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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], indexing="ij")
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) | 100 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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)) | 100 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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) | 101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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 | 101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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`] | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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`]): | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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"`): | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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.
""" | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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",
] | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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__() | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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) if getattr(self, "vae", None) else 8
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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
if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
else 128
)
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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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.Tensor] = None,
negative_pooled_prompt_embeds: Optional[torch.Tensor] = 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.Tensor] = None,
editing_pooled_prompt_embeds: Optional[torch.Tensor] = None,
) -> object:
r"""
Encodes the prompt into text encoder hidden states. | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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.Tensor`, *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. | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
negative_pooled_prompt_embeds (`torch.Tensor`, *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*): | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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.Tensor`, *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.Tensor`, *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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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] | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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
) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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,
) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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."
) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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,
),
)
# 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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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.Tensor:
"""
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. | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
Returns:
`torch.Tensor`: 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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing() | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
def enable_vae_tiling(self):
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.
"""
self.vae.enable_tiling()
def disable_vae_tiling(self):
r"""
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_tiling() | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
@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.Tensor] = None,
negative_pooled_prompt_embeds: Optional[torch.Tensor] = 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, | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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.Tensor] = 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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
[`~pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.invert`] method has to be called beforehand. Edits
will always be performed for the last inverted image(s). | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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*): | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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.Tensor`, *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.Tensor`, *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. | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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.Tensor)`.
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*): | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
`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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
`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. | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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++ | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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: | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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*): | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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. | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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,
) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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,
) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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] | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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, | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
)[0] | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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)) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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
) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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
) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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() | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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() | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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() | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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
)
height, width = image.shape[-2:]
if height % 32 != 0 or width % 32 != 0:
raise ValueError(
"Image height and width must be a factor of 32. "
"Consider down-sampling the input using the `height` and `width` parameters"
)
resized = self.image_processor.postprocess(image=image, output_type="pil") | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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()
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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
@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,
height: Optional[int] = None,
width: Optional[int] = None,
resize_mode: Optional[str] = "default",
crops_coords: Optional[Tuple[int, int, int, int]] = 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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
inversion proposed by [edit-friendly DPDM](https://arxiv.org/abs/2304.06140) will be performed instead. | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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*): | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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)): | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
`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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
Returns:
[`~pipelines.ledits_pp.LEditsPPInversionPipelineOutput`]: Output will contain the resized input image(s)
and respective VAE reconstruction(s).
"""
if height is not None and height % 32 != 0 or width is not None and width % 32 != 0:
raise ValueError("height and width must be a factor of 32.")
# 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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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,
height=height,
width=width,
resize_mode=resize_mode,
crops_coords=crops_coords,
)
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
) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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 | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
# 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] | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
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) | 102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py |
class LEditsPPDiffusionPipelineOutput(BaseOutput):
"""
Output class for LEdits++ Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or
`None` if safety checking could not be performed.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
nsfw_content_detected: Optional[List[bool]] | 103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_output.py |
class LEditsPPInversionPipelineOutput(BaseOutput):
"""
Output class for LEdits++ Diffusion pipelines.
Args:
input_images (`List[PIL.Image.Image]` or `np.ndarray`)
List of the cropped and resized input images as PIL images of length `batch_size` or NumPy array of shape `
(batch_size, height, width, num_channels)`.
vae_reconstruction_images (`List[PIL.Image.Image]` or `np.ndarray`)
List of VAE reconstruction of all input images as PIL images of length `batch_size` or NumPy array of shape
` (batch_size, height, width, num_channels)`.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
vae_reconstruction_images: Union[List[PIL.Image.Image], np.ndarray] | 104 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_output.py |
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) | 105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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() | 105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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] | 105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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