
Args:
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead.
negative_prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
enable_edit_guidance (`bool`):
Whether to guide towards an editing prompt or not.
editing_prompt (`str` or `List[str]`, *optional*):
Editing prompt(s) to be encoded. If not defined and 'enable_edit_guidance' is True, one has to pass
`editing_prompt_embeds` instead.
editing_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated edit text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided and 'enable_edit_guidance' is True, editing_prompt_embeds will be generated from
`editing_prompt` input argument.
editing_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated edit pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled editing_pooled_prompt_embeds will be generated from `editing_prompt`
input argument.
"""
device = device or self._execution_device
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is not None:
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
if self.text_encoder_2 is not None:
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
else:
scale_lora_layers(self.text_encoder_2, lora_scale)
batch_size = self.batch_size
# Define tokenizers and text encoders
tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
text_encoders = (
[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
)
num_edit_tokens = 0
# get unconditional embeddings for classifier free guidance
zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
if negative_prompt_embeds is None:
negative_prompt = negative_prompt or ""
negative_prompt_2 = negative_prompt_2 or negative_prompt
# normalize str to list
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
negative_prompt_2 = (
batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
)
uncond_tokens: List[str]
if batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but image inversion "
f" has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of the input images."
)
else:
uncond_tokens = [negative_prompt, negative_prompt_2]
negative_prompt_embeds_list = []
for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
if isinstance(self, TextualInversionLoaderMixin):
negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
uncond_input = tokenizer(
negative_prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
negative_prompt_embeds = text_encoder(
uncond_input.input_ids.to(device),
output_hidden_states=True,
)
# We are only ALWAYS interested in the pooled output of the final text encoder
negative_pooled_prompt_embeds = negative_prompt_embeds[0]
negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
negative_prompt_embeds_list.append(negative_prompt_embeds)
negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
if zero_out_negative_prompt:
negative_prompt_embeds = torch.zeros_like(negative_prompt_embeds)
negative_pooled_prompt_embeds = torch.zeros_like(negative_pooled_prompt_embeds)
if enable_edit_guidance and editing_prompt_embeds is None:
editing_prompt_2 = editing_prompt
editing_prompts = [editing_prompt, editing_prompt_2]
edit_prompt_embeds_list = []
for editing_prompt, tokenizer, text_encoder in zip(editing_prompts, tokenizers, text_encoders):
if isinstance(self, TextualInversionLoaderMixin):
editing_prompt = self.maybe_convert_prompt(editing_prompt, tokenizer)
max_length = negative_prompt_embeds.shape[1]
edit_concepts_input = tokenizer(
# [x for item in editing_prompt for x in repeat(item, batch_size)],
editing_prompt,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
return_length=True,
)
num_edit_tokens = edit_concepts_input.length - 2
edit_concepts_embeds = text_encoder(
edit_concepts_input.input_ids.to(device),
output_hidden_states=True,
)
# We are only ALWAYS interested in the pooled output of the final text encoder
editing_pooled_prompt_embeds = edit_concepts_embeds[0]
if clip_skip is None:
edit_concepts_embeds = edit_concepts_embeds.hidden_states[-2]
else:
# "2" because SDXL always indexes from the penultimate layer.
edit_concepts_embeds = edit_concepts_embeds.hidden_states[-(clip_skip + 2)]
edit_prompt_embeds_list.append(edit_concepts_embeds)
edit_concepts_embeds = torch.concat(edit_prompt_embeds_list, dim=-1)
elif not enable_edit_guidance:
edit_concepts_embeds = None
editing_pooled_prompt_embeds = None
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
bs_embed, seq_len, _ = negative_prompt_embeds.shape
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
if enable_edit_guidance:
bs_embed_edit, seq_len, _ = edit_concepts_embeds.shape
edit_concepts_embeds = edit_concepts_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
edit_concepts_embeds = edit_concepts_embeds.repeat(1, num_images_per_prompt, 1)
edit_concepts_embeds = edit_concepts_embeds.view(bs_embed_edit * num_images_per_prompt, seq_len, -1)
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
if enable_edit_guidance:
editing_pooled_prompt_embeds = editing_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed_edit * num_images_per_prompt, -1
)
if self.text_encoder is not None:
if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder, lora_scale)
if self.text_encoder_2 is not None:
if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder_2, lora_scale)
return (
negative_prompt_embeds,
edit_concepts_embeds,
negative_pooled_prompt_embeds,
editing_pooled_prompt_embeds,
num_edit_tokens,
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, eta, generator=None):
# 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
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
negative_prompt=None,
negative_prompt_2=None,
negative_prompt_embeds=None,
negative_pooled_prompt_embeds=None,
):
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."
