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class Blip2QFormerModel(Blip2PreTrainedModel):
"""
Querying Transformer (Q-Former), used in BLIP-2.
""" | 258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
def __init__(self, config: Blip2Config):
super().__init__(config)
self.config = config
self.embeddings = Blip2TextEmbeddings(config.qformer_config)
self.visual_encoder = Blip2VisionModel(config.vision_config)
self.query_tokens = nn.Parameter(torch.zeros(1, config.num_query_tokens... | 258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
self.encoder = Blip2QFormerEncoder(config.qformer_config)
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
... | 258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
Arguments:
attention_mask (`torch.Tensor`):
Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
input_shape (`Tuple[int]`):
The shape of the input to the model.
device (`torch.device`):
The device of the input to ... | 258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
Returns:
`torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.
"""
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
... | 258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# eff... | 258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
def forward(
self,
text_input=None,
image_input=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None... | 258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of:
shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and
value hidden states of the attention... | 258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
text = self.tokenizer(text_input, return_tensors="pt", padding=True)
text = text.to(self.device)
input_ids = text.input_ids
batch_size = input_ids.shape[0]
query_atts = torch.ones((batch_size, self.query_tokens.size()[1]), dtype=torch.long).to(self.device)
attention_mask = torch.... | 258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
embedding_output = self.embeddings(
input_ids=input_ids,
query_embeds=self.query_tokens,
past_key_values_length=past_key_values_length,
)
# embedding_output = self.layernorm(query_embeds)
# embedding_output = self.dropout(embedding_output)
input_shap... | 258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, device)
# If a 2D or 3D attenti... | 258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
if isinstance(encoder_attention_mask, list):
encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
elif encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
e... | 258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_lengt... | 258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
... | 258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
... | 258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
class ContextCLIPTextModel(CLIPPreTrainedModel):
config_class = CLIPTextConfig
_no_split_modules = ["CLIPEncoderLayer"]
def __init__(self, config: CLIPTextConfig):
super().__init__(config)
self.text_model = ContextCLIPTextTransformer(config)
# Initialize weights and apply final pro... | 259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_ctx_clip.py |
def forward(
self,
ctx_embeddings: torch.Tensor = None,
ctx_begin_pos: list = None,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
... | 259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_ctx_clip.py |
class ContextCLIPTextTransformer(nn.Module):
def __init__(self, config: CLIPTextConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = ContextCLIPTextEmbeddings(config)
self.encoder = CLIPEncoder(config)
self.final_layer_norm... | 260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_ctx_clip.py |
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is ... | 260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_ctx_clip.py |
bsz, seq_len = input_shape
if ctx_embeddings is not None:
seq_len += ctx_embeddings.size(1)
# CLIP's text model uses causal mask, prepare it here.
# https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
causal_attention_mask = self._b... | 260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_ctx_clip.py |
last_hidden_state = encoder_outputs[0]
last_hidden_state = self.final_layer_norm(last_hidden_state)
# text_embeds.shape = [batch_size, sequence_length, transformer.width]
# take features from the eot embedding (eot_token is the highest number in each sequence)
# casting to torch.int for... | 260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_ctx_clip.py |
def _build_causal_attention_mask(self, bsz, seq_len, dtype):
# lazily create causal attention mask, with full attention between the vision tokens
# pytorch uses additive attention mask; fill with -inf
mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype)
mask.fill_(torch.tensor(torch.fi... | 260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_ctx_clip.py |
class ContextCLIPTextEmbeddings(nn.Module):
def __init__(self, config: CLIPTextConfig):
super().__init__()
embed_dim = config.hidden_size
self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_di... | 261 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_ctx_clip.py |
seq_length = (input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]) + ctx_len
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
if inputs_embeds is None:
inputs_embeds = self.token_embedding(input_ids)
# for each input e... | 261 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_ctx_clip.py |
position_embeddings = self.position_embedding(position_ids)
embeddings = inputs_embeds + position_embeddings
return embeddings | 261 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_ctx_clip.py |
class LattePipelineOutput(BaseOutput):
frames: torch.Tensor | 262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
class LattePipeline(DiffusionPipeline):
r"""
Pipeline for text-to-video generation using Latte.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.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. Latte uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transforme... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
_optional_components = ["tokenizer", "text_encoder"]
model_cpu_offload_seq = "text_encoder->transformer->vae"
_callback_tensor_inputs = [
"latents",
"prompt_embeds",
"negative_prompt_embeds",
]
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/utils.py
def mask_text_embeddings(self, emb, mask):
if emb.shape[0] == 1:
keep_index = mask.sum().item()
return emb[:, :, :keep_index, :], keep_index # 1, 120, 4096 -> 1 7 4096
else:
... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt
def encode_prompt(
self,
prompt: Union[str, List[str]],
do_classifier_free_guidance: bool = True,
negative_prompt: str = "",
num_images_per_prompt: int = 1,
device: Optional[torch.device] = None... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
negative_prompt (`str` or `List[str]`, *optional*):
The prompt not to guide the video generation. If not defined, one has to pass `negative_prompt_embeds`
instead. Ignored when n... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
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.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. ... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
if device is None:
device = self._execution_device
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]
... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.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_length - 1 : -1])
logger.warning(
"The following part of your... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
if self.text_encoder is not None:
dtype = self.text_encoder.dtype
elif self.transformer is not None:
dtype = self.transformer.dtype
else:
dtype = None
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.s... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens = [negative_prompt] * batch_size if isinstance(negative_prompt, str) else negative_prompt
uncond_tokens = self._text_preprocessing(uncond_tokens, c... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each ge... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# Perform additional masking.
