text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
if not output_type == "latent":
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return KolorsPipelineOutput(images=image) | 52 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors.py |
class KolorsPipelineOutput(BaseOutput):
"""
Output class for Kolors pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy a... | 53 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_output.py |
class ChatGLMConfig(PretrainedConfig):
model_type = "chatglm" | 54 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
def __init__(
self,
num_layers=28,
padded_vocab_size=65024,
hidden_size=4096,
ffn_hidden_size=13696,
kv_channels=128,
num_attention_heads=32,
seq_length=2048,
hidden_dropout=0.0,
classifier_dropout=None,
attention_dropout=0.0,
... | 54 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
self.hidden_size = hidden_size
self.ffn_hidden_size = ffn_hidden_size
self.kv_channels = kv_channels
self.num_attention_heads = num_attention_heads
self.seq_length = seq_length
self.hidden_dropout = hidden_dropout
self.classifier_dropout = classifier_dropout
self.... | 54 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
self.fp32_residual_connection = fp32_residual_connection
self.quantization_bit = quantization_bit
self.pre_seq_len = pre_seq_len
self.prefix_projection = prefix_projection
super().__init__(**kwargs) | 54 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
class RMSNorm(torch.nn.Module):
def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs):
super().__init__()
self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype))
self.eps = eps
def forward(self, hidden_states: torch.Tensor)... | 55 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
class CoreAttention(torch.nn.Module):
def __init__(self, config: ChatGLMConfig, layer_number):
super(CoreAttention, self).__init__()
self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling
self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
if self.ap... | 56 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
coeff = None
self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
if self.apply_query_key_layer_scaling:
coeff = self.layer_number
self.norm_factor *= coeff
self.coeff = coeff
self.attention_dropout = torch.nn.Dropout(config.attention_dropout) | 56 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
def forward(self, query_layer, key_layer, value_layer, attention_mask):
pytorch_major_version = int(torch.__version__.split(".")[0])
if pytorch_major_version >= 2:
query_layer, key_layer, value_layer = [
k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]
... | 56 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
context_layer = context_layer.reshape(*new_context_layer_shape)
else:
# Raw attention scores | 56 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
# [b, np, sq, sk]
output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0))
# [sq, b, np, hn] -> [sq, b * np, hn]
query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1)
# [sk, b, np, hn] -> [sk, b * np, ... | 56 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
# Raw attention scores. [b * np, sq, sk]
matmul_result = torch.baddbmm(
matmul_input_buffer,
query_layer.transpose(0, 1), # [b * np, sq, hn]
key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk]
beta=0.0,
alpha=(1.0 / ... | 56 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
# attention scores and attention mask [b, np, sq, sk]
if self.attention_softmax_in_fp32:
attention_scores = attention_scores.float()
if self.coeff is not None:
attention_scores = attention_scores * self.coeff
if attention_mask is None and attention_sco... | 56 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.attention_dropout(attention_probs)
# =========================
# Context layer. [sq, b, hp]
#... | 56 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
# context layer shape: [b, np, sq, hn]
output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3))
# change view [sk, b * np, hn]
value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1)
# change view ... | 56 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
return context_layer | 56 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
class SelfAttention(torch.nn.Module):
"""Parallel self-attention layer abstract class.
Self-attention layer takes input with size [s, b, h] and returns output of the same size.
