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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from transformers import Qwen2Tokenizer, Qwen3Model, T5TokenizerFast
from ...configuration_utils import FrozenDict
from ...guiders import ClassifierFreeGuidance
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import AnimaModularPipeline
class AnimaTextEncoderStep(ModularPipelineBlocks):
model_name = "anima"
@property
def description(self) -> str:
return "Text encoder step that encodes Anima prompts into Qwen states and T5 token ids."
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("text_encoder", Qwen3Model),
ComponentSpec("tokenizer", Qwen2Tokenizer),
ComponentSpec("t5_tokenizer", T5TokenizerFast),
ComponentSpec(
"guider",
ClassifierFreeGuidance,
config=FrozenDict({"guidance_scale": 4.0}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam.template("prompt"),
InputParam.template("negative_prompt"),
InputParam.template("max_sequence_length"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"qwen_prompt_embeds",
type_hint=torch.Tensor,
description="Qwen prompt embeddings to be consumed by the Anima text conditioner.",
),
OutputParam(
"qwen_attention_mask",
type_hint=torch.Tensor,
description="Qwen prompt attention mask to be consumed by the Anima text conditioner.",
),
OutputParam(
"t5_input_ids",
type_hint=torch.Tensor,
description="T5 prompt token ids to be consumed by the Anima text conditioner.",
),
OutputParam(
"t5_attention_mask",
type_hint=torch.Tensor,
description="T5 prompt attention mask to be consumed by the Anima text conditioner.",
),
OutputParam(
"negative_qwen_prompt_embeds",
type_hint=torch.Tensor,
description="Negative Qwen prompt embeddings to be consumed by the Anima text conditioner.",
),
OutputParam(
"negative_qwen_attention_mask",
type_hint=torch.Tensor,
description="Negative Qwen prompt attention mask to be consumed by the Anima text conditioner.",
),
OutputParam(
"negative_t5_input_ids",
type_hint=torch.Tensor,
description="Negative T5 prompt token ids to be consumed by the Anima text conditioner.",
),
OutputParam(
"negative_t5_attention_mask",
type_hint=torch.Tensor,
description="Negative T5 prompt attention mask to be consumed by the Anima text conditioner.",
),
]
@staticmethod
def check_inputs(block_state):
if not isinstance(block_state.prompt, str) and not isinstance(block_state.prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(block_state.prompt)}")
if block_state.max_sequence_length is not None and block_state.max_sequence_length > 4096:
raise ValueError(
f"`max_sequence_length` cannot be greater than 4096 but is {block_state.max_sequence_length}"
)
@staticmethod
def _get_qwen_prompt_embeds(
components: AnimaModularPipeline,
prompt: str | list[str],
max_sequence_length: int,
device: torch.device,
dtype: torch.dtype,
) -> tuple[torch.Tensor, torch.Tensor]:
prompt = [prompt] if isinstance(prompt, str) else prompt
text_inputs = components.tokenizer(
prompt,
padding="longest",
max_length=max_sequence_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids.to(device)
prompt_attention_mask = text_inputs.attention_mask.to(device)
if text_input_ids.shape[-1] == 0:
text_input_ids = text_input_ids.new_zeros((text_input_ids.shape[0], 1))
prompt_attention_mask = prompt_attention_mask.new_zeros((prompt_attention_mask.shape[0], 1))
prompt_embeds = components.text_encoder(
input_ids=text_input_ids,
attention_mask=prompt_attention_mask,
output_hidden_states=False,
).last_hidden_state
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
prompt_embeds = prompt_embeds * prompt_attention_mask.to(prompt_embeds).unsqueeze(-1)
return prompt_embeds, prompt_attention_mask
@staticmethod
def _get_t5_prompt_ids(
components: AnimaModularPipeline,
prompt: str | list[str],
max_sequence_length: int,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
prompt = [prompt] if isinstance(prompt, str) else prompt
text_inputs = components.t5_tokenizer(
prompt,
padding="longest",
max_length=max_sequence_length,
truncation=True,
return_tensors="pt",
)
return text_inputs.input_ids.to(device), text_inputs.attention_mask.to(device)
@classmethod
def encode_prompt(
cls,
components: AnimaModularPipeline,
prompt: str | list[str],
negative_prompt: str | list[str] | None = None,
prepare_unconditional_embeds: bool = True,
max_sequence_length: int = 512,
device: torch.device | None = None,
dtype: torch.dtype | None = None,
) -> dict[str, torch.Tensor | None]:
device = device or components._execution_device
dtype = dtype or components.text_encoder.dtype
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
prompt_embeds, prompt_attention_mask = cls._get_qwen_prompt_embeds(
components=components,
prompt=prompt,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
t5_input_ids, t5_attention_mask = cls._get_t5_prompt_ids(
components=components,
prompt=prompt,
max_sequence_length=max_sequence_length,
device=device,
)
negative_prompt_embeds = None
negative_prompt_attention_mask = None
negative_t5_input_ids = None
negative_t5_attention_mask = None
if prepare_unconditional_embeds:
negative_prompt = negative_prompt if negative_prompt is not None else ""
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
if prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
if batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
negative_prompt_embeds, negative_prompt_attention_mask = cls._get_qwen_prompt_embeds(
components=components,
prompt=negative_prompt,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
negative_t5_input_ids, negative_t5_attention_mask = cls._get_t5_prompt_ids(
components=components,
prompt=negative_prompt,
max_sequence_length=max_sequence_length,
device=device,
)
return {
"qwen_prompt_embeds": prompt_embeds,
"qwen_attention_mask": prompt_attention_mask,
"t5_input_ids": t5_input_ids,
"t5_attention_mask": t5_attention_mask,
"negative_qwen_prompt_embeds": negative_prompt_embeds,
"negative_qwen_attention_mask": negative_prompt_attention_mask,
"negative_t5_input_ids": negative_t5_input_ids,
"negative_t5_attention_mask": negative_t5_attention_mask,
}
@torch.no_grad()
def __call__(self, components: AnimaModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
self.check_inputs(block_state)
prompt_outputs = self.encode_prompt(
components=components,
prompt=block_state.prompt,
negative_prompt=block_state.negative_prompt,
prepare_unconditional_embeds=components.guider.num_conditions > 1,
max_sequence_length=block_state.max_sequence_length,
device=components._execution_device,
dtype=components.text_encoder.dtype,
)
for name, value in prompt_outputs.items():
setattr(block_state, name, value)
self.set_block_state(state, block_state)
return components, state
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