text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
for location in from_where:
for bs_item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]:
for batch, item in enumerate(bs_item):
if item.shape[1] == num_pixels:
cross_maps = item.reshape(len(prompts), -1, *resolution, item.shape[-... | 105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
def __init__(self, average: bool, batch_size=1, max_resolution=16, max_size: int = None):
self.step_store = self.get_empty_store()
self.attention_store = []
self.cur_step = 0
self.average = average
self.batch_size = batch_size
if max_size is None:
self.max_siz... | 105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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... | 106 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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)) | 106 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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... | 107 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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... | 107 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
class LEditsPPPipelineStableDiffusion(
DiffusionPipeline, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin
):
"""
Pipeline for textual image editing using LEDits++ with Stable Diffusion.
This model inherits from [`DiffusionPipeline`] and builds on the [`S... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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 uses the text portion of
[CLIP](https://huggingfac... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
[`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]. If any other scheduler is passed it will
automatically be set to [`DPMSolverMultistepScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered off... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
model_cpu_offload_seq = "text_encoder->unet->vae"
_exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
def __init__(
self,
vae: Auto... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if not isinstance(scheduler, DDIMScheduler) and not isinstance(scheduler, DPMSolverMultistepScheduler):
scheduler = DPMSolverMultistepScheduler.from_config(
scheduler.config, algorithm_type="sde-dpmsolver++", solver_order=2
)
logger.warning(
"This pipe... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if scheduler is not None and getattr(scheduler.config, "clip_sample", False) is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` i... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
is_unet_version_less_0_9_0 = (
unet is not None
and hasattr(unet.config, "_diffusers_version")
and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
)
is_unet_sample_size_less_64 = (
unet is not None an... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
d... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
negative_prompt=None,
editing_prompt_embeddings=None,
negative_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if editing_prompt_embeddings is not None and negative_prompt_embeds is not None:
if editing_prompt_embeddings.shape != negative_prompt_embeds.shape:
raise ValueError(
"`editing_prompt_embeddings` and `negative_prompt_embeds` must have the same shape when passed directly, ... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def prepare_unet(self, attention_store, PnP: bool = False):
attn_procs = {}
for name in self.unet.at... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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,
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
Args:
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
enable_edit_guidance (`bool`):
whether to perform any editing or reconstruct the input image instead
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
provided, text embeddings will be generated from `prompt` input argument.
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 wil... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if lora_scale is not None and isinstance(self, StableDiffusionLoraLoaderMixin):
self._lora_scale = lora_scale | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
batch_size = self.batch_size
num_edit_tokens = None | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and ... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
if enable_edit_guidance:
if editing_prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
# if isinstance(self, TextualInversionLoaderMixin):
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
num_edit_tokens = text_inputs.length - 2 # not counting startoftext and endoftext
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(
[x for item in editing_prompt for x in repeat(item, batch_size)],
padding="longest",
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if (
hasattr(self.text_encoder.config, "use_attention_mask")
and self.text_encoder.config.use_attention_mask
):
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if clip_skip is None:
editing_prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
editing_prompt_embeds = editing_prompt_embeds[0]
else:
editing_prompt_embeds = self.text_encoder(
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# obtaining the final prompt representations passes through the LayerNorm
# layer.
editing_prompt_embeds = self.text_encoder.text_model.final_layer_norm(editing_prompt_embeds) | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
editing_prompt_embeds = editing_prompt_embeds.to(dtype=negative_prompt_embeds.dtype, device=device)
bs_embed_edit, seq_len, _ = editing_prompt_embeds.shape
editing_prompt_embeds = editing_prompt_embeds.to(dtype=negative_prompt_embeds.dtype, device=device)
editing_prompt_embeds = edi... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale b... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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()
def ... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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() | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
negative_prompt: Optional[Union[str, List[str]]] = None,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = ... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
use_intersect_mask: bool = True,
attn_store_steps: Optional[List[int]] = [],
store_averaged_over_steps: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guidance_rescale: float = 0.0,
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[Cal... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
Args:
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
generator (`torch.Generator`, *optional*):
One ... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
The prompt or prompts to guide the image generation. The image is reconstructed by setting
`editing_prompt = None`. Guidance direction of prompt should be specified via
`reverse_editing_direction`.
editing_prompt_embeds (`torch.Tensor>`, *optional*):
Pre-compu... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
'edit_threshold' is defined as 'λ' of equation 12 of [LEDITS++
Paper](https://arxiv.org/abs/2301.12247).
