import math
import inspect
import numpy as np
from typing import Any, Dict, Optional, Tuple, Union, List, Callable
import torch
import torch.nn.functional as F
from einops import rearrange
from diffusers.models.attention import _chunked_feed_forward
from diffusers.models.unets.unet_2d_condition import UNet2DConditionOutput
from diffusers.models.transformers.transformer_2d import Transformer2DModelOutput
from diffusers.pipelines.flux.pipeline_flux import (
retrieve_timesteps,
replace_example_docstring,
EXAMPLE_DOC_STRING,
calculate_shift,
XLA_AVAILABLE,
FluxPipelineOutput
)
# from diffusers.models.transformers import FLUXTransformer2DModel
from diffusers.utils import (
deprecate,
BaseOutput,
is_torch_version,
logging,
USE_PEFT_BACKEND,
scale_lora_layers,
unscale_lora_layers,
)
from diffusers.models.attention_processor import (
Attention,
AttnProcessor,
AttnProcessor2_0,
)
logger = logging.get_logger(__name__)
attn_maps = {}
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def FluxPipeline_call(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = 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,
):
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_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
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(
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,
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,
##################################################
height=height,
##################################################
)[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()
if output_type == "latent":
image = latents
else:
latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
image = self.vae.decode(latents, return_dict=False)[0]
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return FluxPipelineOutput(images=image)
def UNet2DConditionModelForward(
self,
sample: torch.Tensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
class_labels: Optional[torch.Tensor] = None,
timestep_cond: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
mid_block_additional_residual: Optional[torch.Tensor] = None,
down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[UNet2DConditionOutput, Tuple]:
r"""
The [`UNet2DConditionModel`] forward method.
Args:
sample (`torch.Tensor`):
The noisy input tensor with the following shape `(batch, channel, height, width)`.
timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.
encoder_hidden_states (`torch.Tensor`):
The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
class_labels (`torch.Tensor`, *optional*, defaults to `None`):
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
through the `self.time_embedding` layer to obtain the timestep embeddings.
attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
negative values to the attention scores corresponding to "discard" tokens.
cross_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).
added_cond_kwargs: (`dict`, *optional*):
A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
are passed along to the UNet blocks.
down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
A tuple of tensors that if specified are added to the residuals of down unet blocks.
mid_block_additional_residual: (`torch.Tensor`, *optional*):
A tensor that if specified is added to the residual of the middle unet block.
down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)
encoder_attention_mask (`torch.Tensor`):
A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
`True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
which adds large negative values to the attention scores corresponding to "discard" tokens.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
tuple.
Returns:
[`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned,
otherwise a `tuple` is returned where the first element is the sample tensor.
"""
# By default samples have to be AT least a multiple of the overall upsampling factor.
# The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
# However, the upsampling interpolation output size can be forced to fit any upsampling size
# on the fly if necessary.
default_overall_up_factor = 2**self.num_upsamplers
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
forward_upsample_size = False
upsample_size = None
for dim in sample.shape[-2:]:
if dim % default_overall_up_factor != 0:
