import inspect import warnings from dataclasses import dataclass from typing import Callable, List, Optional, Union import numpy as np import PIL import torch from transformers import ( CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection, GPT2Tokenizer, ) from ...models import AutoencoderKL from ...schedulers import KarrasDiffusionSchedulers from ...utils import ( PIL_INTERPOLATION, deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor, ) from ...utils.outputs import BaseOutput from ..pipeline_utils import DiffusionPipeline from .modeling_text_decoder import UniDiffuserTextDecoder from .modeling_uvit import UniDiffuserModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess def preprocess(image): warnings.warn( "The preprocess method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor.preprocess instead", FutureWarning, ) if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): w, h = image[0].size w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image # New BaseOutput child class for joint image-text output @dataclass class ImageTextPipelineOutput(BaseOutput): """ Output class for joint image-text pipelines. Args: images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, num_channels)`. text (`List[str]` or `List[List[str]]`) List of generated text strings of length `batch_size` or a list of list of strings whose outer list has length `batch_size`. """ images: Optional[Union[List[PIL.Image.Image], np.ndarray]] text: Optional[Union[List[str], List[List[str]]]] class UniDiffuserPipeline(DiffusionPipeline): r""" Pipeline for a bimodal image-text [UniDiffuser](https://arxiv.org/pdf/2303.06555.pdf) model, which supports unconditional text and image generation, text-conditioned image generation, image-conditioned text generation, and joint image-text generation. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. This is part of the UniDiffuser image representation, along with the CLIP vision encoding. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Similar to Stable Diffusion, UniDiffuser uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel) to encode text prompts. image_encoder ([`CLIPVisionModel`]): UniDiffuser uses the vision portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModel) to encode images as part of its image representation, along with the VAE latent representation. image_processor ([`CLIPImageProcessor`]): CLIP image processor of class [CLIPImageProcessor](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPImageProcessor), used to preprocess the image before CLIP encoding it with `image_encoder`. clip_tokenizer ([`CLIPTokenizer`]): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTokenizer) which is used to tokenizer a prompt before encoding it with `text_encoder`. text_decoder ([`UniDiffuserTextDecoder`]): Frozen text decoder. This is a GPT-style model which is used to generate text from the UniDiffuser embedding. text_tokenizer ([`GPT2Tokenizer`]): Tokenizer of class [GPT2Tokenizer](https://huggingface.co/docs/transformers/model_doc/gpt2#transformers.GPT2Tokenizer) which is used along with the `text_decoder` to decode text for text generation. unet ([`UniDiffuserModel`]): UniDiffuser uses a [U-ViT](https://github.com/baofff/U-ViT) model architecture, which is similar to a [`Transformer2DModel`] with U-Net-style skip connections between transformer layers. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image and/or text latents. The original UniDiffuser paper uses the [`DPMSolverMultistepScheduler`] scheduler. """ def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, image_encoder: CLIPVisionModelWithProjection, image_processor: CLIPImageProcessor, clip_tokenizer: CLIPTokenizer, text_decoder: UniDiffuserTextDecoder, text_tokenizer: GPT2Tokenizer, unet: UniDiffuserModel, scheduler: KarrasDiffusionSchedulers, ): super().__init__() if text_encoder.config.hidden_size != text_decoder.prefix_inner_dim: raise ValueError( f"The text encoder hidden size and text decoder prefix inner dim must be the same, but" f" `text_encoder.config.hidden_size`: {text_encoder.config.hidden_size} and `text_decoder.prefix_inner_dim`: {text_decoder.prefix_inner_dim}" ) self.register_modules( vae=vae, text_encoder=text_encoder, image_encoder=image_encoder, image_processor=image_processor, clip_tokenizer=clip_tokenizer, text_decoder=text_decoder, text_tokenizer=text_tokenizer, unet=unet, scheduler=scheduler, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.num_channels_latents = vae.config.latent_channels self.text_encoder_seq_len = text_encoder.config.max_position_embeddings