"""Based on https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/flux.1-image-generation/flux_helper.py"""
import inspect
import json
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import numpy as np
import openvino as ov
import torch
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
from diffusers.utils.torch_utils import randn_tensor
from transformers import AutoTokenizer
TRANSFORMER_PATH = Path("transformer/transformer.xml")
VAE_DECODER_PATH = Path("vae/vae_decoder.xml")
TEXT_ENCODER_PATH = Path("text_encoder/text_encoder.xml")
TEXT_ENCODER_2_PATH = Path("text_encoder_2/text_encoder_2.xml")
def cleanup_torchscript_cache():
"""
Helper for removing cached model representation
"""
torch._C._jit_clear_class_registry()
torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
torch.jit._state._clear_class_state()
def _prepare_latent_image_ids(
batch_size, height, width, device=torch.device("cpu"), dtype=torch.float32
):
latent_image_ids = torch.zeros(height // 2, width // 2, 3)
latent_image_ids[..., 1] = (
latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
)
latent_image_ids[..., 2] = (
latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
)
latent_image_id_height, latent_image_id_width, latent_image_id_channels = (
latent_image_ids.shape
)
latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1)
latent_image_ids = latent_image_ids.reshape(
batch_size,
latent_image_id_height * latent_image_id_width,
latent_image_id_channels,
)
return latent_image_ids.to(device=device, dtype=dtype)
def rope(pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor:
assert dim % 2 == 0, "The dimension must be even."
scale = torch.arange(0, dim, 2, dtype=torch.float32, device=pos.device) / dim
omega = 1.0 / (theta**scale)
batch_size, seq_length = pos.shape
out = pos.unsqueeze(-1) * omega.unsqueeze(0).unsqueeze(0)
cos_out = torch.cos(out)
sin_out = torch.sin(out)
stacked_out = torch.stack([cos_out, -sin_out, sin_out, cos_out], dim=-1)
out = stacked_out.view(batch_size, -1, dim // 2, 2, 2)
return out.float()
def calculate_shift(
image_seq_len,
base_seq_len: int = 256,
max_seq_len: int = 4096,
base_shift: float = 0.5,
max_shift: float = 1.16,
):
m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
b = base_shift - m * base_seq_len
mu = image_seq_len * m + b
return mu
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
def retrieve_timesteps(
scheduler,
num_inference_steps: Optional[int] = None,
timesteps: Optional[List[int]] = None,
sigmas: Optional[List[float]] = None,
**kwargs,
):
"""
Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
Args:
scheduler (`SchedulerMixin`):
The scheduler to get timesteps from.
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
must be `None`.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
timesteps (`List[int]`, *optional*):
Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
`num_inference_steps` and `sigmas` must be `None`.
sigmas (`List[float]`, *optional*):
Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
`num_inference_steps` and `timesteps` must be `None`.
Returns:
`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
second element is the number of inference steps.
"""
if timesteps is not None and sigmas is not None:
raise ValueError(
"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values"
)
if timesteps is not None:
accepts_timesteps = "timesteps" in set(
inspect.signature(scheduler.set_timesteps).parameters.keys()
)
if not accepts_timesteps:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" timestep schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(timesteps=timesteps, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
elif sigmas is not None:
accept_sigmas = "sigmas" in set(
inspect.signature(scheduler.set_timesteps).parameters.keys()
)
if not accept_sigmas:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" sigmas schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(sigmas=sigmas, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
else:
scheduler.set_timesteps(num_inference_steps, **kwargs)
timesteps = scheduler.timesteps
return timesteps, num_inference_steps
class OVFluxPipeline(DiffusionPipeline):
def __init__(
self,
scheduler,
transformer,
vae,
text_encoder,
text_encoder_2,
tokenizer,
tokenizer_2,
transformer_config,
vae_config,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
transformer=transformer,
scheduler=scheduler,
)
self.vae_config = vae_config
self.transformer_config = transformer_config
self.vae_scale_factor = 2 ** (
len(self.vae_config.get("block_out_channels", [0] * 16))
if hasattr(self, "vae") and self.vae is not None
else 16
)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.tokenizer_max_length = (
self.tokenizer.model_max_length
if hasattr(self, "tokenizer") and self.tokenizer is not None
else 77
)
self.default_sample_size = 64
def _get_t5_prompt_embeds(
self,
prompt: Union[str, List[str]] = None,
num_images_per_prompt: int = 1,
max_sequence_length: int = 512,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
text_inputs = self.tokenizer_2(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_length=False,
return_overflowing_tokens=False,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = torch.from_numpy(self.text_encoder_2(text_input_ids)[0])
_, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(
batch_size * num_images_per_prompt, seq_len, -1
)
return prompt_embeds
def _get_clip_prompt_embeds(
self,
prompt: Union[str, List[str]],
num_images_per_prompt: int = 1,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer_max_length,
truncation=True,
return_overflowing_tokens=False,
return_length=False,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = torch.from_numpy(self.text_encoder(text_input_ids)[1])
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1)
return prompt_embeds
def encode_prompt(
self,
prompt: Union[str, List[str]],
prompt_2: Union[str, List[str]],
num_images_per_prompt: int = 1,
prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
max_sequence_length: int = 512,
):
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
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.
