# Copyright 2024 Stability AI and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Any, Callable, Dict, List, Optional, Union import torch from transformers import ( T5EncoderModel, T5TokenizerFast, ) from diffusers.image_processor import PipelineImageInput from diffusers import AutoencoderKL # Waiting for diffusers udpdate from diffusers.schedulers import FlowMatchEulerDiscreteScheduler from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import logging from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput from diffusers.pipelines.flux.pipeline_flux import retrieve_timesteps from controlnet_bria import BriaControlNetModel, BriaMultiControlNetModel from pipeline_bria import BriaPipeline from transformer_bria import BriaTransformer2DModel from bria_utils import get_original_sigmas XLA_AVAILABLE = False logger = logging.get_logger(__name__) # pylint: disable=invalid-name class BriaControlNetPipeline(BriaPipeline): r""" Args: transformer ([`SD3Transformer2DModel`]): Conditional Transformer (MMDiT) architecture to denoise the encoded image latents. scheduler ([`FlowMatchEulerDiscreteScheduler`]): A scheduler to be used in combination with `transformer` to denoise the encoded image latents. vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`T5EncoderModel`]): Frozen text-encoder. Stable Diffusion 3 uses [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant. tokenizer (`T5TokenizerFast`): Tokenizer of class [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer). """ model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder->transformer->vae" _optional_components = [] _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"] def __init__( # EYAL - removed clip text encoder + tokenizer self, transformer: BriaTransformer2DModel, scheduler: Union[FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers], vae: AutoencoderKL, text_encoder: T5EncoderModel, tokenizer: T5TokenizerFast, controlnet: BriaControlNetModel, ): super().__init__( transformer=transformer, scheduler=scheduler, vae=vae, text_encoder=text_encoder, tokenizer=tokenizer ) self.register_modules(controlnet=controlnet) def prepare_image( self, image, width, height, batch_size, num_images_per_prompt, device, dtype, do_classifier_free_guidance=False, guess_mode=False, ): if isinstance(image, torch.Tensor): pass else: image = self.image_processor.preprocess(image, height=height, width=width) image_batch_size = image.shape[0] if image_batch_size == 1: repeat_by = batch_size else: # image batch size is the same as prompt batch size repeat_by = num_images_per_prompt image = image.repeat_interleave(repeat_by, dim=0) image = image.to(device=device, dtype=dtype) if do_classifier_free_guidance and not guess_mode: image = torch.cat([image] * 2) return image def prepare_control(self, control_image, width, height, batch_size, num_images_per_prompt, device, control_mode): num_channels_latents = self.transformer.config.in_channels // 4 control_image = self.prepare_image( image=control_image, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=self.vae.dtype, ) height, width = control_image.shape[-2:] # vae encode control_image = self.vae.encode(control_image).latent_dist.sample() control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor # pack height_control_image, width_control_image = control_image.shape[2:] control_image = self._pack_latents( control_image, batch_size * num_images_per_prompt, num_channels_latents, height_control_image, width_control_image, ) # Here we ensure that `control_mode` has the same length as the control_image. if control_mode is not None: if not isinstance(control_mode, int): raise ValueError(" For `BriaControlNet`, `control_mode` should be an `int` or `None`") control_mode = torch.tensor(control_mode).to(device, dtype=torch.long) control_mode = control_mode.view(-1, 1).expand(control_image.shape[0], 1) return control_image, control_mode def prepare_multi_control(self, control_image, width, height, batch_size, num_images_per_prompt, device, control_mode): num_channels_latents = self.transformer.config.in_channels // 4 control_images = [] for i, control_image_ in enumerate(control_image): control_image_ = self.prepare_image( image=control_image_, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=self.vae.dtype, ) height, width = control_image_.shape[-2:] # vae encode control_image_ = self.vae.encode(control_image_).latent_dist.sample() control_image_ = (control_image_ - self.vae.config.shift_factor) * self.vae.config.scaling_factor # pack height_control_image, width_control_image = control_image_.shape[2:] control_image_ = self._pack_latents( control_image_, batch_size * num_images_per_prompt, num_channels_latents, height_control_image, width_control_image, ) control_images.append(control_image_) control_image = control_images # Here we ensure that `control_mode` has the same length as the control_image. if isinstance(control_mode, list) and len(control_mode) != len(control_image): raise ValueError( "For Multi-ControlNet, `control_mode` must be a list of the same " + " length as the number of controlnets (control images) specified" ) if not isinstance(control_mode, list): control_mode = [control_mode] * len(control_image) # set control mode control_modes = [] for cmode in control_mode: if cmode is None: cmode = -1 control_mode = torch.tensor(cmode).expand(control_images[0].shape[0]).to(device, dtype=torch.long) control_modes.append(control_mode) control_mode = control_modes return control_image, control_mode def get_controlnet_keep(self, timesteps, control_guidance_start, control_guidance_end): controlnet_keep = [] for i in range(len(timesteps)): keeps = [ 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e) for s, e in zip(control_guidance_start, control_guidance_end) ] controlnet_keep.append(keeps[0] if isinstance(self.controlnet, BriaControlNetModel) else keeps) return controlnet_keep def get_control_start_end(self, control_guidance_start, control_guidance_end): if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list): control_guidance_start = len(control_guidance_end) * [control_guidance_start] elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list): control_guidance_end = len(control_guidance_start) * [control_guidance_end] elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list): mult = 1 # TODO - why is this 1? control_guidance_start, control_guidance_end = ( mult * [control_guidance_start], mult * [control_guidance_end], ) return control_guidance_start, control_guidance_end @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 30, timesteps: List[int] = None, guidance_scale: float = 3.5, control_guidance_start: Union[float, List[float]] = 0.0, control_guidance_end: Union[float, List[float]] = 1.0, control_image: Optional[PipelineImageInput] = None, control_mode: Optional[Union[int, List[int]]] = None, controlnet_conditioning_scale: Union[float, List[float]] = 1.0, negative_prompt: Optional[Union[str, List[str]]] = None, 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, negative_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 = 128, ): 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. 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 5.0): 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. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). 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. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_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.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] 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 256): Maximum sequence length to use with the `prompt`. Examples: Returns: [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] 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 control_guidance_start, control_guidance_end = self.get_control_start_end( control_guidance_start=control_guidance_start, control_guidance_end=control_guidance_end ) # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, height, width, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_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, negative_prompt_embeds, text_ids) = self.encode_prompt( prompt=prompt, negative_prompt=negative_prompt, do_classifier_free_guidance=self.do_classifier_free_guidance, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, device=device, num_images_per_prompt=num_images_per_prompt, max_sequence_length=max_sequence_length, lora_scale=lora_scale, ) if self.do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) # 3. Prepare control image if control_image is not None: if isinstance(self.controlnet, BriaControlNetModel): control_image, control_mode = self.prepare_control( control_image=control_image, width=width, height=height, batch_size=batch_size, num_images_per_prompt=num_images_per_prompt, device=device, control_mode=control_mode, ) elif isinstance(self.controlnet, BriaMultiControlNetModel): control_image, control_mode = self.prepare_multi_control( control_image=control_image, width=width, height=height, batch_size=batch_size, num_images_per_prompt=num_images_per_prompt, device=device, control_mode=control_mode, ) # 4. Prepare timesteps # Sample from training sigmas sigmas = get_original_sigmas( num_train_timesteps=self.scheduler.config.num_train_timesteps, num_inference_steps=num_inference_steps ) timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, timesteps, sigmas=sigmas ) num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) self._num_timesteps = len(timesteps) # 5. Prepare latent variables num_channels_latents = self.transformer.config.in_channels // 4 # due to patch=2, we devide by 4 latents, latent_image_ids = self.prepare_latents( batch_size=batch_size * num_images_per_prompt, num_channels_latents=num_channels_latents, height=height, width=width, dtype=prompt_embeds.dtype, device=device, generator=generator, latents=latents, ) # 6. Create tensor stating which controlnets to keep if control_image is not None: controlnet_keep = self.get_controlnet_keep( timesteps=timesteps, control_guidance_start=control_guidance_start, control_guidance_end=control_guidance_end, ) # EYAL - added the CFG loop # 7. Denoising loop with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents # if type(self.scheduler) != FlowMatchEulerDiscreteScheduler: if not isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler): latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latent_model_input.shape[0]) # Handling ControlNet if control_image is not None: if isinstance(controlnet_keep[i], list): if isinstance(controlnet_conditioning_scale, list): cond_scale = controlnet_conditioning_scale else: cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])] else: controlnet_cond_scale = controlnet_conditioning_scale if isinstance(controlnet_cond_scale, list): controlnet_cond_scale = controlnet_cond_scale[0] cond_scale = controlnet_cond_scale * controlnet_keep[i] # controlnet controlnet_block_samples, controlnet_single_block_samples = self.controlnet( hidden_states=latents, controlnet_cond=control_image, controlnet_mode=control_mode, conditioning_scale=cond_scale, timestep=timestep, # 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, ) else: controlnet_block_samples, controlnet_single_block_samples = None, None # This is predicts "v" from flow-matching noise_pred = self.transformer( hidden_states=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds, joint_attention_kwargs=self.joint_attention_kwargs, return_dict=False, txt_ids=text_ids, img_ids=latent_image_ids, controlnet_block_samples=controlnet_block_samples, controlnet_single_block_samples=controlnet_single_block_samples, )[0] # perform guidance if self.do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) # 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) negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_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.to(dtype=self.vae.dtype), 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)