text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
class Event:
type: str
value: int | 382 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_utils.py |
class Tokenizer:
def __init__(self, regular_ids: int):
# The special tokens: 0=PAD, 1=EOS, and 2=UNK
self._num_special_tokens = 3
self._num_regular_tokens = regular_ids
def encode(self, token_ids):
encoded = []
for token_id in token_ids:
if not 0 <= token_id ... | 383 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_utils.py |
class Codec:
"""Encode and decode events.
Useful for declaring what certain ranges of a vocabulary should be used for. This is intended to be used from
Python before encoding or after decoding with GenericTokenVocabulary. This class is more lightweight and does not
include things like EOS or UNK token ... | 384 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_utils.py |
Args:
max_shift_steps: Maximum number of shift steps that can be encoded.
steps_per_second: Shift steps will be interpreted as having a duration of
1 / steps_per_second.
event_ranges: Other supported event types and their ranges.
"""
self.steps_per_second = st... | 384 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_utils.py |
def is_shift_event_index(self, index: int) -> bool:
return (self._shift_range.min_value <= index) and (index <= self._shift_range.max_value)
@property
def max_shift_steps(self) -> int:
return self._shift_range.max_value
def encode_event(self, event: Event) -> int:
"""Encode an even... | 384 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_utils.py |
def event_type_range(self, event_type: str) -> Tuple[int, int]:
"""Return [min_id, max_id] for an event type."""
offset = 0
for er in self._event_ranges:
if event_type == er.type:
return offset, offset + (er.max_value - er.min_value)
offset += er.max_value... | 384 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_utils.py |
class ProgramGranularity:
# both tokens_map_fn and program_map_fn should be idempotent
tokens_map_fn: Callable[[Sequence[int], Codec], Sequence[int]]
program_map_fn: Callable[[int], int] | 385 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_utils.py |
class MidiProcessor:
def __init__(self):
self.codec = Codec(
max_shift_steps=DEFAULT_MAX_SHIFT_SECONDS * DEFAULT_STEPS_PER_SECOND,
steps_per_second=DEFAULT_STEPS_PER_SECOND,
event_ranges=[
EventRange("pitch", note_seq.MIN_MIDI_PITCH, note_seq.MAX_MIDI_PITC... | 386 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_utils.py |
ns = note_seq.midi_to_note_sequence(midi)
ns_sus = note_seq.apply_sustain_control_changes(ns)
for note in ns_sus.notes:
if not note.is_drum:
note.program = program_to_slakh_program(note.program)
samples = np.zeros(int(ns_sus.total_time * SAMPLE_RATE))
_, fr... | 386 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_utils.py |
events = [
note_representation_processor_chain(event, self.codec, self.note_representation_config) for event in events
]
input_tokens = [self.tokenizer.encode(event["inputs"]) for event in events]
return input_tokens | 386 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_utils.py |
class LearnedClassifierFreeSamplingEmbeddings(ModelMixin, ConfigMixin):
"""
Utility class for storing learned text embeddings for classifier free sampling
"""
@register_to_config
def __init__(self, learnable: bool, hidden_size: Optional[int] = None, length: Optional[int] = None):
super().__... | 387 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
class VQDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using VQ Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
Args:
vqvae ([`VQModel`]):
Vector Quantized Variational Auto-Encoder (VAE) model to encode and decode images to and from latent
representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-base-patch32](https://huggingface.co/openai/... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
def __init__(
self,
vqvae: VQModel,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
transformer: Transformer2DModel,
scheduler: VQDiffusionScheduler,
learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings,
):
super(... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the poo... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
negative_prompt_embeds = self.learned_classifier_free_sampling_embeddings.embeddings
negative_prompt_embeds = negative_prompt_embeds.unsqueeze(0).repeat(batch_size, 1, 1)
... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
# 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.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
num_inference_steps: int = 100,
guidance_scale: float = 5.0,
truncation_rate: float = 1.0,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = ... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expens... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
`truncation_rate` are set to zero.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.htm... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: ... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images.
