text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
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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 kern... | 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... | 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.att... | 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... | 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://hugging... | 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/mode... | 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
... | 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_embe... | 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]... | 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_extract... | 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... | 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: Op... | 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... | 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 argum... | 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 ... | 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... | 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... | 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"
... | 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(
... | 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... | 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,
... | 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_... | 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]
... | 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_emb... | 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:
... | 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 u... | 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 forw... | 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... | 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(
... | 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.attentio... | 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:
""... | 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=dty... | 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... | 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 deco... | 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"):
... | 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[... | 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, L... | 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 subs... | 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 t... | 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... | 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 (`fl... | 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).
... | 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.
... | 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... | 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:
Wheth... | 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.
... | 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 sp... | 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.
... | 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,
# ... | 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
... | 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,
... | 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=st... | 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_embe... | 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([... | 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])
... | 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
... | 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(
... | 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... | 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,
de... | 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:
... | 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_... | 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
... | 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,
... | 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]}!"
... | 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(
... | 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.act... | 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
)
... | 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_thre... | 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),
... | 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... | 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_thre... | 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_edi... | 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(
... | 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_... | 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(... | 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, ima... | 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 usin... | 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... | 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 guida... | 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 ... | 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 ... | 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 an... | 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 > ... | 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=d... | 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_en... | 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, re... | 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 = rand... | 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... | 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_ste... | 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)`... | 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 `
(bat... | 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 * h... | 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(le... | 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]
... | 105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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