"""
def __init__(self, config: ChatGLMConfig, layer_number, device=None):
super(SelfAttention, self).__init__()
... | 57 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
self.multi_query_attention = config.multi_query_attention
self.qkv_hidden_size = 3 * self.projection_size
if self.multi_query_attention:
self.num_multi_query_groups_per_partition = config.multi_query_group_num
self.qkv_hidden_size = (
self.projection_size + 2 * se... | 57 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None):
if self.multi_query_attention:
num_attention_heads = self.num_multi_query_groups_per_partition
else:
num_attention_heads = self.num_attention_heads_per_partition
return torch.empt... | 57 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
# Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)]
mixed_x_layer = self.query_key_value(hidden_states) | 57 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
if self.multi_query_attention:
(query_layer, key_layer, value_layer) = mixed_x_layer.split(
[
self.num_attention_heads_per_partition * self.hidden_size_per_attention_head,
self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,... | 57 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
+ (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
)
else:
new_tensor_shape = mixed_x_layer.size()[:-1] + (
self.num_attention_heads_per_partition,
3 * self.hidden_size_per_attention_head,
)
mixed_x_l... | 57 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
# [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn]
(query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
# apply relative positional encoding (rotary embedding)
if rotary_pos_emb is not None:
query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb... | 57 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
if self.multi_query_attention:
key_layer = key_layer.unsqueeze(-2)
key_layer = key_layer.expand(
-1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1
)
key_layer = key_layer.contiguous().view(
... | 57 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask)
# =================
# Output. [sq, b, h]
# =================
output = self.dense(context_layer)
return output, kv_cache | 57 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
class MLP(torch.nn.Module):
"""MLP.
MLP will take the input with h hidden state, project it to 4*h hidden dimension, perform nonlinear transformation,
and project the state back into h hidden dimension.
"""
def __init__(self, config: ChatGLMConfig, device=None):
super(MLP, self).__init__()... | 58 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
# Project back to h.
self.dense_4h_to_h = nn.Linear(
config.ffn_hidden_size, config.hidden_size, bias=self.add_bias, device=device, **_config_to_kwargs(config)
)
def forward(self, hidden_states):
# [s, b, 4hp]
intermediate_parallel = self.dense_h_to_4h(hidden_states)
... | 58 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
class GLMBlock(torch.nn.Module):
"""A single transformer layer.
Transformer layer takes input with size [s, b, h] and returns an output of the same size.
"""
def __init__(self, config: ChatGLMConfig, layer_number, device=None):
super(GLMBlock, self).__init__()
self.layer_number = layer... | 59 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
# Layernorm on the attention output
self.post_attention_layernorm = LayerNormFunc(
config.hidden_size, eps=config.layernorm_epsilon, device=device, dtype=config.torch_dtype
)
# MLP
self.mlp = MLP(config, device=device)
def forward(
self,
hidden_states,
... | 59 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training)
layernorm_input = residual + layernorm_input
# Layer norm post the self attention.
layernorm_output = self.post_attention_layernorm(layernorm_input)
# MLP.
mlp_output ... | 59 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
class GLMTransformer(torch.nn.Module):
"""Transformer class."""
def __init__(self, config: ChatGLMConfig, device=None):
super(GLMTransformer, self).__init__()
self.fp32_residual_connection = config.fp32_residual_connection
self.post_layer_norm = config.post_layer_norm
# Number... | 60 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
def _get_layer(self, layer_number):
return self.layers[layer_number]
def forward(
self,
hidden_states,
attention_mask,
rotary_pos_emb,
kv_caches=None,
use_cache: Optional[bool] = True,
output_hidden_states: Optional[bool] = False,
):
if no... | 60 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
layer = self._get_layer(index)
if torch.is_grad_enabled() and self.gradient_checkpointing:
layer_ret = torch.utils.checkpoint.checkpoint(
layer, hidden_states, attention_mask, rotary_pos_emb, kv_caches[index], use_cache
)
else:
... | 60 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
class ChatGLMPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
is_parallelizable = False
supports_gradient_checkpointing = True
config_class = ChatGLMConfig
base_model_prefix... | 61 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
def get_masks(self, input_ids, past_key_values, padding_mask=None):
batch_size, seq_length = input_ids.shape
full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device)
full_attention_mask.tril_()
past_length = 0
if past_key_values:
p... | 61 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
def get_position_ids(self, input_ids, device):
batch_size, seq_length = input_ids.shape
position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
return position_ids
def _set_gradient_checkpointing(self, module, value=False):
if isin... | 61 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
class Embedding(torch.nn.Module):
"""Language model embeddings."""