user_mask (`torch.Tensor`, *optional*):
User-provided mask for even better control over the editing process. This is helpful when LEDITS++'s
implicit masks... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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/231... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
guidance_rescale (`float`, *optional*, defaults to 0.0):
Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
Flawed](https://arx... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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 specifie... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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, and t... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if use_intersect_mask:
use_cross_attn_mask = True
if use_cross_attn_mask:
self.smoothing = LeditsGaussianSmoothing(self.device)
if user_mask is not None:
user_mask = user_mask.to(self.device)
org_prompt = ""
# 1. Check inputs. Raise error if not co... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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_embeds is not None:
enable_edit_guidance = True
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
edit_concepts, uncond_embeddings, num_edit_tokens = self.encode_prompt(
editing_prompt=editing_prompt,
device=self.device,
num_images_per_prompt=num_images_per_prompt,
enable_edit_guidance=enable_edit_guidance,
negative_prompt=negative_prompt,
edit... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# 4. Prepare timesteps
# self.scheduler.set_timesteps(num_inference_steps, device=self.device)
timesteps = self.inversion_steps
t_to_idx = {int(v): k for k, v in enumerate(timesteps[-zs.shape[0] :])}
if use_cross_attn_mask:
self.attention_store = LeditsAttentionStore(
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
None,
None,
text_embeddings.dtype,
self.device,
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
text_embed_input = text_embeddings
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embed_input).sample
noise_pred_out = no... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
elif enable_edit_guidance:
if self.activation_mask is None:
self.activation_mask = torch.zeros(
(len(timesteps), len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape)
)
if self.sem_guidance ... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if isinstance(edit_guidance_scale, list):
edit_guidance_scale_c = edit_guidance_scale[c]
else:
edit_guidance_scale_c = edit_guidance_scale
if isinstance(edit_threshold, list):
edit_thresh... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
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_... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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,
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
attn_map = torch.sum(attn_map, dim=3)
# gaussian_smoothing
attn_map = F.pad(attn_map.unsqueeze(1), (1, 1, 1, 1), mode="reflect")
attn_map = self.smoothing(attn_map).squeeze(1)
# torch.quantile function expe... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
if use_intersect_mask:
if t <= 800:
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,... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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),
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
noise_pred = noise_pred_uncond + noise_guidance_edit
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... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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)
latents = ... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# 8. Post-processing
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
0
]
image, has_nsfw_concept = self.run_safety_checker(image, self.device, text_embeddings.dtype)
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
@torch.no_grad()
def invert(
self,
image: PipelineImageInput,
source_prompt: str = "",
source_guidance_scale: float = 3.5,
num_inversion_steps: int = 30,
skip: float = 0.15,
generator: Optional[torch.Generator] = None,
cross_attention_kwargs: Optional[... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make inversion
deterministic.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
The width in preprocessed. If `None`, will use get_default_height_width()` to get the default width.
resize_mode (`str`, *optional*, defaults to `default`):
The resize mode, can be one of `default` or `fill`. If `default`, will resize the image to fit within
the specified wid... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
crops_coords (`List[Tuple[int, int, int, int]]`, *optional*, defaults to `None`):
The crop coordinates for each image in the batch. If `None`, will not crop the image. | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# 1. encode image
x0, resized = self.encode_image(
image,
dtype=self.text_encoder.dtype,
height=height,
width=width,
resize_mode=resize_mode,
crops_coords=crops_coords,
)
self.batch_size = x0.shape[0]
# autoencoder ... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
uncond_embedding, text_embeddings, _ = self.encode_prompt(
num_images_per_prompt=1,
device=self.device,
negative_prompt=None,
enable_edit_guidance=do_classifier_free_guidance,
editing_prompt=source_prompt,
lora_scale=lora_scale,
clip_sk... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
self.scheduler.set_timesteps(len(self.scheduler.timesteps))
# noise maps
zs = torch.zeros(size=variance_noise_shape, device=self.device, dtype=uncond_embedding.dtype)
with self.progress_bar(total=len(timesteps)) as progress_bar:
for t in timesteps:
idx = num_inversio... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
# correction to avoid error accumulation
xts[idx] = xtm1_corrected
progress_bar.update()
if XLA_AVAILABLE:
xm.mark_step()
self.init_latents = xts[-1].expand(self.batch_size, -1, -1, -1)
zs = zs.flip(0)
self.zs = zs
r... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
@torch.no_grad()
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 = im... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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... | 108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py |
class CogVideoXPipelineOutput(BaseOutput):
r"""
Output class for CogVideo pipelines.
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
denoised PI... | 109 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_output.py |
class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
r"""
Pipeline for text-to-video generation using CogVideoX.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading ... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
text_encoder ([`T5EncoderModel`]):
Frozen text-encoder. CogVideoX uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transf... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
_optional_components = []
model_cpu_offload_seq = "text_encoder->transformer->vae"
_callback_tensor_inputs = [
"latents",
"prompt_embeds",
"negative_prompt_embeds",
]
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
vae: ... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
self.register_modules(
tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
)
self.vae_scale_factor_spatial = (
2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
)
self.vae_scal... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
def _get_t5_prompt_embeds(
self,
prompt: Union[str, List[str]] = None,
num_videos_per_prompt: int = 1,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
):
device = device or self._execution_device
... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because `max... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
def encode_prompt(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
do_classifier_free_guidance: bool = True,
num_videos_per_prompt: int = 1,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optiona... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
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. Ign... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
provided, text embeddings will be generated from `prompt` input argument.
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 wil... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
prompt = [prompt] if isinstance(prompt, str) else prompt
if prompt is not None:
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
prompt_embeds = self._get_t5_prompt_embeds(
prompt=prompt,
... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
if prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif batch_size != len(negative_pro... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
def prepare_latents(
self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
):
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(gener... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def decode_latents(self, latents: torch.Tensor) -> torch.Tensor:
latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
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)... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in cal... | 110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py |
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