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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 json
import torch
from transformers import AutoTokenizer, Mistral3Model
from ...configuration_utils import FrozenDict
from ...guiders import ClassifierFreeGuidance
from ...utils import logging
from ...utils.import_utils import is_transformers_version
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import ErnieImageModularPipeline
if is_transformers_version("<", "5.0.0"):
raise ImportError("`ErnieImageModularPipeline` requires `transformers>=5.0.0` for `Ministral3ForCausalLM`.")
from transformers import Ministral3ForCausalLM # noqa: E402
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class ErnieImagePromptEnhancerStep(ModularPipelineBlocks):
model_name = "ernie-image"
@property
def description(self) -> str:
return "Prompt enhancer step that rewrites the input prompt using a causal language model (PE)."
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("pe", Ministral3ForCausalLM),
ComponentSpec("pe_tokenizer", AutoTokenizer),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
"prompt",
required=True,
type_hint=str,
description="The prompt or prompts to guide image generation.",
),
InputParam("height", type_hint=int, description="The height in pixels of the generated image."),
InputParam("width", type_hint=int, description="The width in pixels of the generated image."),
InputParam(
"pe_system_prompt",
type_hint=str,
default=None,
description="Optional system prompt passed to the prompt enhancer.",
),
InputParam(
"pe_temperature",
type_hint=float,
default=0.6,
description="Sampling temperature used when generating with the prompt enhancer.",
),
InputParam(
"pe_top_p",
type_hint=float,
default=0.95,
description="Nucleus sampling `top_p` used when generating with the prompt enhancer.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam("prompt", type_hint=list, description="The prompt list after prompt-enhancer rewriting."),
OutputParam("height", type_hint=int, description="The resolved image height in pixels."),
OutputParam("width", type_hint=int, description="The resolved image width in pixels."),
]
@staticmethod
def _enhance_prompt(
pe: Ministral3ForCausalLM,
pe_tokenizer: AutoTokenizer,
prompt: str,
device: torch.device,
width: int,
height: int,
system_prompt: str | None,
temperature: float,
top_p: float,
) -> str:
user_content = json.dumps({"prompt": prompt, "width": width, "height": height}, ensure_ascii=False)
messages = []
if system_prompt is not None:
messages.append({"role": "system", "content": system_prompt})
messages.append({"role": "user", "content": user_content})
input_text = pe_tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
inputs = pe_tokenizer(input_text, return_tensors="pt").to(device)
output_ids = pe.generate(
**inputs,
max_new_tokens=pe_tokenizer.model_max_length,
do_sample=temperature != 1.0 or top_p != 1.0,
temperature=temperature,
top_p=top_p,
pad_token_id=pe_tokenizer.pad_token_id,
eos_token_id=pe_tokenizer.eos_token_id,
)
generated_ids = output_ids[0][inputs["input_ids"].shape[1] :]
return pe_tokenizer.decode(generated_ids, skip_special_tokens=True).strip()
@torch.no_grad()
def __call__(self, components: ErnieImageModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
prompt = block_state.prompt
if isinstance(prompt, str):
prompt = [prompt]
height = block_state.height or components.default_height
width = block_state.width or components.default_width
revised = [
self._enhance_prompt(
pe=components.pe,
pe_tokenizer=components.pe_tokenizer,
prompt=p,
device=device,
width=width,
height=height,
system_prompt=block_state.pe_system_prompt,
temperature=block_state.pe_temperature,
top_p=block_state.pe_top_p,
)
for p in prompt
]
block_state.prompt = revised
block_state.height = height
block_state.width = width
self.set_block_state(state, block_state)
return components, state
class ErnieImageTextEncoderStep(ModularPipelineBlocks):
model_name = "ernie-image"
@property
def description(self) -> str:
return (
"Text encoder step that encodes prompts into variable-length hidden states for the ErnieImage transformer."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("text_encoder", Mistral3Model),
ComponentSpec("tokenizer", AutoTokenizer),
ComponentSpec(
"guider",
ClassifierFreeGuidance,
config=FrozenDict({"guidance_scale": 4.0}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam("prompt", type_hint=str, description="The prompt or prompts to guide image generation."),
InputParam(
"negative_prompt",
type_hint=str,
description="The prompt or prompts to avoid during image generation.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"prompt_embeds",
type_hint=list,
kwargs_type="denoiser_input_fields",
description="List of per-prompt text embeddings of shape (T, H).",
),
OutputParam(
"negative_prompt_embeds",
type_hint=list,
kwargs_type="denoiser_input_fields",
description="List of per-prompt negative text embeddings for classifier-free guidance.",
),
]
@staticmethod
def _encode(
text_encoder: Mistral3Model,
tokenizer: AutoTokenizer,
prompt: list[str],
device: torch.device,
) -> list[torch.Tensor]:
text_hiddens = []
for p in prompt:
ids = tokenizer(p, add_special_tokens=True, truncation=True, padding=False)["input_ids"]
if len(ids) == 0:
ids = [tokenizer.bos_token_id if tokenizer.bos_token_id is not None else 0]
input_ids = torch.tensor([ids], device=device)
outputs = text_encoder(input_ids=input_ids, output_hidden_states=True)
text_hiddens.append(outputs.hidden_states[-2][0])
return text_hiddens
@torch.no_grad()
def __call__(self, components: ErnieImageModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
prompt = block_state.prompt
if prompt is None:
prompt = [""]
if isinstance(prompt, str):
prompt = [prompt]
block_state.prompt_embeds = self._encode(
text_encoder=components.text_encoder,
tokenizer=components.tokenizer,
prompt=prompt,
device=device,
)
if components.requires_unconditional_embeds:
negative_prompt = block_state.negative_prompt
if negative_prompt is None:
negative_prompt = ""
if isinstance(negative_prompt, str):
negative_prompt = [negative_prompt] * len(prompt)
if len(negative_prompt) != len(prompt):
raise ValueError(
f"`negative_prompt` must have the same length as `prompt` ({len(prompt)}), "
f"got {len(negative_prompt)}."
)
block_state.negative_prompt_embeds = self._encode(
text_encoder=components.text_encoder,
tokenizer=components.tokenizer,
prompt=negative_prompt,
device=device,
)
else:
block_state.negative_prompt_embeds = None
self.set_block_state(state, block_state)
return components, state
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