import torch
import torch.nn as nn
import numpy as np
import math
from typing import Any, Callable, Dict, List, Optional, Union
from diffusers.pipelines.flux.pipeline_flux import calculate_shift, retrieve_timesteps, FluxPipeline
from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
from diffusers.utils import is_torch_xla_available, logging
from diffusers.utils.torch_utils import randn_tensor
if is_torch_xla_available():
import torch_xla.core.xla_model as xm # type: ignore
XLA_AVAILABLE = True
else:
XLA_AVAILABLE = False
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def _get_clip_prompt_embeds(
tokenizer,
text_encoder,
prompt: Union[str, List[str]],
num_images_per_prompt: int = 1,
device: Optional[torch.device] = None,
):
device = device or text_encoder.device
dtype = text_encoder.dtype
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=text_encoder.config.max_position_embeddings,
truncation=True,
return_overflowing_tokens=False,
return_length=False,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=False)
# Use pooled output of CLIPTextModel
prompt_embeds = prompt_embeds.pooler_output
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
# 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 _get_t5_prompt_embeds(
tokenizer,
text_encoder,
prompt: Union[str, List[str]] = None,
num_images_per_prompt: int = 1,
max_sequence_length: int = 512,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
):
device = device or text_encoder.device
dtype = dtype or text_encoder.dtype
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
text_inputs = tokenizer(
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 = text_encoder(text_input_ids.to(device), output_hidden_states=False)[0]
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
_, 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 encode_prompt(
tokenizers,
text_encoders,
prompt: Union[str, List[str]],
prompt_2: Union[str, List[str]] = None,
num_images_per_prompt: int = 1,
max_sequence_length: int = 512,
):
tokenizer_1, tokenizer_2 = tokenizers
text_encoder_1, text_encoder_2 = text_encoders
device = text_encoder_1.device
dtype = text_encoder_1.dtype
prompt = [prompt] if isinstance(prompt, str) else prompt
prompt_2 = prompt_2 or prompt
prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
batch_size = len(prompt)
# We only use the pooled prompt output from the CLIPTextModel
pooled_prompt_embeds = _get_clip_prompt_embeds(
tokenizer=tokenizer_1,
text_encoder=text_encoder_1,
prompt=prompt,
num_images_per_prompt=num_images_per_prompt,
)
prompt_embeds = _get_t5_prompt_embeds(
tokenizer=tokenizer_2,
text_encoder=text_encoder_2,
prompt=prompt_2,
num_images_per_prompt=num_images_per_prompt,
max_sequence_length=max_sequence_length,
)
text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
return prompt_embeds, pooled_prompt_embeds, text_ids
class CustomFluxPipeline(FluxPipeline):
@staticmethod
def _prepare_latent_image_ids(height, width, list_layer_box, device, dtype):
latent_image_ids_list = []
for layer_idx in range(len(list_layer_box)):
if list_layer_box[layer_idx] == None:
continue
else:
latent_image_ids = torch.zeros(height // 2, width // 2, 3) # [h/2, w/2, 3]
latent_image_ids[..., 0] = layer_idx # use the first dimension for layer representation
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, :]
x1, y1, x2, y2 = list_layer_box[layer_idx]
x1, y1, x2, y2 = x1 // 16, y1 // 16, x2 // 16, y2 // 16
latent_image_ids = latent_image_ids[y1:y2, x1:x2, :]
latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
latent_image_ids = latent_image_ids.reshape(
latent_image_id_height * latent_image_id_width, latent_image_id_channels
)
latent_image_ids_list.append(latent_image_ids)
full_latent_image_ids = torch.cat(latent_image_ids_list, dim=0)
return full_latent_image_ids.to(device=device, dtype=dtype)
def prepare_latents(
self,
batch_size,
num_layers,
num_channels_latents,
height,
width,
list_layer_box,
dtype,
device,
generator,
latents=None,
):
height = 2 * (int(height) // self.vae_scale_factor)
width = 2 * (int(width) // self.vae_scale_factor)
shape = (batch_size, num_layers, num_channels_latents, height, width)
if latents is not None:
latent_image_ids = self._prepare_latent_image_ids(batch_size, height, width, device, dtype)
return latents.to(device=device, dtype=dtype), 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, device=device, dtype=dtype) # [bs, f, c_latent, h, w]
latent_image_ids = self._prepare_latent_image_ids(height, width, list_layer_box, device, dtype)
return latents, latent_image_ids
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
validation_box: List[tuple] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 28,
timesteps: List[int] = None,
guidance_scale: float = 3.5,
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,
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
max_sequence_length: int = 512,
num_layers: int = 5,
sdxl_vae: nn.Module = None,
transparent_decoder: nn.Module = None,
):
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.