self.text_encoder_hidden_size = text_encoder.config.hidden_size self.image_encoder_projection_dim = image_encoder.config.projection_dim self.unet_resolution = unet.config.sample_size self.text_intermediate_dim = self.text_encoder_hidden_size if self.text_decoder.prefix_hidden_dim is not None: self.text_intermediate_dim = self.text_decoder.prefix_hidden_dim self.mode = None # TODO: handle safety checking? self.safety_checker = None # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload # Add self.image_encoder, self.text_decoder to cpu_offloaded_models list def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta')` and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.image_encoder, self.text_decoder]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload # Add self.image_encoder, self.text_decoder to cpu_offloaded_models list def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae, self.image_encoder, self.text_decoder]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def _infer_mode(self, prompt, prompt_embeds, image, latents, prompt_latents, vae_latents, clip_latents): r""" Infer the generation task ('mode') from the inputs to `__call__`. If the mode has been manually set, the set mode will be used. """ prompt_available = (prompt is not None) or (prompt_embeds is not None) image_available = image is not None input_available = prompt_available or image_available prompt_latents_available = prompt_latents is not None vae_latents_available = vae_latents is not None clip_latents_available = clip_latents is not None full_latents_available = latents is not None image_latents_available = vae_latents_available and clip_latents_available all_indv_latents_available = prompt_latents_available and image_latents_available if self.mode is not None: # Preferentially use the mode set by the user mode = self.mode elif prompt_available: mode = "text2img" elif image_available: mode = "img2text" else: # Neither prompt nor image supplied, infer based on availability of latents if full_latents_available or all_indv_latents_available: mode = "joint" elif prompt_latents_available: mode = "text" elif image_latents_available: mode = "img" else: # No inputs or latents available mode = "joint" # Give warnings for ambiguous cases if self.mode is None and prompt_available and image_available: logger.warning( f"You have supplied both a text prompt and image to the pipeline and mode has not been set manually," f" defaulting to mode '{mode}'." ) if self.mode is None and not input_available: if vae_latents_available != clip_latents_available: # Exactly one of vae_latents and clip_latents is supplied logger.warning( f"You have supplied exactly one of `vae_latents` and `clip_latents`, whereas either both or none" f" are expected to be supplied. Defaulting to mode '{mode}'." ) elif not prompt_latents_available and not vae_latents_available and not clip_latents_available: # No inputs or latents supplied logger.warning( f"No inputs or latents have been supplied, and mode has not been manually set," f" defaulting to mode '{mode}'." ) return mode # Functions to manually set the mode def set_text_mode(self): r"""Manually set the generation mode to unconditional ("marginal") text generation.""" self.mode = "text" def set_image_mode(self): r"""Manually set the generation mode to unconditional ("marginal") image generation.""" self.mode = "img" def set_text_to_image_mode(self): r"""Manually set the generation mode to text-conditioned image generation.""" self.mode = "text2img" def set_image_to_text_mode(self): r"""Manually set the generation mode to image-conditioned text generation.""" self.mode = "img2text" def set_joint_mode(self): r"""Manually set the generation mode to unconditional joint image-text generation.""" self.mode = "joint" def reset_mode(self): r"""Removes a manually set mode; after calling this, the pipeline will infer the mode from inputs.""" self.mode = None def _infer_batch_size( self, mode, prompt, prompt_embeds, image, num_images_per_prompt, num_prompts_per_image, latents, prompt_latents, vae_latents, clip_latents, ): r"""Infers the batch size and multiplier depending on mode and supplied arguments to `__call__`.""" if num_images_per_prompt is None: num_images_per_prompt = 1 if num_prompts_per_image is None: num_prompts_per_image = 1 assert num_images_per_prompt > 0, "num_images_per_prompt must be a positive integer" assert num_prompts_per_image > 0, "num_prompts_per_image must be a positive integer" if mode in ["text2img"]: 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: # Either prompt or prompt_embeds