"""
prompt = [prompt] if isinstance(prompt, str) else prompt
if prompt is not None:
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
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,
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,
)
text_ids = torch.zeros(batch_size, prompt_embeds.shape[1], 3)
text_ids = text_ids.repeat(num_images_per_prompt, 1, 1)
return prompt_embeds, pooled_prompt_embeds, text_ids
def check_inputs(
self,
prompt,
prompt_2,
height,
width,
prompt_embeds=None,
pooled_prompt_embeds=None,
max_sequence_length=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(
f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
)
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_2 is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (
not isinstance(prompt, str) and not isinstance(prompt, list)
):
raise ValueError(
f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
)
elif prompt_2 is not None and (
not isinstance(prompt_2, str) and not isinstance(prompt_2, list)
):
raise ValueError(
f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}"
)
if prompt_embeds is not None and pooled_prompt_embeds is None:
raise ValueError(
"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
)
if max_sequence_length is not None and max_sequence_length > 512:
raise ValueError(
f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}"
)
@staticmethod
def _prepare_latent_image_ids(batch_size, height, width):
return _prepare_latent_image_ids(batch_size, height, width)
@staticmethod
def _pack_latents(latents, batch_size, num_channels_latents, height, width):
latents = latents.view(
batch_size, num_channels_latents, height // 2, 2, width // 2, 2
)
latents = latents.permute(0, 2, 4, 1, 3, 5)
latents = latents.reshape(
batch_size, (height // 2) * (width // 2), num_channels_latents * 4
)
return latents
@staticmethod
def _unpack_latents(latents, height, width, vae_scale_factor):
batch_size, num_patches, channels = latents.shape
height = height // vae_scale_factor
width = width // vae_scale_factor
latents = latents.view(batch_size, height, width, channels // 4, 2, 2)
latents = latents.permute(0, 3, 1, 4, 2, 5)
latents = latents.reshape(
batch_size, channels // (2 * 2), height * 2, width * 2
)
return latents
def prepare_latents(
self,
batch_size,
num_channels_latents,
height,
width,
generator,
latents=None,
):
height = 2 * (int(height) // self.vae_scale_factor)
width = 2 * (int(width) // self.vae_scale_factor)
shape = (batch_size, num_channels_latents, height, width)
if latents is not None:
latent_image_ids = self._prepare_latent_image_ids(batch_size, height, width)
return latents, latent_image_ids
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
latents = randn_tensor(shape, generator=generator)
latents = self._pack_latents(
latents, batch_size, num_channels_latents, height, width
)
latent_image_ids = self._prepare_latent_image_ids(batch_size, height, width)
return latents, latent_image_ids
@property
def guidance_scale(self):
return self._guidance_scale
@property
def num_timesteps(self):
return self._num_timesteps
@property
def interrupt(self):
return self._interrupt
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
negative_prompt: str = None,
num_inference_steps: int = 28,
timesteps: List[int] = None,
guidance_scale: float = 7.0,
num_images_per_prompt: Optional[int] = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
max_sequence_length: int = 512,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
will be used instead
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image. This is set to 1024 by default for the best results.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image. This is set to 1024 by default for the best results.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
passed will be used. Must be in descending order.
guidance_scale (`float`, *optional*, defaults to 7.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
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.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple.
max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
Returns:
[`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
images.