joint_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
Examples:
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,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
max_sequence_length=max_sequence_length,
)
self._guidance_scale = guidance_scale
self._joint_attention_kwargs = joint_attention_kwargs
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]
device = self._execution_device
lora_scale = (
self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
)
(
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,
device=device,
num_images_per_prompt=num_images_per_prompt,
max_sequence_length=max_sequence_length,
lora_scale=lora_scale,
)
# 4. Prepare latent variables
num_channels_latents = self.transformer.config.in_channels // 4
latents, latent_image_ids = self.prepare_latents(
batch_size * num_images_per_prompt,
num_layers,
num_channels_latents,
height,
width,
validation_box,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 5. Prepare timesteps
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
image_seq_len = latent_image_ids.shape[0] # ???
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(
self.scheduler,
num_inference_steps,
device,
timesteps,
sigmas,
mu=mu,
)
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
self._num_timesteps = len(timesteps)
# handle guidance
if self.transformer.config.guidance_embeds:
guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
guidance = guidance.expand(latents.shape[0])
else:
guidance = None
# 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)
noise_pred = self.transformer(
hidden_states=latents,
list_layer_box=validation_box,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
# compute the previous noisy sample x_t -> x_t-1
latents_dtype = latents.dtype
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
if latents.dtype != latents_dtype:
if torch.backends.mps.is_available():
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
latents = latents.to(latents_dtype)
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
# 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 XLA_AVAILABLE:
xm.mark_step()
# create a grey latent
bs, n_frames, channel_latent, height, width = latents.shape
pixel_grey = torch.zeros(size=(bs*n_frames, 3, height*8, width*8), device=latents.device, dtype=latents.dtype)
latent_grey = self.vae.encode(pixel_grey).latent_dist.sample()
latent_grey = (latent_grey - self.vae.config.shift_factor) * self.vae.config.scaling_factor
latent_grey = latent_grey.view(bs, n_frames, channel_latent, height, width) # [bs, f, c_latent, h, w]
# fill in the latents
for layer_idx in range(latent_grey.shape[1]):
x1, y1, x2, y2 = validation_box[layer_idx]
x1, y1, x2, y2 = x1 // 8, y1 // 8, x2 // 8, y2 // 8
latent_grey[:, layer_idx, :, y1:y2, x1:x2] = latents[:, layer_idx, :, y1:y2, x1:x2]
latents = latent_grey
if output_type == "latent":
image = latents
else:
latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
latents = latents.reshape(bs * n_frames, channel_latent, height, width)
image = self.vae.decode(latents, return_dict=False)[0]
if sdxl_vae is not None:
sdxl_vae = sdxl_vae.to(dtype=image.dtype, device=image.device)
sdxl_latents = sdxl_vae.encode(image).latent_dist.sample()
transparent_decoder = transparent_decoder.to(dtype=image.dtype, device=image.device)
result_list, vis_list = transparent_decoder(sdxl_vae, sdxl_latents)
else:
result_list, vis_list = None, None
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image, result_list, vis_list)
return FluxPipelineOutput(images=image), result_list, vis_list
class CustomFluxPipelineCfg(CustomFluxPipeline):
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
validation_box: List[tuple] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 28,
timesteps: List[int] = None,
guidance_scale: float = 3.5,
true_gs: float = 3.5,
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,
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
max_sequence_length: int = 512,
num_layers: int = 5,
sdxl_vae: nn.Module = None,
transparent_decoder: nn.Module = None,
):
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.