"""
if isinstance(prompt, str):
... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
latents_shape = (batch_size, self.transformer.num_latent_pixels)
if latents is None:
mask_class = self.transformer.num_vector_embeds - 1
latents = torch.full(latents_shape, mask_class).to(self.device)
else:
if latents.shape != latents_shape:
raise Valu... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the sample if we are doing classifier free guidance
latent_model_input = torch.cat([sample] * 2) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
# compute the previous noisy sample x_t -> x_t-1
sample = self.scheduler.step(model_output, timestep=t, sample=sample, generator=generator).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, sample)
... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
def truncate(self, log_p_x_0: torch.Tensor, truncation_rate: float) -> torch.Tensor:
"""
Truncates `log_p_x_0` such that for each column vector, the total cumulative probability is `truncation_rate`
The lowest probabilities that would increase the cumulative probability above `truncation_rate` a... | 388 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py |
class Pix2PixInversionPipelineOutput(BaseOutput, TextualInversionLoaderMixin):
"""
Output class for Stable Diffusion pipelines.
Args:
latents (`torch.Tensor`)
inverted latents tensor
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of leng... | 389 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
class Pix2PixZeroL2Loss:
def __init__(self):
self.loss = 0.0
def compute_loss(self, predictions, targets):
self.loss += ((predictions - targets) ** 2).sum((1, 2)).mean(0) | 390 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
class Pix2PixZeroAttnProcessor:
"""An attention processor class to store the attention weights.
In Pix2Pix Zero, it happens during computations in the cross-attention blocks."""
def __init__(self, is_pix2pix_zero=False):
self.is_pix2pix_zero = is_pix2pix_zero
if self.is_pix2pix_zero:
... | 391 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.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)
if self.... | 391 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
return hidden_states | 391 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
class StableDiffusionPix2PixZeroPipeline(DiffusionPipeline, StableDiffusionMixin):
r"""
Pipeline for pixel-level image editing using Pix2Pix Zero. Based on Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/tran... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
[`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`], or [`DDPMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model c... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = [
"safety_checker",
"feature_extractor",
"caption_generator",
"caption_processor",
"inverse_scheduler",
]
_exclude_from_cpu_offload = ["safety_checker"]
def __init__(
self,
v... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.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"
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.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... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
caption_processor=caption_processor,
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torc... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
prompt_embeds_tuple = self.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`b... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
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.
lora... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.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)
if prompt is not None and isinstance(prompt, str):
batch_size = 1... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = sel... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.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 | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
if clip_skip is None:
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
prompt_embeds = prompt_embeds[0]
else:
prompt_embeds = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
if self.text_encoder is not None:
prompt_embeds_dtype = self.text_encoder.dtype
elif self.unet is not None:
prompt_embeds_dtype = self.unet.dtype
else:
prompt_embeds_dtype = prompt_embeds.dtype
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, devic... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is no... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
if do_classifier_free_guidance:
# 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=prompt_embeds_dtype, device=device)
negative... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.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):
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.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... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used w... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
def check_inputs(
self,
prompt,
source_embeds,
target_embeds,
callback_steps,
prompt_embeds=None,
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def generate_caption(self, images):
"""Generates caption for a given image."""
text = "a photography of"
pre... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
def construct_direction(self, embs_source: torch.Tensor, embs_target: torch.Tensor):
"""Constructs the edit direction to steer the image generation process semantically."""
return (embs_target.mean(0) - embs_source.mean(0)).unsqueeze(0)
@torch.no_grad()
def get_embeds(self, prompt: List[str], b... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
def prepare_image_latents(self, image, batch_size, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
ima... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
if isinstance(generator, list):
latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
]
latents = torch.cat(latents, dim=0)
else:
latents = self.vae.encode(image).latent_di... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
if batch_size != latents.shape[0]:
if batch_size % latents.shape[0] == 0:
# expand image_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {latents.shape[0]} initial"
" image... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
f"Cannot duplicate `image` of batch size {latents.shape[0]} to {batch_size} text prompts."