)
elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
raise ValueError(
"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
)
# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, device, latents):
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 _get_add_time_ids(
self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
):
add_time_ids = list(original_size + crops_coords_top_left + target_size)
passed_add_embed_dim = (
self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
)
expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
if expected_add_embed_dim != passed_add_embed_dim:
raise ValueError(
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
)
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
return add_time_ids
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
def upcast_vae(self):
dtype = self.vae.dtype
self.vae.to(dtype=torch.float32)
use_torch_2_0_or_xformers = isinstance(
self.vae.decoder.mid_block.attentions[0].processor,
(
AttnProcessor2_0,
XFormersAttnProcessor,
LoRAXFormersAttnProcessor,
LoRAAttnProcessor2_0,
),
)
# if xformers or torch_2_0 is used attention block does not need
# to be in float32 which can save lots of memory
if use_torch_2_0_or_xformers:
self.vae.post_quant_conv.to(dtype)
self.vae.decoder.conv_in.to(dtype)
self.vae.decoder.mid_block.to(dtype)
# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
def get_guidance_scale_embedding(
self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
) -> torch.FloatTensor:
"""
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
w (`torch.Tensor`):
Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
embedding_dim (`int`, *optional*, defaults to 512):
Dimension of the embeddings to generate.
dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
Data type of the generated embeddings.
Returns:
`torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
"""
assert len(w.shape) == 1
w = w * 1000.0
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
emb = w.to(dtype)[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0, 1))
assert emb.shape == (w.shape[0], embedding_dim)
return emb
@property
def guidance_scale(self):
return self._guidance_scale
@property
def guidance_rescale(self):
return self._guidance_rescale
@property
def clip_skip(self):
return self._clip_skip
# 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.
@property
def do_classifier_free_guidance(self):
return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
@property
def cross_attention_kwargs(self):
return self._cross_attention_kwargs
@property
def denoising_end(self):
return self._denoising_end
@property
def num_timesteps(self):
return self._num_timesteps
# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEditsPPPipelineStableDiffusion.prepare_unet
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 and place_in_unet != "mid":
attn_procs[name] = LEDITSCrossAttnProcessor(
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)
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
denoising_end: Optional[float] = None,
negative_prompt: Optional[Union[str, List[str]]] = None,
negative_prompt_2: Optional[Union[str, List[str]]] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guidance_rescale: float = 0.0,
crops_coords_top_left: Tuple[int, int] = (0, 0),
target_size: Optional[Tuple[int, int]] = None,
editing_prompt: Optional[Union[str, List[str]]] = None,
editing_prompt_embeddings: Optional[torch.Tensor] = None,
editing_pooled_prompt_embeds: 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]]] = 0,
edit_cooldown_steps: Optional[Union[int, List[int]]] = None,
edit_threshold: Optional[Union[float, List[float]]] = 0.9,
sem_guidance: Optional[List[torch.Tensor]] = None,
use_cross_attn_mask: bool = False,
use_intersect_mask: bool = False,
user_mask: Optional[torch.FloatTensor] = None,
attn_store_steps: Optional[List[int]] = [],
store_averaged_over_steps: bool = True,
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
r"""
The call function to the pipeline for editing. The
[`~pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.invert`] method has to be called beforehand. Edits
will always be performed for the last inverted image(s).
Args:
denoising_end (`float`, *optional*):
When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
completed before it is intentionally prematurely terminated. As a result, the returned sample will
still retain a substantial amount of noise as determined by the discrete timesteps selected by the
scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
"Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
negative_prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*):
Optional image input to work with IP Adapters.
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_xl.StableDiffusionXLPipelineOutput`] 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.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
guidance_rescale (`float`, *optional*, defaults to 0.7):
Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
[Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
Guidance rescale factor should fix overexposure when using zero terminal SNR.
