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