must be present for text2img. batch_size = prompt_embeds.shape[0] multiplier = num_images_per_prompt elif mode in ["img2text"]: if isinstance(image, PIL.Image.Image): batch_size = 1 else: # Image must be available and type either PIL.Image.Image or torch.FloatTensor. # Not currently supporting something like image_embeds. batch_size = image.shape[0] multiplier = num_prompts_per_image elif mode in ["img"]: if vae_latents is not None: batch_size = vae_latents.shape[0] elif clip_latents is not None: batch_size = clip_latents.shape[0] else: batch_size = 1 multiplier = num_images_per_prompt elif mode in ["text"]: if prompt_latents is not None: batch_size = prompt_latents.shape[0] else: batch_size = 1 multiplier = num_prompts_per_image elif mode in ["joint"]: if latents is not None: batch_size = latents.shape[0] elif prompt_latents is not None: batch_size = prompt_latents.shape[0] elif vae_latents is not None: batch_size = vae_latents.shape[0] elif clip_latents is not None: batch_size = clip_latents.shape[0] else: batch_size = 1 if num_images_per_prompt == num_prompts_per_image: multiplier = num_images_per_prompt else: multiplier = min(num_images_per_prompt, num_prompts_per_image) logger.warning( f"You are using mode `{mode}` and `num_images_per_prompt`: {num_images_per_prompt} and" f" num_prompts_per_image: {num_prompts_per_image} are not equal. Using batch size equal to" f" `min(num_images_per_prompt, num_prompts_per_image) = {batch_size}." ) return batch_size, multiplier # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt # self.tokenizer => self.clip_tokenizer def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not 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. 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`). 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. """ 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] if prompt_embeds is None: text_inputs = self.clip_tokenizer( prompt, padding="max_length", max_length=self.clip_tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.clip_tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.clip_tokenizer.batch_decode( untruncated_ids[:, self.clip_tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.clip_tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt max_length = prompt_embeds.shape[1] uncond_input = self.clip_tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_instruct_pix2pix.StableDiffusionInstructPix2PixPipeline.prepare_image_latents # Add num_prompts_per_image argument, sample from autoencoder moment distribution def encode_image_vae_latents( self, image, batch_size, num_prompts_per_image, dtype, device, do_classifier_free_guidance, generator=None, ): if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" ) image = image.to(device=device, dtype=dtype) batch_size = batch_size * num_prompts_per_image if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if isinstance(generator, list): image_latents = [ self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i]) * self.vae.config.scaling_factor for i in range(batch_size) ] image_latents = torch.cat(image_latents, dim=0) else: image_latents = self.vae.encode(image).latent_dist.sample(generator=generator) # Scale image_latents by the VAE's scaling factor image_latents = image_latents * self.vae.config.scaling_factor if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0: # expand image_latents for batch_size deprecation_message = ( f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial" " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" " your script to pass as many initial images as text prompts to suppress this warning." ) deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) additional_image_per_prompt = batch_size // image_latents.shape[0] image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0) elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts." ) else: image_latents = torch.cat([image_latents], dim=0) if do_classifier_free_guidance: uncond_image_latents = torch.zeros_like(image_latents) image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0) return image_latents def encode_image_clip_latents( self, image, batch_size, num_prompts_per_image, dtype, device, generator=None, ): # Map image to CLIP embedding. if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" ) preprocessed_image = self.image_processor.preprocess( image, return_tensors="pt", ) preprocessed_image = preprocessed_image.to(device=device, dtype=dtype) batch_size = batch_size * num_prompts_per_image if isinstance(generator, list): image_latents = [ self.image_encoder(**preprocessed_image[i : i + 1]).image_embeds for i in range(batch_size) ] image_latents = torch.cat(image_latents, dim=0) else: image_latents = self.image_encoder(**preprocessed_image).image_embeds if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0: # expand image_latents for batch_size deprecation_message = ( f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial" " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" " your script to pass as many initial images as text prompts to suppress this warning." ) deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) additional_image_per_prompt = batch_size // image_latents.shape[0] image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0) elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts." ) else: image_latents = torch.cat([image_latents], dim=0) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) return image_latents # Note that the CLIP latents are not decoded for image generation. # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents # Rename: decode_latents -> decode_image_latents def decode_image_latents(self, latents): latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image def prepare_text_latents( self, batch_size, num_images_per_prompt, seq_len, hidden_size, dtype, device, generator, latents=None ): # Prepare latents for the CLIP embedded prompt. shape = (batch_size * num_images_per_prompt, seq_len, hidden_size) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: # latents is assumed to have shace (B, L, D) latents = latents.repeat(num_images_per_prompt, 1, 1) latents = latents.to(device=device, dtype=dtype) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents # Rename prepare_latents -> prepare_image_vae_latents and add num_prompts_per_image argument. def prepare_image_vae_latents( self, batch_size, num_prompts_per_image, num_channels_latents, height, width, dtype, device, generator, latents=None, ): shape = ( batch_size * num_prompts_per_image, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor, ) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: # latents is assumed to have shape (B, C, H, W) latents = latents.repeat(num_prompts_per_image, 1, 1, 1) latents = latents.to(device=device, dtype=dtype) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def prepare_image_clip_latents( self, batch_size, num_prompts_per_image, clip_img_dim, dtype, device, generator, latents=None ): # Prepare latents for the CLIP embedded image. shape = (batch_size * num_prompts_per_image, 1, clip_img_dim) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: # latents is assumed to have shape (B, L, D) latents = latents.repeat(num_prompts_per_image, 1, 1) latents = latents.to(device=device, dtype=dtype) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def _split(self, x, height, width): r""" Splits a flattened embedding x of shape (B, C * H * W + clip_img_dim) into two tensors of shape (B, C, H, W) and (B, 1, clip_img_dim) """ batch_size = x.shape[0] latent_height = height // self.vae_scale_factor latent_width = width // self.vae_scale_factor img_vae_dim = self.num_channels_latents * latent_height * latent_width img_vae, img_clip = x.split([img_vae_dim, self.image_encoder_projection_dim], dim=1) img_vae = torch.reshape(img_vae, (batch_size, self.num_channels_latents, latent_height, latent_width)) img_clip = torch.reshape(img_clip, (batch_size, 1, self.image_encoder_projection_dim)) return img_vae, img_clip def _combine(self, img_vae, img_clip): r""" Combines a latent iamge img_vae of shape (B, C, H, W) and a CLIP-embedded image img_clip of shape (B, 1, clip_img_dim) into a single tensor of shape (B, C * H * W + clip_img_dim). """ img_vae = torch.reshape(img_vae, (img_vae.shape[0], -1)) img_clip = torch.reshape(img_clip, (img_clip.shape[0], -1)) return torch.concat([img_vae, img_clip], dim=-1) def _split_joint(self, x, height, width): r""" Splits a flattened embedding x of shape (B, C * H * W + clip_img_dim + text_seq_len * text_dim] into (img_vae, img_clip, text) where img_vae is of shape (B, C, H, W), img_clip is of shape (B, 1, clip_img_dim), and text is of shape (B, text_seq_len, text_dim). """ batch_size = x.shape[0] latent_height = height // self.vae_scale_factor latent_width = width // self.vae_scale_factor img_vae_dim = self.num_channels_latents * latent_height * latent_width text_dim = self.text_encoder_seq_len * self.text_intermediate_dim img_vae, img_clip, text = x.split([img_vae_dim, self.image_encoder_projection_dim, text_dim], dim=1) img_vae = torch.reshape(img_vae, (batch_size, self.num_channels_latents, latent_height, latent_width)) img_clip = torch.reshape(img_clip, (batch_size, 1, self.image_encoder_projection_dim)) text = torch.reshape(text, (batch_size, self.text_encoder_seq_len, self.text_intermediate_dim)) return img_vae, img_clip, text def _combine_joint(self, img_vae, img_clip, text): r""" Combines a latent image img_vae of shape (B, C, H, W), a CLIP-embedded image img_clip of shape (B, L_img, clip_img_dim), and a text embedding text of shape (B, L_text, text_dim) into a single embedding x of shape (B, C * H * W + L_img * clip_img_dim + L_text * text_dim). """ img_vae = torch.reshape(img_vae, (img_vae.shape[0], -1)) img_clip = torch.reshape(img_clip, (img_clip.shape[0], -1)) text = torch.reshape(text, (text.shape[0], -1)) return torch.concat([img_vae, img_clip, text], dim=-1) def _get_noise_pred( self, mode, latents, t, prompt_embeds, img_vae, img_clip, max_timestep, data_type, guidance_scale, generator, device, height, width, ): r""" Gets the noise prediction using the `unet` and performs classifier-free guidance, if necessary. """ if mode == "joint": # Joint text-image generation img_vae_latents, img_clip_latents, text_latents = self._split_joint(latents, height, width) img_vae_out, img_clip_out, text_out = self.unet( img_vae_latents, img_clip_latents, text_latents, timestep_img=t, timestep_text=t, data_type=data_type ) x_out = self._combine_joint(img_vae_out, img_clip_out, text_out) if guidance_scale <= 1.0: return x_out # Classifier-free guidance img_vae_T = randn_tensor(img_vae.shape, generator=generator, device=device, dtype=img_vae.dtype) img_clip_T = randn_tensor(img_clip.shape, generator=generator, device=device, dtype=img_clip.dtype) text_T = randn_tensor(prompt_embeds.shape, generator=generator, device=device, dtype=prompt_embeds.dtype) _, _, text_out_uncond = self.unet( img_vae_T, img_clip_T, text_latents, timestep_img=max_timestep, timestep_text=t, data_type=data_type ) img_vae_out_uncond, img_clip_out_uncond, _ = self.unet( img_vae_latents, img_clip_latents, text_T, timestep_img=t, timestep_text=max_timestep, data_type=data_type, ) x_out_uncond = self._combine_joint(img_vae_out_uncond, img_clip_out_uncond, text_out_uncond) return guidance_scale * x_out + (1.0 - guidance_scale) * x_out_uncond elif mode == "text2img": # Text-conditioned image generation img_vae_latents, img_clip_latents = self._split(latents, height, width) img_vae_out, img_clip_out, text_out = self.unet( img_vae_latents, img_clip_latents, prompt_embeds, timestep_img=t, timestep_text=0, data_type=data_type ) img_out = self._combine(img_vae_out, img_clip_out) if guidance_scale <= 1.0: return img_out # Classifier-free guidance text_T = randn_tensor(prompt_embeds.shape, generator=generator, device=device, dtype=prompt_embeds.dtype) img_vae_out_uncond, img_clip_out_uncond, text_out_uncond = self.unet( img_vae_latents, img_clip_latents, text_T, timestep_img=t, timestep_text=max_timestep, data_type=data_type, ) img_out_uncond = self._combine(img_vae_out_uncond, img_clip_out_uncond) return guidance_scale * img_out + (1.0 - guidance_scale) * img_out_uncond elif mode == "img2text": # Image-conditioned text generation img_vae_out, img_clip_out, text_out = self.unet( img_vae, img_clip, latents, timestep_img=0, timestep_text=t, data_type=data_type ) if guidance_scale <= 1.0: return text_out # Classifier-free guidance img_vae_T = randn_tensor(img_vae.shape, generator=generator, device=device, dtype=img_vae.dtype) img_clip_T = randn_tensor(img_clip.shape, generator=generator, device=device, dtype=img_clip.dtype) img_vae_out_uncond, img_clip_out_uncond, text_out_uncond = self.unet( img_vae_T, img_clip_T, latents, timestep_img=max_timestep, timestep_text=t, data_type=data_type ) return guidance_scale * text_out + (1.0 - guidance_scale) * text_out_uncond elif mode == "text": # Unconditional ("marginal") text generation (no CFG) img_vae_out, img_clip_out, text_out = self.unet( img_vae, img_clip, latents, timestep_img=max_timestep, timestep_text=t, data_type=data_type ) return text_out elif mode == "img": # Unconditional ("marginal") image generation (no CFG) img_vae_latents, img_clip_latents = self._split(latents, height, width) img_vae_out, img_clip_out, text_out = self.unet( img_vae_latents, img_clip_latents, prompt_embeds, timestep_img=t, timestep_text=max_timestep, data_type=data_type, ) img_out = self._combine(img_vae_out, img_clip_out) return img_out def check_latents_shape(self, latents_name, latents, expected_shape): latents_shape = latents.shape expected_num_dims = len(expected_shape) + 1 # expected dimensions plus the batch dimension expected_shape_str = ", ".join(str(dim) for dim in expected_shape) if len(latents_shape) != expected_num_dims: raise ValueError( f"`{latents_name}` should have shape (batch_size, {expected_shape_str}), but the current shape" f" {latents_shape} has {len(latents_shape)} dimensions." ) for i in range(1, expected_num_dims): if latents_shape[i] != expected_shape[i - 1]: raise ValueError( f"`{latents_name}` should have shape (batch_size, {expected_shape_str}), but the current shape" f" {latents_shape} has {latents_shape[i]} != {expected_shape[i - 1]} at dimension {i}." ) def check_inputs( self, mode, prompt, image, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, latents=None, prompt_latents=None, vae_latents=None, clip_latents=None, ): # Check inputs before running the generative process. if height % self.vae_scale_factor != 0 or width % self.vae_scale_factor != 0: raise ValueError( f"`height` and `width` have to be divisible by {self.vae_scale_factor} but are {height} and {width}." ) if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if mode == "text2img": if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) if mode == "img2text": if image is None: raise ValueError("`img2text` mode requires an image to be provided.") # Check provided latents latent_height = height // self.vae_scale_factor latent_width = width // self.vae_scale_factor full_latents_available = latents is not None prompt_latents_available = prompt_latents is not None vae_latents_available = vae_latents is not None clip_latents_available = clip_latents is not None if full_latents_available: individual_latents_available = ( prompt_latents is not None or vae_latents is not None or clip_latents is not None ) if individual_latents_available: logger.warning( "You have supplied both `latents` and at least one of `prompt_latents`, `vae_latents`, and" " `clip_latents`. The value of `latents` will override the value of any individually supplied latents." ) # Check shape of full latents img_vae_dim = self.num_channels_latents * latent_height * latent_width text_dim = self.text_encoder_seq_len * self.text_encoder_hidden_size latents_dim = img_vae_dim + self.image_encoder_projection_dim + text_dim latents_expected_shape = (latents_dim,) self.check_latents_shape("latents", latents, latents_expected_shape) # Check individual latent shapes, if present if prompt_latents_available: prompt_latents_expected_shape = (self.text_encoder_seq_len, self.text_encoder_hidden_size) self.check_latents_shape("prompt_latents", prompt_latents, prompt_latents_expected_shape) if vae_latents_available: vae_latents_expected_shape = (self.num_channels_latents, latent_height, latent_width) self.check_latents_shape("vae_latents", vae_latents, vae_latents_expected_shape) if clip_latents_available: clip_latents_expected_shape = (1, self.image_encoder_projection_dim) self.check_latents_shape("clip_latents", clip_latents, clip_latents_expected_shape) if mode in ["text2img", "img"] and vae_latents_available and clip_latents_available: if vae_latents.shape[0] != clip_latents.shape[0]: raise ValueError( f"Both `vae_latents` and `clip_latents` are supplied, but their batch dimensions are not equal:" f" {vae_latents.shape[0]} != {clip_latents.shape[0]}." ) if mode == "joint" and prompt_latents_available and vae_latents_available and clip_latents_available: if prompt_latents.shape[0] != vae_latents.shape[0] or prompt_latents.shape[0] != clip_latents.shape[0]: raise ValueError( f"All of `prompt_latents`, `vae_latents`, and `clip_latents` are supplied, but their batch" f" dimensions are not equal: {prompt_latents.shape[0]} != {vae_latents.shape[0]}" f" != {clip_latents.shape[0]}." ) @torch.no_grad() def __call__( self, prompt: Optional[Union[str, List[str]]] = None, image: Optional[Union[torch.FloatTensor, PIL.Image.Image]] = None, height: Optional[int] = None, width: Optional[int] = None, data_type: Optional[int] = 1, num_inference_steps: int = 50, guidance_scale: float = 8.0, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, num_prompts_per_image: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_latents: Optional[torch.FloatTensor] = None, vae_latents: Optional[torch.FloatTensor] = None, clip_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, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, ): 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. Required for text-conditioned image generation (`text2img`) mode. image (`torch.FloatTensor` or `PIL.Image.Image`, *optional*): `Image`, or tensor representing an image batch. Required for image-conditioned text generation (`img2text`) mode. height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The height in pixels of the generated image. width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The width in pixels of the generated image. data_type (`int`, *optional*, defaults to 1): The data type (either 0 or 1). Only used if you are loading a checkpoint which supports a data type embedding; this is added for compatibility with the UniDiffuser-v1 checkpoint. 