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
[https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952).
target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
For most cases, `target_size` should be set to the desired height and width of the generated image. If
not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
section 2.2 of [https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952).
editing_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. The image is reconstructed by setting
`editing_prompt = None`. Guidance direction of prompt should be specified via
`reverse_editing_direction`.
editing_prompt_embeddings (`torch.Tensor`, *optional*):
Pre-generated edit text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided, editing_prompt_embeddings will be generated from `editing_prompt` input argument.
editing_pooled_prompt_embeddings (`torch.Tensor`, *optional*):
Pre-generated pooled edit text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, editing_prompt_embeddings will be generated from `editing_prompt` input
argument.
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 guiding the image generation. If provided as list values should correspond to
`editing_prompt`. `edit_guidance_scale` is defined as `s_e` of equation 12 of [LEDITS++
Paper](https://arxiv.org/abs/2301.12247).
edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10):
Number of diffusion steps (for each prompt) for which guidance is not applied.
edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`):
Number of diffusion steps (for each prompt) after which guidance is no longer applied.
edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9):
Masking threshold of guidance. Threshold should be proportional to the image region that is modified.
'edit_threshold' is defined as 'λ' of equation 12 of [LEDITS++
Paper](https://arxiv.org/abs/2301.12247).
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`.
use_cross_attn_mask:
Whether cross-attention masks are used. Cross-attention masks are always used when use_intersect_mask
is set to true. Cross-attention masks are defined as 'M^1' of equation 12 of [LEDITS++
paper](https://arxiv.org/pdf/2311.16711.pdf).
use_intersect_mask:
Whether the masking term is calculated as intersection of cross-attention masks and masks derived from
the noise estimate. Cross-attention mask are defined as 'M^1' and masks derived from the noise estimate
are defined as 'M^2' of equation 12 of [LEDITS++ paper](https://arxiv.org/pdf/2311.16711.pdf).
user_mask:
User-provided mask for even better control over the editing process. This is helpful when LEDITS++'s
implicit masks do not meet user preferences.
attn_store_steps:
Steps for which the attention maps are stored in the AttentionStore. Just for visualization purposes.
store_averaged_over_steps:
Whether the attention maps for the 'attn_store_steps' are stored averaged over the diffusion steps. If
False, attention maps for each step are stores separately. Just for visualization purposes.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
Examples:
Returns:
[`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
returning a tuple, the first element is a list with the generated images.
"""
if self.inversion_steps is None:
raise ValueError(
"You need to invert an input image first before calling the pipeline. The `invert` method has to be called beforehand. Edits will always be performed for the last inverted image(s)."
)
eta = self.eta
num_images_per_prompt = 1
latents = self.init_latents
zs = self.zs
self.scheduler.set_timesteps(len(self.scheduler.timesteps))
if use_intersect_mask:
use_cross_attn_mask = True
if use_cross_attn_mask:
self.smoothing = LeditsGaussianSmoothing(self.device)
if user_mask is not None:
user_mask = user_mask.to(self.device)
# TODO: Check inputs
# 1. Check inputs. Raise error if not correct
# self.check_inputs(
# callback_steps,
# negative_prompt,
# negative_prompt_2,
# prompt_embeds,
# negative_prompt_embeds,
# pooled_prompt_embeds,
# negative_pooled_prompt_embeds,
# )
self._guidance_rescale = guidance_rescale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs
self._denoising_end = denoising_end
# 2. Define call parameters
batch_size = self.batch_size
device = self._execution_device
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
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
(
prompt_embeds,
edit_prompt_embeds,
negative_pooled_prompt_embeds,
pooled_edit_embeds,
num_edit_tokens,
) = self.encode_prompt(
device=device,
num_images_per_prompt=num_images_per_prompt,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
negative_prompt_embeds=negative_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
lora_scale=text_encoder_lora_scale,
clip_skip=self.clip_skip,
enable_edit_guidance=enable_edit_guidance,
editing_prompt=editing_prompt,
editing_prompt_embeds=editing_prompt_embeddings,
editing_pooled_prompt_embeds=editing_pooled_prompt_embeds,
)
# 4. Prepare timesteps
# self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.inversion_steps
t_to_idx = {int(v): k for k, v in enumerate(timesteps)}
if use_cross_attn_mask:
self.attention_store = LeditsAttentionStore(
average=store_averaged_over_steps,
batch_size=batch_size,
max_size=(latents.shape[-2] / 4.0) * (latents.shape[-1] / 4.0),
max_resolution=None,
)
self.prepare_unet(self.attention_store)
resolution = latents.shape[-2:]
att_res = (int(resolution[0] / 4), int(resolution[1] / 4))
# 5. Prepare latent variables
latents = self.prepare_latents(device=device, latents=latents)