def __init__(self, config: ChatGLMConfig, device=None):
super(Embedding, self).__init__()
self.hidden_size = config.hidden_size
# Word embeddings (parallel).
self.word_embeddings = nn.Embedding(
conf... | 62 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
class RotaryEmbedding(nn.Module):
def __init__(self, dim, original_impl=False, device=None, dtype=None):
super().__init__()
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim))
self.register_buffer("inv_freq", inv_freq)
self.dim = dim
sel... | 63 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
# Create position indexes `[0, 1, ..., seq_len - 1]`
seq_idx = torch.arange(seq_len, dtype=torch.float, device=device)
# Calculate the product of position index and $\theta_i$
idx_theta = torch.outer(seq_idx, theta).float()
cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta... | 63 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
class PrefixEncoder(torch.nn.Module):
"""
The torch.nn model to encode the prefix Input shape: (batch-size, prefix-length) Output shape: (batch-size,
prefix-length, 2*layers*hidden)
"""
def __init__(self, config: ChatGLMConfig):
super().__init__()
self.prefix_projection = config.pre... | 64 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
def forward(self, prefix: torch.Tensor):
if self.prefix_projection:
prefix_tokens = self.embedding(prefix)
past_key_values = self.trans(prefix_tokens)
else:
past_key_values = self.embedding(prefix)
return past_key_values | 64 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
class ChatGLMModel(ChatGLMPreTrainedModel):
def __init__(self, config: ChatGLMConfig, device=None, empty_init=True):
super().__init__(config)
if empty_init:
init_method = skip_init
else:
init_method = default_init
init_kwargs = {}
if device is not None... | 65 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
self.rotary_pos_emb = RotaryEmbedding(
rotary_dim // 2, original_impl=config.original_rope, device=device, dtype=config.torch_dtype
)
self.encoder = init_method(GLMTransformer, config, **init_kwargs)
self.output_layer = init_method(
nn.Linear,
config.hidden_si... | 65 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
def get_prompt(self, batch_size, device, dtype=torch.half):
prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device)
past_key_values = self.prefix_encoder(prefix_tokens).type(dtype)
past_key_values = past_key_values.view(
batch_size, self.pre_seq_len, self.nu... | 65 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
def forward(
self,
input_ids,
position_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.BoolTensor] = None,
full_attention_mask: Optional[torch.BoolTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
i... | 65 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
if self.pre_seq_len is not None:
if past_key_values is None:
past_key_values = self.get_prompt(
batch_size=batch_size, device=input_ids.device, dtype=inputs_embeds.dtype
)
if attention_mask is not None:
attention_mask = torch.ca... | 65 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
# Rotary positional embeddings
rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
if position_ids is not None:
rotary_pos_emb = rotary_pos_emb[position_ids]
else:
rotary_pos_emb = rotary_pos_emb[None, :seq_length]
rotary_pos_emb = rotary_pos_emb.transpose(0, 1)... | 65 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
) | 65 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py |
class SPTokenizer:
def __init__(self, model_path: str):
# reload tokenizer
assert os.path.isfile(model_path), model_path
self.sp_model = SentencePieceProcessor(model_file=model_path)
# BOS / EOS token IDs
self.n_words: int = self.sp_model.vocab_size()
self.bos_id: in... | 66 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"]
special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens
self.special_tokens = {}
self.index_special_tokens = {}
for token in special_tokens:
self.special_tokens[... | 66 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
def tokenize(self, s: str, encode_special_tokens=False):
if encode_special_tokens:
last_index = 0
t = []
for match in re.finditer(self.role_special_token_expression, s):
if last_index < match.start():
t.extend(self.sp_model.EncodeAsPieces(s... | 66 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
def decode(self, t: List[int]) -> str:
text, buffer = "", []
for token in t:
if token in self.index_special_tokens:
if buffer:
text += self.sp_model.decode(buffer)
buffer = []
text += self.index_special_tokens[token]
... | 66 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
def convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
if index in self.index_special_tokens:
return self.index_special_tokens[index]
if index in [self.eos_id, self.bos_id, self.pad_id] or index < 0:
return ""
ret... | 66 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
class ChatGLMTokenizer(PreTrainedTokenizer):
vocab_files_names = {"vocab_file": "tokenizer.model"}
model_input_names = ["input_ids", "attention_mask", "position_ids"]
def __init__(
self,
vocab_file,
padding_side="left",
clean_up_tokenization_spaces=False,
encode_spe... | 67 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
def get_command(self, token):
if token in self.special_tokens:
return self.special_tokens[token]
assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}"
return self.tokenizer.special_tokens[token]
@property
def unk_token(self) -> str:... | 67 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
def get_vocab(self):
"""Returns vocab as a dict"""
vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, text, **kwargs):
return self.tokenizer.tokenize(text, encode_special_tokens=s... | 67 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
Args:
save_directory (`str`):
The directory in which to save the vocabulary.