if mask_feature and not embeds_initially_provided:
prompt_embeds = prompt_embeds.unsqueeze(1)
masked_prompt_embeds, keep_indices = self.mask_text_embeddings(prompt_embeds, prompt_embeds_attention_mask)
masked_prompt_embeds = masked_prompt_embeds.... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.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... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
def check_inputs(
self,
prompt,
height,
width,
negative_prompt,
callback_on_step_end_tensor_inputs,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` ha... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.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... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
if prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if negative_prompt is ... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `pr... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
if clean_caption and not is_ftfy_available():
logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
logger.warning("Setting `clean_caption` to False...")
clean_caption = False
if not isinstance(text, (tuple, list)):
text = [text]
... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
def _clean_caption(self, caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub("<person>", "person", caption)
# urls:
... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# 31C0—31EF CJK Strokes
# 31F0—31FF Katakana Phonetic Extensions
# 3200—32FF Enclosed CJK Letters and Months
# 3300—33FF CJK Compatibility
# 3400—4DBF CJK Unified Ideographs Extension A
# 4DC0—4DFF Yijing Hexagram Symbols
# 4E00—9FFF CJK Unified Ideographs
caption... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# все виды тире / all types of dash --> "-"
caption = re.sub(
r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa
"-",
caption,
)
# кавычки к одному стандарту
caption... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# "#123"
caption = re.sub(r"#\d{1,3}\b", "", caption)
# "#12345.."
caption = re.sub(r"#\d{5,}\b", "", caption)
# "123456.."
caption = re.sub(r"\b\d{6,}\b", "", caption)
# filenames:
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
caption = ... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
caption = re.sub(r"^\.\S+$", "", caption)
return caption.strip() | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.prepare_latents
def prepare_latents(
self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
):
shape = (
batch_size,
... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@property
def guidance_scale(self):
return self._guidance_scale
# here `guidance_scale` is defined analog to the guidance weight `w` of e... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
negative_prompt: str = "",
num_inference_steps: int = 50,
timesteps: Optional[List[int]] = None,
guidance_scale: float = 7.5,
num_imag... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
clean_caption: bool = True,
mask_feature: bool = True,
enable_temporal_attentions: bool = True,
decode_chunk_size: Optional[int] = None,
) -> Union[LattePipelineOutput, Tuple]:
"""
Function invoked when calling the pipeline for generation. | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the video generation. If not defined, one has to pass `prompt_embeds`.
instead.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide t... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
guidance_scale (`float`, *optional*, defaults to 7.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance ... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
width (`int`, *optional*, defaults to self.unet.config.sample_size):
The width in pixels of the generated video.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
prompt_embeds (`torch.FloatTensor`, *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.
negative_prompt_embeds (`torch.FloatTensor`, *opt... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
callback_on_step_end (`Callable[[int, int, Dict], None]`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
A callback function or a list of callback functions to be called at the end of each denoising step.
callback_on_step_end_tensor_inputs (`List[str]`, *optional*):
... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
enable_temporal_attentions (`bool`, *optional*, defaults to `True`): Whether to enable temporal attentions
decode_chunk_size (`int`, *optional*):
The number of frames to decode at a time. Higher chunk size leads to better temporal consistency at the
expense of more memory usa... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
Examples:
Returns:
[`~pipelines.latte.pipeline_latte.LattePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.latte.pipeline_latte.LattePipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generate... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# 1. Check inputs. Raise error if not correct
height = height or self.transformer.config.sample_size * self.vae_scale_factor
width = width or self.transformer.config.sample_size * self.vae_scale_factor
self.check_inputs(
prompt,
height,
width,
nega... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.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.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode inpu... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
self._num_timesteps = len(timesteps)
# 5. Prepare latents.
latent_channels = self.transformer.config.in_channels
latents = self.prepare_latents(
... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
if self.interrupt:
continue
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input =... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
current_timestep = t
if not torch.is_tensor(current_timestep):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_m... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
current_timestep = current_timestep.expand(latent_model_input.shape[0]) | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# predict noise model_output
noise_pred = self.transformer(
latent_model_input,
encoder_hidden_states=prompt_embeds,
timestep=current_timestep,
enable_temporal_attentions=enable_temporal_attentions,
retur... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# compute previous video: x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# call the callback, if provided
if callback_on_step_end is not None:
callback_kwargs = {}
for ... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
if XLA_AVAILABLE:
xm.mark_step()
if output_type == "latents":
deprecation_message = (
"Passing `output_type='latents'` is deprecated. Please pass `output_type='latent'` instead."