# Forward upsample size to force interpolation output size.
forward_upsample_size = True
break
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension
# expects mask of shape:
# [batch, key_tokens]
# adds singleton query_tokens dimension:
# [batch, 1, key_tokens]
# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
if attention_mask is not None:
# assume that mask is expressed as:
# (1 = keep, 0 = discard)
# convert mask into a bias that can be added to attention scores:
# (keep = +0, discard = -10000.0)
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
attention_mask = attention_mask.unsqueeze(1)
# convert encoder_attention_mask to a bias the same way we do for attention_mask
if encoder_attention_mask is not None:
encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
# 0. center input if necessary
if self.config.center_input_sample:
sample = 2 * sample - 1.0
# 1. time
t_emb = self.get_time_embed(sample=sample, timestep=timestep)
emb = self.time_embedding(t_emb, timestep_cond)
aug_emb = None
class_emb = self.get_class_embed(sample=sample, class_labels=class_labels)
if class_emb is not None:
if self.config.class_embeddings_concat:
emb = torch.cat([emb, class_emb], dim=-1)
else:
emb = emb + class_emb
aug_emb = self.get_aug_embed(
emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
)
if self.config.addition_embed_type == "image_hint":
aug_emb, hint = aug_emb
sample = torch.cat([sample, hint], dim=1)
emb = emb + aug_emb if aug_emb is not None else emb
if self.time_embed_act is not None:
emb = self.time_embed_act(emb)
encoder_hidden_states = self.process_encoder_hidden_states(
encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
)
# 2. pre-process
sample = self.conv_in(sample)
# 2.5 GLIGEN position net
if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None:
cross_attention_kwargs = cross_attention_kwargs.copy()
gligen_args = cross_attention_kwargs.pop("gligen")
cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)}
# 3. down
# we're popping the `scale` instead of getting it because otherwise `scale` will be propagated
# to the internal blocks and will raise deprecation warnings. this will be confusing for our users.
################################################################################
if cross_attention_kwargs is None:
cross_attention_kwargs = {'timestep' : timestep}
else:
cross_attention_kwargs['timestep'] = timestep
################################################################################
if cross_attention_kwargs is not None:
cross_attention_kwargs = cross_attention_kwargs.copy()
lora_scale = cross_attention_kwargs.pop("scale", 1.0)
else:
lora_scale = 1.0
if USE_PEFT_BACKEND:
# weight the lora layers by setting `lora_scale` for each PEFT layer
scale_lora_layers(self, lora_scale)
is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
# using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets
is_adapter = down_intrablock_additional_residuals is not None
# maintain backward compatibility for legacy usage, where
# T2I-Adapter and ControlNet both use down_block_additional_residuals arg
# but can only use one or the other
if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:
deprecate(
"T2I should not use down_block_additional_residuals",
"1.3.0",
"Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \
and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \
for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ",
standard_warn=False,
)
down_intrablock_additional_residuals = down_block_additional_residuals
is_adapter = True
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
# For t2i-adapter CrossAttnDownBlock2D
additional_residuals = {}
if is_adapter and len(down_intrablock_additional_residuals) > 0:
additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
encoder_attention_mask=encoder_attention_mask,
**additional_residuals,
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
if is_adapter and len(down_intrablock_additional_residuals) > 0:
sample += down_intrablock_additional_residuals.pop(0)
down_block_res_samples += res_samples
if is_controlnet:
new_down_block_res_samples = ()
for down_block_res_sample, down_block_additional_residual in zip(
down_block_res_samples, down_block_additional_residuals
):
down_block_res_sample = down_block_res_sample + down_block_additional_residual
new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
down_block_res_samples = new_down_block_res_samples
# 4. mid
if self.mid_block is not None:
if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
encoder_attention_mask=encoder_attention_mask,
)
else:
sample = self.mid_block(sample, emb)
# To support T2I-Adapter-XL
if (
is_adapter
and len(down_intrablock_additional_residuals) > 0
and sample.shape == down_intrablock_additional_residuals[0].shape
):
sample += down_intrablock_additional_residuals.pop(0)
if is_controlnet:
sample = sample + mid_block_additional_residual
# 5. up
for i, upsample_block in enumerate(self.up_blocks):
is_final_block = i == len(self.up_blocks) - 1
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
# if we have not reached the final block and need to forward the
# upsample size, we do it here
if not is_final_block and forward_upsample_size:
upsample_size = down_block_res_samples[-1].shape[2:]
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
upsample_size=upsample_size,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
)
else:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
upsample_size=upsample_size,
)
# 6. post-process
if self.conv_norm_out:
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
if USE_PEFT_BACKEND:
# remove `lora_scale` from each PEFT layer
unscale_lora_layers(self, lora_scale)
if not return_dict:
return (sample,)
return UNet2DConditionOutput(sample=sample)
def SD3Transformer2DModelForward(
self,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
pooled_projections: torch.FloatTensor = None,
timestep: torch.LongTensor = None,
block_controlnet_hidden_states: List = None,
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
"""
The [`SD3Transformer2DModel`] forward method.