# 6. Prepare extra step kwargs.
extra_step_kwargs = self.prepare_extra_step_kwargs(eta)
if self.text_encoder_2 is None:
text_encoder_projection_dim = int(negative_pooled_prompt_embeds.shape[-1])
else:
text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
# 7. Prepare added time ids & embeddings
add_text_embeds = negative_pooled_prompt_embeds
add_time_ids = self._get_add_time_ids(
self.size,
crops_coords_top_left,
self.size,
dtype=negative_pooled_prompt_embeds.dtype,
text_encoder_projection_dim=text_encoder_projection_dim,
)
if enable_edit_guidance:
prompt_embeds = torch.cat([prompt_embeds, edit_prompt_embeds], dim=0)
add_text_embeds = torch.cat([add_text_embeds, pooled_edit_embeds], dim=0)
edit_concepts_time_ids = add_time_ids.repeat(edit_prompt_embeds.shape[0], 1)
add_time_ids = torch.cat([add_time_ids, edit_concepts_time_ids], dim=0)
self.text_cross_attention_maps = [editing_prompt] if isinstance(editing_prompt, str) else editing_prompt
prompt_embeds = prompt_embeds.to(device)
add_text_embeds = add_text_embeds.to(device)
add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
if ip_adapter_image is not None:
# TODO: fix image encoding
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
if self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
image_embeds = image_embeds.to(device)
# 8. Denoising loop
self.sem_guidance = None
self.activation_mask = None
if (
self.denoising_end is not None
and isinstance(self.denoising_end, float)
and self.denoising_end > 0
and self.denoising_end < 1
):
discrete_timestep_cutoff = int(
round(
self.scheduler.config.num_train_timesteps
- (self.denoising_end * self.scheduler.config.num_train_timesteps)
)
)
num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
timesteps = timesteps[:num_inference_steps]
# 9. Optionally get Guidance Scale Embedding
timestep_cond = None
if self.unet.config.time_cond_proj_dim is not None:
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
timestep_cond = self.get_guidance_scale_embedding(
guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
).to(device=device, dtype=latents.dtype)
self._num_timesteps = len(timesteps)
with self.progress_bar(total=self._num_timesteps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * (1 + self.enabled_editing_prompts))
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
if ip_adapter_image is not None:
added_cond_kwargs["image_embeds"] = image_embeds
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
noise_pred_out = noise_pred.chunk(1 + self.enabled_editing_prompts) # [b,4, 64, 64]
noise_pred_uncond = noise_pred_out[0]
noise_pred_edit_concepts = noise_pred_out[1:]
noise_guidance_edit = torch.zeros(
noise_pred_uncond.shape,
device=self.device,
dtype=noise_pred_uncond.dtype,
)
if sem_guidance is not None and len(sem_guidance) > i:
noise_guidance_edit += sem_guidance[i].to(self.device)
elif enable_edit_guidance:
if self.activation_mask is None:
self.activation_mask = torch.zeros(
(len(timesteps), self.enabled_editing_prompts, *noise_pred_edit_concepts[0].shape)
)
if self.sem_guidance is None:
self.sem_guidance = torch.zeros((len(timesteps), *noise_pred_uncond.shape))
# noise_guidance_edit = torch.zeros_like(noise_guidance)
for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):
if isinstance(edit_warmup_steps, list):
edit_warmup_steps_c = edit_warmup_steps[c]
else:
edit_warmup_steps_c = edit_warmup_steps
if i < edit_warmup_steps_c:
continue
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 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_cooldown_steps_c:
continue
noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond
if reverse_editing_direction_c:
noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1
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 = self.attention_store.aggregate_attention(
attention_maps=self.attention_store.step_store,
prompts=self.text_cross_attention_maps,
res=att_res,
from_where=["up", "down"],
is_cross=True,
select=self.text_cross_attention_maps.index(editing_prompt[c]),
)
attn_map = out[:, :, :, 1 : 1 + num_edit_tokens[c]] # 0 -> startoftext
# average over all tokens
if attn_map.shape[3] != num_edit_tokens[c]:
raise ValueError(
f"Incorrect shape of attention_map. Expected size {num_edit_tokens[c]}, but found {attn_map.shape[3]}!"