joint_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
Examples:
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,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
max_sequence_length=max_sequence_length,
)
self._guidance_scale = guidance_scale
self._joint_attention_kwargs = joint_attention_kwargs
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]
device = self._execution_device
lora_scale = (
self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
)
(
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,
device=device,
num_images_per_prompt=num_images_per_prompt,
max_sequence_length=max_sequence_length,
lora_scale=lora_scale,
)
(
neg_prompt_embeds,
neg_pooled_prompt_embeds,
neg_text_ids,
) = self.encode_prompt(
prompt="",
prompt_2=None,
device=device,
num_images_per_prompt=num_images_per_prompt,
max_sequence_length=max_sequence_length,
lora_scale=lora_scale,
)
# 4. Prepare latent variables
num_channels_latents = self.transformer.config.in_channels // 4
latents, latent_image_ids = self.prepare_latents(
batch_size * num_images_per_prompt,
num_layers,
num_channels_latents,
height,
width,
validation_box,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 5. Prepare timesteps
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
image_seq_len = latent_image_ids.shape[0] # ???
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(
self.scheduler,
num_inference_steps,
device,
timesteps,
sigmas,
mu=mu,
)
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
self._num_timesteps = len(timesteps)
# handle guidance
if self.transformer.config.guidance_embeds:
guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
guidance = guidance.expand(latents.shape[0])
else:
guidance = None
# 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)
noise_pred = self.transformer(
hidden_states=latents,
list_layer_box=validation_box,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
neg_noise_pred = self.transformer(
hidden_states=latents,
list_layer_box=validation_box,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=neg_pooled_prompt_embeds,
encoder_hidden_states=neg_prompt_embeds,
txt_ids=neg_text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
noise_pred = neg_noise_pred + true_gs * (noise_pred - neg_noise_pred)
# compute the previous noisy sample x_t -> x_t-1
latents_dtype = latents.dtype
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
if latents.dtype != latents_dtype:
if torch.backends.mps.is_available():
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
latents = latents.to(latents_dtype)
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
# 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 XLA_AVAILABLE:
xm.mark_step()
# create a grey latent
bs, n_frames, channel_latent, height, width = latents.shape
pixel_grey = torch.zeros(size=(bs*n_frames, 3, height*8, width*8), device=latents.device, dtype=latents.dtype)
latent_grey = self.vae.encode(pixel_grey).latent_dist.sample()
latent_grey = (latent_grey - self.vae.config.shift_factor) * self.vae.config.scaling_factor
latent_grey = latent_grey.view(bs, n_frames, channel_latent, height, width) # [bs, f, c_latent, h, w]
# fill in the latents
for layer_idx in range(latent_grey.shape[1]):
if validation_box[layer_idx] == None:
continue
x1, y1, x2, y2 = validation_box[layer_idx]
x1, y1, x2, y2 = x1 // 8, y1 // 8, x2 // 8, y2 // 8
latent_grey[:, layer_idx, :, y1:y2, x1:x2] = latents[:, layer_idx, :, y1:y2, x1:x2]
latents = latent_grey
if output_type == "latent":
image = latents
else:
latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
latents = latents.reshape(bs * n_frames, channel_latent, height, width)
latents_segs = torch.split(latents, 16, dim=0) ### split latents by 16 to avoid odd purple output