Args:
hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
Input `hidden_states`.
encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
from the embeddings of input conditions.
timestep ( `torch.LongTensor`):
Used to indicate denoising step.
block_controlnet_hidden_states: (`list` of `torch.Tensor`):
A list of tensors that if specified are added to the residuals of transformer blocks.
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).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
tuple.
Returns:
If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
`tuple` where the first element is the sample tensor.
"""
if joint_attention_kwargs is not None:
joint_attention_kwargs = joint_attention_kwargs.copy()
lora_scale = joint_attention_kwargs.pop("scale", 1.0)
else:
lora_scale = 1.0
if USE_PEFT_BACKEND:
# weight the lora layers by setting `lora_scale` for each PEFT layer
scale_lora_layers(self, lora_scale)
else:
if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
logger.warning(
"Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
)
height, width = hidden_states.shape[-2:]
hidden_states = self.pos_embed(hidden_states) # takes care of adding positional embeddings too.
temb = self.time_text_embed(timestep, pooled_projections)
encoder_hidden_states = self.context_embedder(encoder_hidden_states)
for index_block, block in enumerate(self.transformer_blocks):
if self.training and self.gradient_checkpointing:
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
return module(*inputs)
return custom_forward
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
encoder_hidden_states,
temb,
**ckpt_kwargs,
)
else:
encoder_hidden_states, hidden_states = block(
hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb,
##########################################################################################
timestep=timestep, height=height // self.config.patch_size,
##########################################################################################
)
# controlnet residual
if block_controlnet_hidden_states is not None and block.context_pre_only is False:
interval_control = len(self.transformer_blocks) // len(block_controlnet_hidden_states)
hidden_states = hidden_states + block_controlnet_hidden_states[index_block // interval_control]
hidden_states = self.norm_out(hidden_states, temb)
hidden_states = self.proj_out(hidden_states)
# unpatchify
patch_size = self.config.patch_size
height = height // patch_size
width = width // patch_size
hidden_states = hidden_states.reshape(
shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels)
)
hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
output = hidden_states.reshape(
shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size)
)
if USE_PEFT_BACKEND:
# remove `lora_scale` from each PEFT layer
unscale_lora_layers(self, lora_scale)
if not return_dict:
return (output,)
return Transformer2DModelOutput(sample=output)
def FluxTransformer2DModelForward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor = None,
pooled_projections: torch.Tensor = None,
timestep: torch.LongTensor = None,
img_ids: torch.Tensor = None,
txt_ids: torch.Tensor = None,
guidance: torch.Tensor = None,
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
controlnet_block_samples=None,
controlnet_single_block_samples=None,
return_dict: bool = True,
controlnet_blocks_repeat: bool = False,
##################################################
height: int = None,
##################################################
) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
"""
The [`FluxTransformer2DModel`] forward method.
Args:
hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
Input `hidden_states`.
encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
from the embeddings of input conditions.
timestep ( `torch.LongTensor`):
Used to indicate denoising step.
block_controlnet_hidden_states: (`list` of `torch.Tensor`):
A list of tensors that if specified are added to the residuals of transformer blocks.
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).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
tuple.
Returns:
If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
`tuple` where the first element is the sample tensor.