)
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)
# torch.quantile function expects float32
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, *att_res), 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, self.unet.config.in_channels, 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 += noise_guidance_edit_tmp
self.sem_guidance[i] = noise_guidance_edit.detach().cpu()
noise_pred = noise_pred_uncond + noise_guidance_edit
# compute the previous noisy sample x_t -> x_t-1
if enable_edit_guidance and self.guidance_rescale > 0.0:
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
noise_pred = rescale_noise_cfg(
noise_pred,
noise_pred_edit_concepts.mean(dim=0, keepdim=False),
guidance_rescale=self.guidance_rescale,
)
idx = t_to_idx[int(t)]
latents = self.scheduler.step(
noise_pred, t, latents, variance_noise=zs[idx], **extra_step_kwargs, return_dict=False
)[0]
# step callback
if use_cross_attn_mask:
store_step = i in attn_store_steps
self.attention_store.between_steps(store_step)
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
negative_pooled_prompt_embeds = callback_outputs.pop(
"negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
)
add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
# negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > 0 and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if XLA_AVAILABLE:
xm.mark_step()
if not output_type == "latent":
# make sure the VAE is in float32 mode, as it overflows in float16
needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
if needs_upcasting:
self.upcast_vae()
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
# cast back to fp16 if needed
if needs_upcasting:
self.vae.to(dtype=torch.float16)
else:
image = latents
if not output_type == "latent":
# apply watermark if available
if self.watermark is not None:
image = self.watermark.apply_watermark(image)
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return LEditsPPDiffusionPipelineOutput(images=image, nsfw_content_detected=None)
@torch.no_grad()
# Modified from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEditsPPPipelineStableDiffusion.encode_image
def encode_image(self, image, dtype=None, height=None, width=None, resize_mode="default", crops_coords=None):
image = self.image_processor.preprocess(
image=image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords
)
resized = self.image_processor.postprocess(image=image, output_type="pil")
if max(image.shape[-2:]) > self.vae.config["sample_size"] * 1.5:
logger.warning(
"Your input images far exceed the default resolution of the underlying diffusion model. "
"The output images may contain severe artifacts! "
"Consider down-sampling the input using the `height` and `width` parameters"
)
image = image.to(self.device, dtype=dtype)
needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
if needs_upcasting:
image = image.float()
self.upcast_vae()
image = image.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
x0 = self.vae.encode(image).latent_dist.mode()
x0 = x0.to(dtype)
# cast back to fp16 if needed
if needs_upcasting:
self.vae.to(dtype=torch.float16)
x0 = self.vae.config.scaling_factor * x0
return x0, resized
@torch.no_grad()
def invert(
self,
image: PipelineImageInput,
source_prompt: str = "",
source_guidance_scale=3.5,
negative_prompt: str = None,
negative_prompt_2: str = None,
num_inversion_steps: int = 50,
skip: float = 0.15,
generator: Optional[torch.Generator] = None,
crops_coords_top_left: Tuple[int, int] = (0, 0),
num_zero_noise_steps: int = 3,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
The function to the pipeline for image inversion as described by the [LEDITS++
Paper](https://arxiv.org/abs/2301.12247). If the scheduler is set to [`~schedulers.DDIMScheduler`] the
inversion proposed by [edit-friendly DPDM](https://arxiv.org/abs/2304.06140) will be performed instead.