image_segs = [self.vae.decode(latents_seg, return_dict=False)[0] for latents_seg in latents_segs]
image = torch.cat(image_segs, dim=0)
if sdxl_vae is not None:
sdxl_vae = sdxl_vae.to(dtype=image.dtype, device=image.device)
decoded_fg, decoded_alpha = sdxl_vae(latents, [validation_box])
decoded_alpha = (decoded_alpha + 1.0) / 2.0 #torch.Size([5, 1, 1024, 1024])
decoded_alpha = torch.clamp(decoded_alpha, min=0.0, max=1.0).permute(0, 2, 3, 1) #torch.Size([5, 1024, 1024, 1])
decoded_fg = (decoded_fg + 1.0) / 2.0
decoded_fg = torch.clamp(decoded_fg, min=0.0, max=1.0).permute(0, 2, 3, 1)#torch.Size([5, 1024, 1024, 3]))
vis_list = None
png = torch.cat([decoded_fg, decoded_alpha], dim=3)#[0] #torch.Size([1024, 1024, 4])
result_list = (png * 255.0).detach().cpu().float().numpy().clip(0, 255).astype(np.uint8)
else:
result_list, vis_list = None, None
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image, result_list, vis_list, latents)
return FluxPipelineOutput(images=image), result_list, vis_list, latents
class CustomFluxPipelineCfgInpaint(CustomFluxPipeline):
@torch.no_grad()
def __call__(
self,
image: Optional[List[torch.FloatTensor]] = None,
mask: Optional[torch.FloatTensor] = None,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
validation_box: List[tuple] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 28,
timesteps: List[int] = None,
guidance_scale: float = 3.5,
true_gs: float = 3.5,
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,
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
max_sequence_length: int = 512,
num_layers: int = 5,
sdxl_vae: nn.Module = None,
transparent_decoder: nn.Module = None,
):
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.
joint_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
Examples:
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,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
max_sequence_length=max_sequence_length,
)
self._guidance_scale = guidance_scale
self._joint_attention_kwargs = joint_attention_kwargs
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]
device = self._execution_device
lora_scale = (
self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
)
(
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,
device=device,
num_images_per_prompt=num_images_per_prompt,
max_sequence_length=max_sequence_length,
lora_scale=lora_scale,
)
(
neg_prompt_embeds,
neg_pooled_prompt_embeds,
neg_text_ids,
) = self.encode_prompt(
prompt="",
prompt_2=None,
device=device,
num_images_per_prompt=num_images_per_prompt,
max_sequence_length=max_sequence_length,
lora_scale=lora_scale,
)
# 4. Prepare latent variables
num_channels_latents = self.transformer.config.in_channels // 4
latents, latent_image_ids = self.prepare_latents(
batch_size * num_images_per_prompt,
num_layers,
num_channels_latents,
height,
width,
validation_box,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 4.1. Prepare image and mask
merged_pt, backgd_pt, list_layer_pt = image[0], image[1], image[2:]
# prepare RGB, Alpha
layer_pt_grey = [layer_pt[:, :3] * ((layer_pt[:, 3:4] + 1) / 2.) for layer_pt in list_layer_pt]
pixel_values_vae_input = torch.cat([merged_pt, backgd_pt] + layer_pt_grey, dim=0).to(device, dtype=self.vae.dtype) # [bs*(l+2), c_img, H, W]
# Convert images to latent space
model_input = self.vae.encode(pixel_values_vae_input).latent_dist.sample()
model_input = (model_input - self.vae.config.shift_factor) * self.vae.config.scaling_factor
model_input = model_input.reshape(1, len(validation_box), model_input.shape[1], model_input.shape[2], model_input.shape[3]) # [bs, f, c_latent, h, w]
# copy latent and noise
orig_latents = model_input
noise = latents.clone()
# 5. Prepare timesteps
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
image_seq_len = latent_image_ids.shape[0] # ???