"""
if joint_attention_kwargs is not None:
joint_attention_kwargs = joint_attention_kwargs.copy()
lora_scale = joint_attention_kwargs.pop("scale", 1.0)
else:
lora_scale = 1.0
if USE_PEFT_BACKEND:
# weight the lora layers by setting `lora_scale` for each PEFT layer
scale_lora_layers(self, lora_scale)
else:
if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
logger.warning(
"Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
)
hidden_states = self.x_embedder(hidden_states)
timestep = timestep.to(hidden_states.dtype) * 1000
if guidance is not None:
guidance = guidance.to(hidden_states.dtype) * 1000
else:
guidance = None
temb = (
self.time_text_embed(timestep, pooled_projections)
if guidance is None
else self.time_text_embed(timestep, guidance, pooled_projections)
)
encoder_hidden_states = self.context_embedder(encoder_hidden_states)
if txt_ids.ndim == 3:
logger.warning(
"Passing `txt_ids` 3d torch.Tensor is deprecated."
"Please remove the batch dimension and pass it as a 2d torch Tensor"
)
txt_ids = txt_ids[0]
if img_ids.ndim == 3:
logger.warning(
"Passing `img_ids` 3d torch.Tensor is deprecated."
"Please remove the batch dimension and pass it as a 2d torch Tensor"
)
img_ids = img_ids[0]
ids = torch.cat((txt_ids, img_ids), dim=0)
image_rotary_emb = self.pos_embed(ids)
for index_block, block in enumerate(self.transformer_blocks):
if torch.is_grad_enabled() and self.gradient_checkpointing:
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
return module(*inputs)
return custom_forward
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
encoder_hidden_states,
temb,
image_rotary_emb,
**ckpt_kwargs,
)
else:
encoder_hidden_states, hidden_states = block(
hidden_states=hidden_states,
encoder_hidden_states=encoder_hidden_states,
temb=temb,
image_rotary_emb=image_rotary_emb,
joint_attention_kwargs=joint_attention_kwargs,
##########################################################################################
timestep=timestep, height=height // self.config.patch_size,
##########################################################################################
)
# controlnet residual
if controlnet_block_samples is not None:
interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
interval_control = int(np.ceil(interval_control))
# For Xlabs ControlNet.
if controlnet_blocks_repeat:
hidden_states = (
hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)]
)
else:
hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
for index_block, block in enumerate(self.single_transformer_blocks):
if torch.is_grad_enabled() and self.gradient_checkpointing:
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
return module(*inputs)
return custom_forward
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
temb,
image_rotary_emb,
**ckpt_kwargs,
)
else:
hidden_states = block(
hidden_states=hidden_states,
temb=temb,
image_rotary_emb=image_rotary_emb,
joint_attention_kwargs=joint_attention_kwargs,
)
# controlnet residual
if controlnet_single_block_samples is not None:
interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
interval_control = int(np.ceil(interval_control))
hidden_states[:, encoder_hidden_states.shape[1] :, ...] = (
hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+ controlnet_single_block_samples[index_block // interval_control]
)
hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]
hidden_states = self.norm_out(hidden_states, temb)
output = self.proj_out(hidden_states)
if USE_PEFT_BACKEND:
# remove `lora_scale` from each PEFT layer
unscale_lora_layers(self, lora_scale)
if not return_dict:
return (output,)
return Transformer2DModelOutput(sample=output)
def Transformer2DModelForward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
timestep: Optional[torch.LongTensor] = None,
added_cond_kwargs: Dict[str, torch.Tensor] = None,
class_labels: Optional[torch.LongTensor] = None,
cross_attention_kwargs: Dict[str, Any] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
return_dict: bool = True,
):
"""
The [`Transformer2DModel`] forward method.
Args:
hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.Tensor` of shape `(batch size, channel, height, width)` if continuous):
Input `hidden_states`.
encoder_hidden_states ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
self-attention.
timestep ( `torch.LongTensor`, *optional*):
Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
`AdaLayerZeroNorm`.
cross_attention_kwargs ( `Dict[str, Any]`, *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).
attention_mask ( `torch.Tensor`, *optional*):
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
negative values to the attention scores corresponding to "discard" tokens.
encoder_attention_mask ( `torch.Tensor`, *optional*):
Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:
* Mask `(batch, sequence_length)` True = keep, False = discard.
* Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.
If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
above. This bias will be added to the cross-attention scores.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
tuple.
Returns:
If `return_dict` is True, an [`~models.transformers.transformer_2d.Transformer2DModelOutput`] is returned,
otherwise a `tuple` where the first element is the sample tensor.
"""
if cross_attention_kwargs is not None:
if cross_attention_kwargs.get("scale", None) is not None:
logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
# we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
# we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
# expects mask of shape:
# [batch, key_tokens]
# adds singleton query_tokens dimension:
# [batch, 1, key_tokens]
# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
if attention_mask is not None and attention_mask.ndim == 2:
# assume that mask is expressed as:
# (1 = keep, 0 = discard)
# convert mask into a bias that can be added to attention scores:
# (keep = +0, discard = -10000.0)
attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
attention_mask = attention_mask.unsqueeze(1)
# convert encoder_attention_mask to a bias the same way we do for attention_mask
if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
# 1. Input
if self.is_input_continuous:
batch_size, _, height, width = hidden_states.shape
residual = hidden_states
hidden_states, inner_dim = self._operate_on_continuous_inputs(hidden_states)
elif self.is_input_vectorized:
hidden_states = self.latent_image_embedding(hidden_states)
elif self.is_input_patches:
height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size
hidden_states, encoder_hidden_states, timestep, embedded_timestep = self._operate_on_patched_inputs(
hidden_states, encoder_hidden_states, timestep, added_cond_kwargs
)
####################################################################################################
cross_attention_kwargs['height'] = height
cross_attention_kwargs['width'] = width
####################################################################################################
# 2. Blocks
for block in self.transformer_blocks:
if self.training and self.gradient_checkpointing:
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
return module(*inputs)
return custom_forward
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
timestep,
cross_attention_kwargs,
class_labels,
**ckpt_kwargs,
)
else:
hidden_states = block(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
timestep=timestep,
cross_attention_kwargs=cross_attention_kwargs,
class_labels=class_labels,
)
# 3. Output
if self.is_input_continuous:
output = self._get_output_for_continuous_inputs(
hidden_states=hidden_states,
residual=residual,
batch_size=batch_size,
height=height,
width=width,
inner_dim=inner_dim,
)
elif self.is_input_vectorized:
output = self._get_output_for_vectorized_inputs(hidden_states)
elif self.is_input_patches:
output = self._get_output_for_patched_inputs(
hidden_states=hidden_states,
timestep=timestep,
class_labels=class_labels,
embedded_timestep=embedded_timestep,
height=height,
width=width,
)
if not return_dict:
return (output,)
return Transformer2DModelOutput(sample=output)
def BasicTransformerBlockForward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
timestep: Optional[torch.LongTensor] = None,
cross_attention_kwargs: Dict[str, Any] = None,
class_labels: Optional[torch.LongTensor] = None,
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
) -> torch.Tensor:
if cross_attention_kwargs is not None:
if cross_attention_kwargs.get("scale", None) is not None:
logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
# Notice that normalization is always applied before the real computation in the following blocks.
# 0. Self-Attention
batch_size = hidden_states.shape[0]
if self.norm_type == "ada_norm":
norm_hidden_states = self.norm1(hidden_states, timestep)
elif self.norm_type == "ada_norm_zero":
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
)
elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]:
norm_hidden_states = self.norm1(hidden_states)
elif self.norm_type == "ada_norm_continuous":
norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"])
elif self.norm_type == "ada_norm_single":
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
).chunk(6, dim=1)
norm_hidden_states = self.norm1(hidden_states)
norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
norm_hidden_states = norm_hidden_states.squeeze(1)
else:
raise ValueError("Incorrect norm used")
if self.pos_embed is not None:
norm_hidden_states = self.pos_embed(norm_hidden_states)
# 1. Prepare GLIGEN inputs
cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
################################################################################
attn_parameters = set(inspect.signature(self.attn1.processor.__call__).parameters.keys())
################################################################################
attn_output = self.attn1(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
attention_mask=attention_mask,
################################################################################
**{k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters},
################################################################################
)
if self.norm_type == "ada_norm_zero":
attn_output = gate_msa.unsqueeze(1) * attn_output
elif self.norm_type == "ada_norm_single":
attn_output = gate_msa * attn_output
hidden_states = attn_output + hidden_states
if hidden_states.ndim == 4:
hidden_states = hidden_states.squeeze(1)
# 1.2 GLIGEN Control
if gligen_kwargs is not None:
hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
# 3. Cross-Attention
if self.attn2 is not None:
if self.norm_type == "ada_norm":
norm_hidden_states = self.norm2(hidden_states, timestep)
elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]:
norm_hidden_states = self.norm2(hidden_states)
elif self.norm_type == "ada_norm_single":
# For PixArt norm2 isn't applied here:
# https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
norm_hidden_states = hidden_states
elif self.norm_type == "ada_norm_continuous":
norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"])
else:
raise ValueError("Incorrect norm")
if self.pos_embed is not None and self.norm_type != "ada_norm_single":
norm_hidden_states = self.pos_embed(norm_hidden_states)
attn_output = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
**cross_attention_kwargs,
)
hidden_states = attn_output + hidden_states
# 4. Feed-forward
# i2vgen doesn't have this norm 🤷♂️
if self.norm_type == "ada_norm_continuous":
norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"])
elif not self.norm_type == "ada_norm_single":
norm_hidden_states = self.norm3(hidden_states)
if self.norm_type == "ada_norm_zero":
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self.norm_type == "ada_norm_single":
norm_hidden_states = self.norm2(hidden_states)
norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
else:
ff_output = self.ff(norm_hidden_states)
if self.norm_type == "ada_norm_zero":
ff_output = gate_mlp.unsqueeze(1) * ff_output
elif self.norm_type == "ada_norm_single":
ff_output = gate_mlp * ff_output
hidden_states = ff_output + hidden_states
if hidden_states.ndim == 4:
hidden_states = hidden_states.squeeze(1)
return hidden_states
def JointTransformerBlockForward(
self,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor,
temb: torch.FloatTensor,
############################################################
height: int = None,
timestep: Optional[torch.Tensor] = None,
############################################################
):
if self.use_dual_attention:
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_hidden_states2, gate_msa2 = self.norm1(
hidden_states, emb=temb
)
else:
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
if self.context_pre_only:
norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states, temb)
else:
norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
encoder_hidden_states, emb=temb
)
# Attention.
attn_output, context_attn_output = self.attn(
hidden_states=norm_hidden_states, encoder_hidden_states=norm_encoder_hidden_states,
############################################################
timestep=timestep, height=height,
############################################################
)
# Process attention outputs for the `hidden_states`.
attn_output = gate_msa.unsqueeze(1) * attn_output
hidden_states = hidden_states + attn_output
if self.use_dual_attention:
attn_output2 = self.attn2(hidden_states=norm_hidden_states2)
attn_output2 = gate_msa2.unsqueeze(1) * attn_output2
hidden_states = hidden_states + attn_output2
norm_hidden_states = self.norm2(hidden_states)
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
else:
ff_output = self.ff(norm_hidden_states)