Args:
image (`PipelineImageInput`):
Input for the image(s) that are to be edited. Multiple input images have to default to the same aspect
ratio.
source_prompt (`str`, defaults to `""`):
Prompt describing the input image that will be used for guidance during inversion. Guidance is disabled
if the `source_prompt` is `""`.
source_guidance_scale (`float`, defaults to `3.5`):
Strength of guidance during inversion.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
negative_prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
num_inversion_steps (`int`, defaults to `50`):
Number of total performed inversion steps after discarding the initial `skip` steps.
skip (`float`, defaults to `0.15`):
Portion of initial steps that will be ignored for inversion and subsequent generation. Lower values
will lead to stronger changes to the input image. `skip` has to be between `0` and `1`.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make inversion
deterministic.
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
[https://huggingface.co./papers/2307.01952](https://huggingface.co./papers/2307.01952).
num_zero_noise_steps (`int`, defaults to `3`):
Number of final diffusion steps that will not renoise the current image. If no steps are set to zero
SD-XL in combination with [`DPMSolverMultistepScheduler`] will produce noise artifacts.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Returns:
[`~pipelines.ledits_pp.LEditsPPInversionPipelineOutput`]: Output will contain the resized input image(s)
and respective VAE reconstruction(s).
"""
# Reset attn processor, we do not want to store attn maps during inversion
self.unet.set_attn_processor(AttnProcessor())
self.eta = 1.0
self.scheduler.config.timestep_spacing = "leading"
self.scheduler.set_timesteps(int(num_inversion_steps * (1 + skip)))
self.inversion_steps = self.scheduler.timesteps[-num_inversion_steps:]
timesteps = self.inversion_steps
num_images_per_prompt = 1
device = self._execution_device
# 0. Ensure that only uncond embedding is used if prompt = ""
if source_prompt == "":
# noise pred should only be noise_pred_uncond
source_guidance_scale = 0.0
do_classifier_free_guidance = False
else:
do_classifier_free_guidance = source_guidance_scale > 1.0
# 1. prepare image
x0, resized = self.encode_image(image, dtype=self.text_encoder_2.dtype)
width = x0.shape[2] * self.vae_scale_factor
height = x0.shape[3] * self.vae_scale_factor
self.size = (height, width)
self.batch_size = x0.shape[0]
# 2. get embeddings
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
if isinstance(source_prompt, str):
source_prompt = [source_prompt] * self.batch_size
(
negative_prompt_embeds,
prompt_embeds,
negative_pooled_prompt_embeds,
edit_pooled_prompt_embeds,
_,
) = self.encode_prompt(
device=device,
num_images_per_prompt=num_images_per_prompt,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
editing_prompt=source_prompt,
lora_scale=text_encoder_lora_scale,
enable_edit_guidance=do_classifier_free_guidance,
)
if self.text_encoder_2 is None:
text_encoder_projection_dim = int(negative_pooled_prompt_embeds.shape[-1])
else:
text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
# 3. Prepare added time ids & embeddings
add_text_embeds = negative_pooled_prompt_embeds
add_time_ids = self._get_add_time_ids(
self.size,
crops_coords_top_left,
self.size,
dtype=negative_prompt_embeds.dtype,
text_encoder_projection_dim=text_encoder_projection_dim,
)
if do_classifier_free_guidance:
negative_prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
add_text_embeds = torch.cat([add_text_embeds, edit_pooled_prompt_embeds], dim=0)
add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
negative_prompt_embeds = negative_prompt_embeds.to(device)
add_text_embeds = add_text_embeds.to(device)
add_time_ids = add_time_ids.to(device).repeat(self.batch_size * num_images_per_prompt, 1)
# autoencoder reconstruction
if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
self.upcast_vae()
x0_tmp = x0.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
image_rec = self.vae.decode(
x0_tmp / self.vae.config.scaling_factor, return_dict=False, generator=generator
)[0]
elif self.vae.config.force_upcast:
x0_tmp = x0.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
image_rec = self.vae.decode(
x0_tmp / self.vae.config.scaling_factor, return_dict=False, generator=generator
)[0]
else:
image_rec = self.vae.decode(x0 / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0]
image_rec = self.image_processor.postprocess(image_rec, output_type="pil")
# 5. find zs and xts
variance_noise_shape = (num_inversion_steps, *x0.shape)