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. guidance_scale (`float`, *optional*, defaults to 8.0): 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. Note that the original [UniDiffuser paper](https://arxiv.org/pdf/2303.06555.pdf) uses a different definition of the guidance scale `w'`, which satisfies `w = w' + 1`. 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`). Used in text-conditioned image generation (`text2img`) mode. num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. Used in `text2img` (text-conditioned image generation) and `img` mode. If the mode is joint and both `num_images_per_prompt` and `num_prompts_per_image` are supplied, `min(num_images_per_prompt, num_prompts_per_image)` samples will be generated. num_prompts_per_image (`int`, *optional*, defaults to 1): The number of prompts to generate per image. Used in `img2text` (image-conditioned text generation) and `text` mode. If the mode is joint and both `num_images_per_prompt` and `num_prompts_per_image` are supplied, `min(num_images_per_prompt, num_prompts_per_image)` samples will be generated. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. 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 joint image-text generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will be generated by sampling using the supplied random `generator`. Note that this is assumed to be a full set of VAE, CLIP, and text latents, if supplied, this will override the value of `prompt_latents`, `vae_latents`, and `clip_latents`. prompt_latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for text generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will be generated by sampling using the supplied random `generator`. vae_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 be generated by sampling using the supplied random `generator`. clip_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 be 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. Used in text-conditioned image generation (`text2img`) mode. 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. Used in text-conditioned image generation (`text2img`) mode. 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.unidiffuser.ImageTextPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Returns: [`~pipelines.unidiffuser.ImageTextPipelineOutput`] or `tuple`: [`pipelines.unidiffuser.ImageTextPipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images, and the second element is a list of generated texts. """ # 0. Default height and width to unet height = height or self.unet_resolution * self.vae_scale_factor width = width or self.unet_resolution * self.vae_scale_factor # 1. Check inputs # Recalculate mode for each call to the pipeline. mode = self._infer_mode(prompt, prompt_embeds, image, latents, prompt_latents, vae_latents, clip_latents) self.check_inputs( mode, prompt, image, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, latents, prompt_latents, vae_latents, clip_latents, ) # 2. Define call parameters batch_size, multiplier = self._infer_batch_size( mode, prompt, prompt_embeds, image, num_images_per_prompt, num_prompts_per_image, latents, prompt_latents, vae_latents, clip_latents, ) device = self._execution_device reduce_text_emb_dim = self.text_intermediate_dim < self.text_encoder_hidden_size or self.mode != "text2img" # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. # Note that this differs from the formulation in the unidiffusers paper! # do_classifier_free_guidance = guidance_scale > 1.0 # check if scheduler is in sigmas space # scheduler_is_in_sigma_space = hasattr(self.scheduler, "sigmas") # 3. Encode input prompt, if available; otherwise prepare text latents if latents is not None: # Overwrite individual latents vae_latents, clip_latents, prompt_latents = self._split_joint(latents, height, width) if mode in ["text2img"]: # 3.1. Encode input prompt, if available assert prompt is not None or prompt_embeds is not None prompt_embeds = self._encode_prompt( prompt=prompt, device=device, num_images_per_prompt=multiplier, do_classifier_free_guidance=False, # don't support standard classifier-free guidance for now negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) else: # 3.2. Prepare text latent variables, if input not available prompt_embeds = self.prepare_text_latents( batch_size=batch_size, num_images_per_prompt=multiplier, seq_len=self.text_encoder_seq_len, hidden_size=self.text_encoder_hidden_size, dtype=self.text_encoder.dtype, # Should work with both full precision and mixed precision device=device, generator=generator, latents=prompt_latents, ) if reduce_text_emb_dim: prompt_embeds = self.text_decoder.encode(prompt_embeds) # 4. Encode image, if available; otherwise prepare image latents if mode in ["img2text"]: # 4.1. Encode images, if available assert image is not None, "`img2text` requires a conditioning image" # Encode image using VAE image_vae = preprocess(image) height, width = image_vae.shape[-2:] image_vae_latents = self.encode_image_vae_latents( image=image_vae, batch_size=batch_size, num_prompts_per_image=multiplier, dtype=prompt_embeds.dtype, device=device, do_classifier_free_guidance=False, # Copied from InstructPix2Pix, don't use their version of CFG generator=generator, ) # Encode image using CLIP image_clip_latents = self.encode_image_clip_latents( image=image, batch_size=batch_size, num_prompts_per_image=multiplier, dtype=prompt_embeds.dtype, device=device, generator=generator, ) # (batch_size, clip_hidden_size) => (batch_size, 1, clip_hidden_size) image_clip_latents = image_clip_latents.unsqueeze(1) else: # 4.2. Prepare image latent variables, if input not available # Prepare image VAE latents in latent space image_vae_latents = self.prepare_image_vae_latents( batch_size=batch_size, num_prompts_per_image=multiplier, num_channels_latents=self.num_channels_latents, height=height, width=width, dtype=prompt_embeds.dtype, device=device, generator=generator, latents=vae_latents, ) # Prepare image CLIP latents image_clip_latents = self.prepare_image_clip_latents( batch_size=batch_size, num_prompts_per_image=multiplier, clip_img_dim=self.image_encoder_projection_dim, dtype=prompt_embeds.dtype, device=device, generator=generator, latents=clip_latents, ) # 5. Set timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # max_timestep = timesteps[0] max_timestep = self.scheduler.config.num_train_timesteps # 6. Prepare latent variables if mode == "joint": latents = self._combine_joint(image_vae_latents, image_clip_latents, prompt_embeds) elif mode in ["text2img", "img"]: latents = self._combine(image_vae_latents, image_clip_latents) elif mode in ["img2text", "text"]: latents = prompt_embeds # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) logger.debug(f"Scheduler extra step kwargs: {extra_step_kwargs}") # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # predict the noise residual # Also applies classifier-free guidance as described in the UniDiffuser paper noise_pred = self._get_noise_pred( mode, latents, t, prompt_embeds, image_vae_latents, image_clip_latents, max_timestep, data_type, guidance_scale, generator, device, height, width, ) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # 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 callback is not None and i % callback_steps == 0: callback(i, t, latents) # 9. Post-processing gen_image = None gen_text = None if mode == "joint": image_vae_latents, image_clip_latents, text_latents = self._split_joint(latents, height, width) # Map latent VAE image back to pixel space gen_image = self.decode_image_latents(image_vae_latents) # Generate text using the text decoder output_token_list, seq_lengths = self.text_decoder.generate_captions( text_latents, self.text_tokenizer.eos_token_id, device=device ) output_list = output_token_list.cpu().numpy() gen_text = [ self.text_tokenizer.decode(output[: int(length)], skip_special_tokens=True) for output, length in zip(output_list, seq_lengths) ] elif mode in ["text2img", "img"]: image_vae_latents, image_clip_latents = self._split(latents, height, width) gen_image = self.decode_image_latents(image_vae_latents) elif mode in ["img2text", "text"]: text_latents = latents output_token_list, seq_lengths = self.text_decoder.generate_captions( text_latents, self.text_tokenizer.eos_token_id, device=device ) output_list = output_token_list.cpu().numpy() gen_text = [ self.text_tokenizer.decode(output[: int(length)], skip_special_tokens=True) for output, length in zip(output_list, seq_lengths) ] # 10. Convert to PIL if output_type == "pil" and gen_image is not None: gen_image = self.numpy_to_pil(gen_image) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (gen_image, gen_text) return ImageTextPipelineOutput(images=gen_image, text=gen_text)