)
deprecate("output_type_latents", "1.0.0", deprecation_mess... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# Similar to diffusers.pipelines.stable_video_diffusion.pipeline_stable_video_diffusion.decode_latents
def decode_latents(self, latents: torch.Tensor, video_length: int, decode_chunk_size: int = 14):
# [batch, channels, frames, height, width] -> [batch*frames, channels, height, width]
latents = late... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
# decode decode_chunk_size frames at a time to avoid OOM
frames = []
for i in range(0, latents.shape[0], decode_chunk_size):
num_frames_in = latents[i : i + decode_chunk_size].shape[0]
decode_kwargs = {}
if accepts_num_frames:
# we only pass num_frames... | 263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latte/pipeline_latte.py |
class LDMSuperResolutionPipeline(DiffusionPipeline):
r"""
A pipeline for image super-resolution using latent diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular ... | 264 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py |
def __init__(
self,
vqvae: VQModel,
unet: UNet2DModel,
scheduler: Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
... | 264 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py |
Args:
image (`torch.Tensor` or `PIL.Image.Image`):
`Image` or tensor representing an image batch to be used as the starting point for the process.
batch_size (`int`, *optional*, defaults to 1):
Number of images to generate.
num_inference_steps (`int`, ... | 264 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py |
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple. | 264 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py |
Example:
```py
>>> import requests
>>> from PIL import Image
>>> from io import BytesIO
>>> from diffusers import LDMSuperResolutionPipeline
>>> import torch
>>> # load model and scheduler
>>> pipeline = LDMSuperResolutionPipeline.from_pretrained("CompVi... | 264 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py |
>>> # run pipeline in inference (sample random noise and denoise)
>>> upscaled_image = pipeline(low_res_img, num_inference_steps=100, eta=1).images[0]
>>> # save image
>>> upscaled_image.save("ldm_generated_image.png")
```
Returns:
[`~pipelines.ImagePipelineOutput`] ... | 264 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py |
# in_channels should be 6: 3 for latents, 3 for low resolution image
latents_shape = (batch_size, self.unet.config.in_channels // 2, height, width)
latents_dtype = next(self.unet.parameters()).dtype
latents = randn_tensor(latents_shape, generator=generator, device=self.device, dtype=latents_dty... | 264 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py |
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
a... | 264 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py |
for t in self.progress_bar(timesteps_tensor):
# concat latents and low resolution image in the channel dimension.
latents_input = torch.cat([latents, image], dim=1)
latents_input = self.scheduler.scale_model_input(latents_input, t)
# predict the noise residual
... | 264 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py |
class LDMTextToImagePipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using latent diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular devi... | 265 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) model to encode and decode images to and from latent representations.
bert ([`LDMBertModel`]):
Text-encoder model based on [`~transformers.BERT`].
tokenizer ([`~transformers.BertTokenizer`]):
A `BertTokeni... | 265 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
def __init__(
self,
vqvae: Union[VQModel, AutoencoderKL],
bert: PreTrainedModel,
tokenizer: PreTrainedTokenizer,
unet: Union[UNet2DModel, UNet2DConditionModel],
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
):
super().__init__()
... | 265 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 1.0,
eta: Optional[float] = 0.0,
generator: Option... | 265 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *option... | 265 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distrib... | 265 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
Example:
```py
>>> from diffusers import DiffusionPipeline
>>> # load model and scheduler
>>> ldm = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
>>> # run pipeline in inference (sample random noise and denoise)
>>> prompt = "A painting of a squirr... | 265 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_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.
"""
# 0. Default height and width t... | 265 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
# get unconditional embeddings for classifier free guidance
if guidance_scale != 1.0:
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=77, truncation=True, return_tensors="pt"
)
negative_prompt_embeds = self.bert(uncond_input.... | 265 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
# get the initial random noise unless the user supplied it
latents_shape = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {... | 265 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_kwargs = {}
if accepts_eta:
extra_kwargs["eta"] = eta
for t in self.progress_bar(self.s... | 265 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
# predict the noise residual
noise_pred = self.unet(latents_input, t, encoder_hidden_states=context).sample
# perform guidance
if guidance_scale != 1.0:
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + gui... | 265 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
return ImagePipelineOutput(images=image) | 265 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
class LDMBertConfig(PretrainedConfig):
model_type = "ldmbert"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} | 266 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
def __init__(
self,
vocab_size=30522,
max_position_embeddings=77,
encoder_layers=32,
encoder_ffn_dim=5120,
encoder_attention_heads=8,
head_dim=64,
encoder_layerdrop=0.0,
activation_function="gelu",
d_model=1280,
dropout=0.1,
... | 266 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.classifier_dropout = classifier_dropout
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.scale_embedding = scale_embedding # sca... | 266 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
super().__init__(pad_token_id=pad_token_id, **kwargs) | 266 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
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