# 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=negative_prompt_embeds.dtype)
for t in reversed(timesteps):
idx = 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.unsqueeze(0))
xts = torch.cat([x0.unsqueeze(0), xts], dim=0)
# noise maps
zs = torch.zeros(size=variance_noise_shape, device=self.device, dtype=negative_prompt_embeds.dtype)
self.scheduler.set_timesteps(len(self.scheduler.timesteps))
for t in self.progress_bar(timesteps):
idx = num_inversion_steps - t_to_idx[int(t)] - 1
# 1. predict noise residual
xt = xts[idx + 1]
latent_model_input = torch.cat([xt] * 2) if do_classifier_free_guidance else xt
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=negative_prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
# 2. perform guidance
if do_classifier_free_guidance:
noise_pred_out = noise_pred.chunk(2)
noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]
noise_pred = noise_pred_uncond + source_guidance_scale * (noise_pred_text - noise_pred_uncond)
xtm1 = xts[idx]
z, xtm1_corrected = compute_noise(self.scheduler, xtm1, xt, t, noise_pred, self.eta)
zs[idx] = z
# correction to avoid error accumulation
xts[idx] = xtm1_corrected
self.init_latents = xts[-1]
zs = zs.flip(0)
if num_zero_noise_steps > 0:
zs[-num_zero_noise_steps:] = torch.zeros_like(zs[-num_zero_noise_steps:])
self.zs = zs
return LEditsPPInversionPipelineOutput(images=resized, vae_reconstruction_images=image_rec)
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.rescale_noise_cfg
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
"""
Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
"""
std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
# rescale the results from guidance (fixes overexposure)
noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
return noise_cfg
# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise_ddim
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
if variance > 0.0:
noise = (prev_latents - mu_xt) / (variance ** (0.5) * eta)
else:
noise = torch.tensor([0.0]).to(latents.device)
return noise, mu_xt + (eta * variance**0.5) * noise
# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise_sde_dpm_pp_2nd
def compute_noise_sde_dpm_pp_2nd(scheduler, prev_latents, latents, timestep, noise_pred, eta):
def first_order_update(model_output, sample): # timestep, prev_timestep, sample):
sigma_t, sigma_s = scheduler.sigmas[scheduler.step_index + 1], scheduler.sigmas[scheduler.step_index]
alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t)
alpha_s, sigma_s = scheduler._sigma_to_alpha_sigma_t(sigma_s)
lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
lambda_s = torch.log(alpha_s) - torch.log(sigma_s)
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
mu_xt = scheduler.dpm_solver_first_order_update(
model_output=model_output, sample=sample, noise=torch.zeros_like(sample)
)
sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h))
if sigma > 0.0:
noise = (prev_latents - mu_xt) / sigma
else:
noise = torch.tensor([0.0]).to(sample.device)
prev_sample = mu_xt + sigma * noise
return noise, prev_sample
def second_order_update(model_output_list, sample): # timestep_list, prev_timestep, sample):
sigma_t, sigma_s0, sigma_s1 = (
scheduler.sigmas[scheduler.step_index + 1],
scheduler.sigmas[scheduler.step_index],
scheduler.sigmas[scheduler.step_index - 1],
)
alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t)
alpha_s0, sigma_s0 = scheduler._sigma_to_alpha_sigma_t(sigma_s0)
alpha_s1, sigma_s1 = scheduler._sigma_to_alpha_sigma_t(sigma_s1)
lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1)
m0, m1 = model_output_list[-1], model_output_list[-2]
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))
if sigma > 0.0:
noise = (prev_latents - mu_xt) / sigma
else:
noise = torch.tensor([0.0]).to(sample.device)
prev_sample = mu_xt + sigma * noise
return noise, prev_sample
if scheduler.step_index is None:
scheduler._init_step_index(timestep)
model_output = scheduler.convert_model_output(model_output=noise_pred, sample=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, latents)
else:
noise, prev_sample = second_order_update(scheduler.model_outputs, latents)
if scheduler.lower_order_nums < scheduler.config.solver_order:
scheduler.lower_order_nums += 1
# upon completion increase step index by one
scheduler._step_index += 1
return noise, prev_sample
# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise
def compute_noise(scheduler, *args):
if isinstance(scheduler, DDIMScheduler):
return compute_noise_ddim(scheduler, *args)
elif (
isinstance(scheduler, DPMSolverMultistepScheduler)
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