)
else:
latents = torch.cat([latents], dim=0) | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
return latents
def get_epsilon(self, model_output: torch.Tensor, sample: torch.Tensor, timestep: int):
pred_type = self.inverse_scheduler.config.prediction_type
alpha_prod_t = self.inverse_scheduler.alphas_cumprod[timestep]
beta_prod_t = 1 - alpha_prod_t
if pred_type == "epsilon":... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
def auto_corr_loss(self, hidden_states, generator=None):
reg_loss = 0.0
for i in range(hidden_states.shape[0]):
for j in range(hidden_states.shape[1]):
noise = hidden_states[i : i + 1, j : j + 1, :, :]
while True:
roll_amount = torch.randin... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
source_embeds: torch.Tensor = None,
target_embeds: torch.Tensor = None,
height: Optional[int] = None,
width: Optional[int] = None,
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
callback_steps: Optional[int] = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
clip_skip: Optional[int] = None,
):
r"""
Function invoked when calling the pipeline for generation. | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
source_embeds (`torch.Tensor`):
Source concept embeddings. Generation of the embeddings ... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as def... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
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.StableDiffusionPipelineOutp... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
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.
Examples: | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# 3. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if cross_attention_kwargs is None:
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negati... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# 5. Generate the inverted noise from the input image or any other image
# generated from the input prompt.
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# 7. Denoising loop where we obtain the cross-attention maps.
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doi... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# 9. Edit the prompt embeddings as per the edit directions discovered.
prompt_embeds_edit = prompt_embeds.clone()
prompt_embeds_edit[1:2] += edit_direction
# 10. Second denoising loop to generate the edited image.
self.scheduler.set_timesteps(num_inference_steps, device=device)
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# we want to learn the latent such that it steers the generation
# process towards the edited direction, so make the make initial
# noise learnable
x_in = latent_model_input.detach().clone()
x_in.requires_grad = True
# optimizer
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# recompute the noise
noise_pred = self.unet(
x_in.detach(),
t,
encoder_hidden_states=prompt_embeds_edit,
cross_attention_kwargs={"timestep": None},
).sample
latents = x_in.detach().chunk(2)[... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if ... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_INVERT_DOC_STRING)
def invert(
self,
prompt: Optional[str] = None,
image: PipelineImageInput = None,
num_inference_steps: int = 50,
guidance_scale: float = 1,
generator: Optional[Union[torch.Generator, List[torch... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
image (`torch.Tensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually ... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
cross_attention_guidance_amount (`float`, defaults to ... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
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 `... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.pipeline_stable_diffusion_pix2pix_zero.Pix2PixInversionPipelineOutput`] or
`tuple`:
[`~pipelines.stable_diffusion.pipeline_stable_diffusion_pix2pix_zero.Pix2PixInversionPipelineOutput`] if
`return_dict` is True, ot... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidan... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# 5. Encode input prompt
num_images_per_prompt = 1
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
prompt_embeds=prompt_embeds,
)
# For classifier f... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# 7. Denoising loop where we obtain the cross-attention maps.
num_warmup_steps = len(timesteps) - num_inference_steps * self.inverse_scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# regularization of the noise prediction
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
grad = var.grad.detach() / num_auto_corr_rolls
noise_pred = noise_pred - lambda_auto_corr * grad
if lambda_kl > 0:
var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True)
# Derive eps... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
# call the callback, if provided
if i == len(timesteps) - 1 or (
(i + 1) > num_warmup_steps and (i + 1) % self.inverse_scheduler.order == 0
):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
... | 392 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py |
class CycleDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin):
r"""
Pipeline for text-guided image to image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implem... | 393 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_cycle_diffusion.py |
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