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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 PIL
import torch
from transformers import Qwen2Tokenizer, Qwen3Model
from ...configuration_utils import FrozenDict
from ...guiders import ClassifierFreeGuidance
from ...image_processor import VaeImageProcessor
from ...models import AutoencoderKL
from ...utils import is_ftfy_available, logging
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import ZImageModularPipeline
if is_ftfy_available():
pass
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def get_qwen_prompt_embeds(
text_encoder: Qwen3Model,
tokenizer: Qwen2Tokenizer,
prompt: str | list[str],
device: torch.device,
max_sequence_length: int = 512,
) -> list[torch.Tensor]:
prompt = [prompt] if isinstance(prompt, str) else prompt
for i, prompt_item in enumerate(prompt):
messages = [
{"role": "user", "content": prompt_item},
]
prompt_item = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True,
)
prompt[i] = prompt_item
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids.to(device)
prompt_masks = text_inputs.attention_mask.to(device).bool()
prompt_embeds = text_encoder(
input_ids=text_input_ids,
attention_mask=prompt_masks,
output_hidden_states=True,
).hidden_states[-2]
prompt_embeds_list = []
for i in range(len(prompt_embeds)):
prompt_embeds_list.append(prompt_embeds[i][prompt_masks[i]])
return prompt_embeds_list
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
def retrieve_latents(
encoder_output: torch.Tensor, generator: torch.Generator | None = None, sample_mode: str = "sample"
):
if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
return encoder_output.latent_dist.sample(generator)
elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
return encoder_output.latent_dist.mode()
elif hasattr(encoder_output, "latents"):
return encoder_output.latents
else:
raise AttributeError("Could not access latents of provided encoder_output")
def encode_vae_image(
image_tensor: torch.Tensor,
vae: AutoencoderKL,
generator: torch.Generator,
device: torch.device,
dtype: torch.dtype,
latent_channels: int = 16,
):
if not isinstance(image_tensor, torch.Tensor):
raise ValueError(f"Expected image_tensor to be a tensor, got {type(image_tensor)}.")
if isinstance(generator, list) and len(generator) != image_tensor.shape[0]:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but it is not same as number of images {image_tensor.shape[0]}."
)
image_tensor = image_tensor.to(device=device, dtype=dtype)
if isinstance(generator, list):
image_latents = [
retrieve_latents(vae.encode(image_tensor[i : i + 1]), generator=generator[i])
for i in range(image_tensor.shape[0])
]
image_latents = torch.cat(image_latents, dim=0)
else:
image_latents = retrieve_latents(vae.encode(image_tensor), generator=generator)
image_latents = (image_latents - vae.config.shift_factor) * vae.config.scaling_factor
return image_latents
class ZImageTextEncoderStep(ModularPipelineBlocks):
model_name = "z-image"
@property
def description(self) -> str:
return "Text Encoder step that generate text_embeddings to guide the video generation"
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("text_encoder", Qwen3Model),
ComponentSpec("tokenizer", Qwen2Tokenizer),
ComponentSpec(
"guider",
ClassifierFreeGuidance,
config=FrozenDict({"guidance_scale": 5.0, "enabled": False}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam("prompt"),
InputParam("negative_prompt"),
InputParam("max_sequence_length", default=512),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"prompt_embeds",
type_hint=list[torch.Tensor],
kwargs_type="denoiser_input_fields",
description="text embeddings used to guide the image generation",
),
OutputParam(
"negative_prompt_embeds",
type_hint=list[torch.Tensor],
kwargs_type="denoiser_input_fields",
description="negative text embeddings used to guide the image generation",
),
]
@staticmethod
def check_inputs(block_state):
if block_state.prompt is not None and (
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)}")
@staticmethod
def encode_prompt(
components,
prompt: str,
device: torch.device | None = None,
prepare_unconditional_embeds: bool = True,
negative_prompt: str | None = None,
max_sequence_length: int = 512,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `list[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
prepare_unconditional_embeds (`bool`):
whether to use prepare unconditional embeddings or not
negative_prompt (`str` or `list[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
max_sequence_length (`int`, defaults to `512`):
The maximum number of text tokens to be used for the generation process.
"""
device = device or components._execution_device
if not isinstance(prompt, list):
prompt = [prompt]
batch_size = len(prompt)
prompt_embeds = get_qwen_prompt_embeds(
text_encoder=components.text_encoder,
tokenizer=components.tokenizer,
prompt=prompt,
max_sequence_length=max_sequence_length,
device=device,
)
negative_prompt_embeds = None
if prepare_unconditional_embeds:
negative_prompt = negative_prompt or ""
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)}."
)
elif 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 = get_qwen_prompt_embeds(
text_encoder=components.text_encoder,
tokenizer=components.tokenizer,
prompt=negative_prompt,
max_sequence_length=max_sequence_length,
device=device,
)
return prompt_embeds, negative_prompt_embeds
@torch.no_grad()
def __call__(self, components: ZImageModularPipeline, state: PipelineState) -> PipelineState:
# Get inputs and intermediates
block_state = self.get_block_state(state)
self.check_inputs(block_state)
block_state.device = components._execution_device
# Encode input prompt
(
block_state.prompt_embeds,
block_state.negative_prompt_embeds,
) = self.encode_prompt(
components=components,
prompt=block_state.prompt,
device=block_state.device,
prepare_unconditional_embeds=components.requires_unconditional_embeds,
negative_prompt=block_state.negative_prompt,
max_sequence_length=block_state.max_sequence_length,
)
# Add outputs
self.set_block_state(state, block_state)
return components, state
class ZImageVaeImageEncoderStep(ModularPipelineBlocks):
model_name = "z-image"
@property
def description(self) -> str:
return "Vae Image Encoder step that generate condition_latents based on image to guide the image generation"
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("vae", AutoencoderKL),
ComponentSpec(
"image_processor",
VaeImageProcessor,
config=FrozenDict({"vae_scale_factor": 8 * 2}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam("image", type_hint=PIL.Image.Image, required=True),
InputParam("height"),
InputParam("width"),
InputParam("generator"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"image_latents",
type_hint=torch.Tensor,
description="video latent representation with the first frame image condition",
),
]
@staticmethod
def check_inputs(components, block_state):
if (block_state.height is not None and block_state.height % components.vae_scale_factor_spatial != 0) or (
block_state.width is not None and block_state.width % components.vae_scale_factor_spatial != 0
):
raise ValueError(
f"`height` and `width` have to be divisible by {components.vae_scale_factor_spatial} but are {block_state.height} and {block_state.width}."
)
def __call__(self, components: ZImageModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
self.check_inputs(components, block_state)
image = block_state.image
device = components._execution_device
dtype = torch.float32
vae_dtype = components.vae.dtype
image_tensor = components.image_processor.preprocess(
image, height=block_state.height, width=block_state.width
).to(device=device, dtype=dtype)
block_state.image_latents = encode_vae_image(
image_tensor=image_tensor,
vae=components.vae,
generator=block_state.generator,
device=device,
dtype=vae_dtype,
latent_channels=components.num_channels_latents,
)
self.set_block_state(state, block_state)
return components, state
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