filename_prefix (`str`, *optional*):
An optional prefix to add to the named of the saved files.
Returns:
`Tuple(str)`: Paths to the files saved.
"""
i... | 67 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
def build_single_message(self, role, metadata, message):
assert role in ["system", "user", "assistant", "observation"], role
role_tokens = [self.get_command(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n")
message_tokens = self.tokenizer.encode(message)
tokens = role_tokens + mes... | 67 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
def build_chat_input(self, query, history=None, role="user"):
if history is None:
history = []
input_ids = []
for item in history:
content = item["content"]
if item["role"] == "system" and "tools" in item:
content = content + "\n" + json.dumps(... | 67 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has t... | 67 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
prefix_tokens = self.get_prefix_tokens()
token_ids_0 = prefix_tokens + token_ids_0
if token_ids_1 is not None:
token_ids_0 = token_ids_0 + token_ids_1 + [self... | 67 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
Args:
encoded_inputs:
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.... | 67 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware... | 67 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(required_input)
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
needs_to_be_padde... | 67 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
if "attention_mask" in encoded_inputs:
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
if "position_ids" in encoded_inputs:
encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
encoded_inputs[sel... | 67 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py |
class AttentionStore:
@staticmethod
def get_empty_store():
return {"down": [], "mid": [], "up": []}
def __call__(self, attn, is_cross: bool, place_in_unet: str):
if self.cur_att_layer >= 0 and is_cross:
if attn.shape[1] == np.prod(self.attn_res):
self.step_store[... | 68 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
def aggregate_attention(self, from_where: List[str]) -> torch.Tensor:
"""Aggregates the attention across the different layers and heads at the specified resolution."""
out = []
attention_maps = self.get_average_attention()
for location in from_where:
for item in attention_map... | 68 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
def __init__(self, attn_res):
"""
Initialize an empty AttentionStore :param step_index: used to visualize only a specific step in the diffusion
process
"""
self.num_att_layers = -1
self.cur_att_layer = 0
self.step_store = self.get_empty_store()
self.attent... | 68 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
class AttendExciteAttnProcessor:
def __init__(self, attnstore, place_in_unet):
super().__init__()
self.attnstore = attnstore
self.place_in_unet = place_in_unet
def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
batch_size, sequen... | 69 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
# only need to store attention maps during the Attend and Excite process
if attention_probs.requires_grad:
self.attnstore(attention_probs, is_cross, self.place_in_unet)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
#... | 69 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin):
r"""
Pipeline for text-to-image generation using Stable Diffusion and Attend-and-Excite.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic met... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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 ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14))... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
more details about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as input... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor"]
_exclude_from_cpu_offload = ["safety_checker"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2D... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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"
... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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,
... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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.... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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 | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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_... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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(
... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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):
... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
def check_inputs(
self,
prompt,
indices,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` ... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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:
... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
if indices_is_list_ints:
indices_batch_size = 1
elif indices_is_list_list_ints:
indices_batch_size = len(indices)
if prompt is not None and isinstance(prompt, str):
prompt_batch_size = 1
elif prompt is not None and isinstance(prompt, list):
prompt... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.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,
... | 70 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py |
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