ff_output = gate_mlp.unsqueeze(1) * ff_output
hidden_states = hidden_states + ff_output
# Process attention outputs for the `encoder_hidden_states`.
if self.context_pre_only:
encoder_hidden_states = None
else:
context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
encoder_hidden_states = encoder_hidden_states + context_attn_output
norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
context_ff_output = _chunked_feed_forward(
self.ff_context, norm_encoder_hidden_states, self._chunk_dim, self._chunk_size
)
else:
context_ff_output = self.ff_context(norm_encoder_hidden_states)
encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
return encoder_hidden_states, hidden_states
def FluxTransformerBlockForward(
self,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor,
temb: torch.FloatTensor,
image_rotary_emb=None,
joint_attention_kwargs=None,
############################################################
height: int = None,
timestep: Optional[torch.Tensor] = None,
############################################################
):
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
encoder_hidden_states, emb=temb
)
joint_attention_kwargs = joint_attention_kwargs or {}
# Attention.
attn_output, context_attn_output = self.attn(
hidden_states=norm_hidden_states,
encoder_hidden_states=norm_encoder_hidden_states,
image_rotary_emb=image_rotary_emb,
############################################################
timestep=timestep, height=height,
############################################################
**joint_attention_kwargs,
)
# Process attention outputs for the `hidden_states`.
attn_output = gate_msa.unsqueeze(1) * attn_output
hidden_states = hidden_states + attn_output
norm_hidden_states = self.norm2(hidden_states)
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
ff_output = self.ff(norm_hidden_states)
ff_output = gate_mlp.unsqueeze(1) * ff_output
hidden_states = hidden_states + ff_output
# Process attention outputs for the `encoder_hidden_states`.
context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
encoder_hidden_states = encoder_hidden_states + context_attn_output
norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
context_ff_output = self.ff_context(norm_encoder_hidden_states)
encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
if encoder_hidden_states.dtype == torch.float16:
encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)
return encoder_hidden_states, hidden_states
def attn_call(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
height: int = None,
width: int = None,
timestep: Optional[torch.Tensor] = None,
*args,
**kwargs,
) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
attention_probs = attn.get_attention_scores(query, key, attention_mask)
####################################################################################################
if hasattr(self, "store_attn_map"):
self.attn_map = rearrange(attention_probs, 'b (h w) d -> b d h w', h=height)
self.timestep = int(timestep.item())
####################################################################################################
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None) -> torch.Tensor:
# Efficient implementation equivalent to the following:
L, S = query.size(-2), key.size(-2)
scale_factor = 1 / math.sqrt(query.size(-1)) if scale is None else scale
attn_bias = torch.zeros(L, S, dtype=query.dtype)
if is_causal:
assert attn_mask is None
temp_mask = torch.ones(L, S, dtype=torch.bool).tril(diagonal=0)
attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
attn_bias.to(query.dtype)
if attn_mask is not None:
if attn_mask.dtype == torch.bool:
attn_mask.masked_fill_(attn_mask.logical_not(), float("-inf"))
else:
attn_bias += attn_mask
attn_weight = query @ key.transpose(-2, -1) * scale_factor
attn_weight += attn_bias.to(attn_weight.device)
attn_weight = torch.softmax(attn_weight, dim=-1)
return torch.dropout(attn_weight, dropout_p, train=True) @ value, attn_weight
def attn_call2_0(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
height: int = None,
width: int = None,
timestep: Optional[torch.Tensor] = None,
*args,
**kwargs,
) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
####################################################################################################
if hasattr(self, "store_attn_map"):
hidden_states, attention_probs = scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
self.attn_map = rearrange(attention_probs, 'batch attn_head (h w) attn_dim -> batch attn_head h w attn_dim ', h=height) # detach height*width
self.timestep = int(timestep.item())
else:
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
####################################################################################################
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) # (b,attn_head,h*w,attn_dim) -> (b,h*w,attn_head*attn_dim)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
def lora_attn_call(self, attn: Attention, hidden_states, height, width, *args, **kwargs):
self_cls_name = self.__class__.__name__
deprecate(
self_cls_name,
"0.26.0",
(
f"Make sure use {self_cls_name[4:]} instead by setting"
"LoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using"
" `LoraLoaderMixin.load_lora_weights`"
),
)
attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)
attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)
attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)
attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)