"""
height = height or self.default_sample_size * self.vae_scale_factor
width = width or self.default_sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
height,
width,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
max_sequence_length=max_sequence_length,
)
self._guidance_scale = guidance_scale
self._interrupt = False
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
(
prompt_embeds,
pooled_prompt_embeds,
text_ids,
) = self.encode_prompt(
prompt=prompt,
prompt_2=prompt_2,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
num_images_per_prompt=num_images_per_prompt,
max_sequence_length=max_sequence_length,
)
# 4. Prepare latent variables
num_channels_latents = self.transformer_config.get("in_channels", 64) // 4
latents, latent_image_ids = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
generator,
latents,
)
# 5. Prepare timesteps
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
image_seq_len = latents.shape[1]
mu = calculate_shift(
image_seq_len,
self.scheduler.config.base_image_seq_len,
self.scheduler.config.max_image_seq_len,
self.scheduler.config.base_shift,
self.scheduler.config.max_shift,
)
timesteps, num_inference_steps = retrieve_timesteps(
scheduler=self.scheduler,
num_inference_steps=num_inference_steps,
timesteps=timesteps,
sigmas=sigmas,
mu=mu,
)
num_warmup_steps = max(
len(timesteps) - num_inference_steps * self.scheduler.order, 0
)
self._num_timesteps = len(timesteps)
# 6. Denoising loop
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
if self.interrupt:
continue
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(latents.shape[0]).to(latents.dtype)
# handle guidance
if self.transformer_config.get("guidance_embeds"):
guidance = torch.tensor([guidance_scale])
guidance = guidance.expand(latents.shape[0])
else:
guidance = None
transformer_input = {
"hidden_states": latents,
"timestep": timestep / 1000,
"pooled_projections": pooled_prompt_embeds,
"encoder_hidden_states": prompt_embeds,
"txt_ids": text_ids,
"img_ids": latent_image_ids,
}
if guidance is not None:
transformer_input["guidance"] = guidance
noise_pred = torch.from_numpy(self.transformer(transformer_input)[0])
latents = self.scheduler.step(
noise_pred, t, latents, return_dict=False
)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or (
(i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
):
progress_bar.update()
if output_type == "latent":
image = latents
else:
latents = self._unpack_latents(
latents, height, width, self.vae_scale_factor
)
latents = latents / self.vae_config.get(
"scaling_factor"
) + self.vae_config.get("shift_factor")
image = self.vae(latents)[0]
image = self.image_processor.postprocess(
torch.from_numpy(image), output_type=output_type
)
if not return_dict:
return (image,)
return FluxPipelineOutput(images=image)
def init_pipeline(
model_dir,
models_dict: Dict[str, Any],
device: str,
use_taef1: bool = False,
):
pipeline_args = {}
print("OpenVINO FLUX Model compilation")
core = ov.Core()
for model_name, model_path in models_dict.items():
pipeline_args[model_name] = core.compile_model(model_path, device)
if model_name == "vae" and use_taef1:
print(f"✅ VAE(TAEF1) - Done!")
else:
print(f"✅ {model_name} - Done!")
transformer_path = models_dict["transformer"]
transformer_config_path = transformer_path.parent / "config.json"
with transformer_config_path.open("r") as f:
transformer_config = json.load(f)
vae_path = models_dict["vae"]
vae_config_path = vae_path.parent / "config.json"
with vae_config_path.open("r") as f:
vae_config = json.load(f)
pipeline_args["vae_config"] = vae_config
pipeline_args["transformer_config"] = transformer_config
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(model_dir / "scheduler")
tokenizer = AutoTokenizer.from_pretrained(model_dir / "tokenizer")
tokenizer_2 = AutoTokenizer.from_pretrained(model_dir / "tokenizer_2")
pipeline_args["scheduler"] = scheduler
pipeline_args["tokenizer"] = tokenizer
pipeline_args["tokenizer_2"] = tokenizer_2
ov_pipe = OVFluxPipeline(**pipeline_args)
return ov_pipe