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(
self.scheduler,
num_inference_steps,
device,
timesteps,
sigmas,
mu=mu,
)
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
self._num_timesteps = len(timesteps)
# handle guidance
if self.transformer.config.guidance_embeds:
guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
guidance = guidance.expand(latents.shape[0])
else:
guidance = None
# 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)
noise_pred = self.transformer(
hidden_states=latents,
list_layer_box=validation_box,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
neg_noise_pred = self.transformer(
hidden_states=latents,
list_layer_box=validation_box,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=neg_pooled_prompt_embeds,
encoder_hidden_states=neg_prompt_embeds,
txt_ids=neg_text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
noise_pred = neg_noise_pred + true_gs * (noise_pred - neg_noise_pred)
# compute the previous noisy sample x_t -> x_t-1
latents_dtype = latents.dtype
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
# blend the latents with the original image
init_latents_proper = orig_latents.to(latents.dtype)
init_mask = mask.reshape(1, -1, 1, 1, 1).to(latents.dtype)
if i < len(timesteps) - 1:
noise_timestep = timesteps[i + 1]
init_latents_proper = self.scheduler.scale_noise(
init_latents_proper, torch.tensor([noise_timestep]), noise
)
latents = (1 - init_mask) * init_latents_proper + init_mask * latents
if latents.dtype != latents_dtype:
if torch.backends.mps.is_available():
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
latents = latents.to(latents_dtype)
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
# 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 XLA_AVAILABLE:
xm.mark_step()
# create a grey latent
bs, n_frames, channel_latent, height, width = latents.shape
pixel_grey = torch.zeros(size=(bs*n_frames, 3, height*8, width*8), device=latents.device, dtype=latents.dtype)
latent_grey = self.vae.encode(pixel_grey).latent_dist.sample()
latent_grey = (latent_grey - self.vae.config.shift_factor) * self.vae.config.scaling_factor
latent_grey = latent_grey.view(bs, n_frames, channel_latent, height, width) # [bs, f, c_latent, h, w]
# fill in the latents
for layer_idx in range(latent_grey.shape[1]):
if validation_box[layer_idx] == None:
continue
x1, y1, x2, y2 = validation_box[layer_idx]
x1, y1, x2, y2 = x1 // 8, y1 // 8, x2 // 8, y2 // 8
latent_grey[:, layer_idx, :, y1:y2, x1:x2] = latents[:, layer_idx, :, y1:y2, x1:x2]
latents = latent_grey
if output_type == "latent":
image = latents
else:
latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
latents = latents.reshape(bs * n_frames, channel_latent, height, width)
latents_segs = torch.split(latents, 16, dim=0) ### split latents by 16 to avoid odd purple output
image_segs = [self.vae.decode(latents_seg, return_dict=False)[0] for latents_seg in latents_segs]
image = torch.cat(image_segs, dim=0)
if sdxl_vae is not None:
sdxl_vae = sdxl_vae.to(dtype=image.dtype, device=image.device)
decoded_fg, decoded_alpha = sdxl_vae(latents, [validation_box])
decoded_alpha = (decoded_alpha + 1.0) / 2.0 #torch.Size([5, 1, 1024, 1024])
decoded_alpha = torch.clamp(decoded_alpha, min=0.0, max=1.0).permute(0, 2, 3, 1) #torch.Size([5, 1024, 1024, 1])
decoded_fg = (decoded_fg + 1.0) / 2.0
decoded_fg = torch.clamp(decoded_fg, min=0.0, max=1.0).permute(0, 2, 3, 1)#torch.Size([5, 1024, 1024, 3]))
vis_list = None
png = torch.cat([decoded_fg, decoded_alpha], dim=3)#[0] #torch.Size([1024, 1024, 4])
result_list = (png * 255.0).detach().cpu().float().numpy().clip(0, 255).astype(np.uint8)
else:
result_list, vis_list = None, None
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image, result_list, vis_list, latents)
return FluxPipelineOutput(images=image), result_list, vis_list, latents