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Description
Describe the bug
in pipeline_flux.py
The docstring says prompt_2 is optional but it is not declared as Optional[] and has no default None value
Minor issue, could be in other pipelines too
def encode_prompt(
self,
prompt: Union[str, List[str]],
prompt_2: Union[str, List[str]],
device: Optional[torch.device] = None,
num_images_per_prompt: int = 1,
prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
max_sequence_length: int = 512,
lora_scale: Optional[float] = None,
):
r"""
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
used in all text-encoders
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
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.
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided, pooled text embeddings will be generated from `prompt` input argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
device = device or self._execution_device
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, FluxLoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is not None and USE_PEFT_BACKEND:
scale_lora_layers(self.text_encoder, lora_scale)
if self.text_encoder_2 is not None and USE_PEFT_BACKEND:
scale_lora_layers(self.text_encoder_2, lora_scale)
prompt = [prompt] if isinstance(prompt, str) else prompt
if prompt_embeds is None:
prompt_2 = prompt_2 or prompt
prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
# We only use the pooled prompt output from the CLIPTextModel
pooled_prompt_embeds = self._get_clip_prompt_embeds(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
)
prompt_embeds = self._get_t5_prompt_embeds(
prompt=prompt_2,
num_images_per_prompt=num_images_per_prompt,
max_sequence_length=max_sequence_length,
device=device,
)
if self.text_encoder is not None:
if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder, lora_scale)
if self.text_encoder_2 is not None:
if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder_2, lora_scale)
dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype
text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
return prompt_embeds, pooled_prompt_embeds, text_ids
Reproduction
Minor issue, quick fix
Logs
System Info
Current