attn._modules.pop("processor")
attn.processor = AttnProcessor()
####################################################################################################
attn.processor.__call__ = attn_call.__get__(attn.processor, AttnProcessor)
####################################################################################################
if hasattr(self, "store_attn_map"):
attn.processor.store_attn_map = True
return attn.processor(attn, hidden_states, height, width, *args, **kwargs)
def lora_attn_call2_0(self, attn: Attention, hidden_states, height, width, *args, **kwargs):
self_cls_name = self.__class__.__name__
deprecate(
self_cls_name,
"0.26.0",
(
f"Make sure use {self_cls_name[4:]} instead by setting"
"LoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using"
" `LoraLoaderMixin.load_lora_weights`"
),
)
attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)
attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)
attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)
attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)
attn._modules.pop("processor")
attn.processor = AttnProcessor2_0()
####################################################################################################
attn.processor.__call__ = attn_call.__get__(attn.processor, AttnProcessor2_0)
####################################################################################################
if hasattr(self, "store_attn_map"):
attn.processor.store_attn_map = True
return attn.processor(attn, hidden_states, height, width, *args, **kwargs)
def joint_attn_call2_0(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
############################################################
height: int = None,
timestep: Optional[torch.Tensor] = None,
############################################################
*args,
**kwargs,
) -> torch.FloatTensor:
residual = hidden_states
batch_size = hidden_states.shape[0]
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# `context` projections.
if encoder_hidden_states is not None:
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
query = torch.cat([query, encoder_hidden_states_query_proj], dim=2)
key = torch.cat([key, encoder_hidden_states_key_proj], dim=2)
value = torch.cat([value, encoder_hidden_states_value_proj], dim=2)
####################################################################################################
if hasattr(self, "store_attn_map"):
hidden_states, attention_probs = scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
image_length = query.shape[2] - encoder_hidden_states_query_proj.shape[2]
# (4,24,4429,4429) -> (4,24,4096,333)
attention_probs = attention_probs[:,:,:image_length,image_length:].cpu()
self.attn_map = rearrange(
attention_probs,
'batch attn_head (height width) attn_dim -> batch attn_head height width attn_dim',
height = height
) # (4, 24, 4096, 333) -> (4, 24, height, width, 333)
self.timestep = timestep[0].cpu().item() # TODO: int -> list
else:
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
####################################################################################################
# hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
if encoder_hidden_states is not None:
# Split the attention outputs.
hidden_states, encoder_hidden_states = (
hidden_states[:, : residual.shape[1]],
hidden_states[:, residual.shape[1] :],
)
if not attn.context_pre_only:
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if encoder_hidden_states is not None:
return hidden_states, encoder_hidden_states
else:
return hidden_states
# FluxAttnProcessor2_0
def flux_attn_call2_0(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
############################################################
height: int = None,
timestep: Optional[torch.Tensor] = None,
############################################################
) -> torch.FloatTensor:
batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
if encoder_hidden_states is not None:
# `context` projections.
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
# attention
query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
if image_rotary_emb is not None:
from diffusers.models.embeddings import apply_rotary_emb
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
####################################################################################################
if hasattr(self, "store_attn_map"):
hidden_states, attention_probs = scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
image_length = query.shape[2] - encoder_hidden_states_query_proj.shape[2]
# (4,24,4429,4429) -> (4,24,4096,333)
attention_probs = attention_probs[:,:,:image_length,image_length:].cpu()
self.attn_map = rearrange(
attention_probs,
'batch attn_head (height width) attn_dim -> batch attn_head height width attn_dim',
height = height
) # (4, 24, 4096, 333) -> (4, 24, height, width, 333)
self.timestep = timestep[0].cpu().item() # TODO: int -> list
else:
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
####################################################################################################
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
if encoder_hidden_states is not None:
encoder_hidden_states, hidden_states = (
hidden_states[:, : encoder_hidden_states.shape[1]],
hidden_states[:, encoder_hidden_states.shape[1] :],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
return hidden_states, encoder_hidden_states
else:
return hidden_states