import inspect from typing import List, Optional, Union import PIL import torch from torch.nn import functional as F from transformers import ( CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection, ) from diffusers import ( DiffusionPipeline, ImagePipelineOutput, UnCLIPScheduler, UNet2DConditionModel, UNet2DModel, ) from diffusers.pipelines.unclip import UnCLIPTextProjModel from diffusers.utils import is_accelerate_available, logging, randn_tensor logger = logging.get_logger(__name__) # pylint: disable=invalid-name def slerp(val, low, high): """ Find the interpolation point between the 'low' and 'high' values for the given 'val'. See https://en.wikipedia.org/wiki/Slerp for more details on the topic. """ low_norm = low / torch.norm(low) high_norm = high / torch.norm(high) omega = torch.acos((low_norm * high_norm)) so = torch.sin(omega) res = (torch.sin((1.0 - val) * omega) / so) * low + (torch.sin(val * omega) / so) * high return res class UnCLIPImageInterpolationPipeline(DiffusionPipeline): """ Pipeline to generate variations from an input image using unCLIP 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: text_encoder ([`CLIPTextModelWithProjection`]): Frozen text-encoder. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `image_encoder`. image_encoder ([`CLIPVisionModelWithProjection`]): Frozen CLIP image-encoder. unCLIP Image Variation uses the vision portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. text_proj ([`UnCLIPTextProjModel`]): Utility class to prepare and combine the embeddings before they are passed to the decoder. decoder ([`UNet2DConditionModel`]): The decoder to invert the image embedding into an image. super_res_first ([`UNet2DModel`]): Super resolution unet. Used in all but the last step of the super resolution diffusion process. super_res_last ([`UNet2DModel`]): Super resolution unet. Used in the last step of the super resolution diffusion process. decoder_scheduler ([`UnCLIPScheduler`]): Scheduler used in the decoder denoising process. Just a modified DDPMScheduler. super_res_scheduler ([`UnCLIPScheduler`]): Scheduler used in the super resolution denoising process. Just a modified DDPMScheduler. """ decoder: UNet2DConditionModel text_proj: UnCLIPTextProjModel text_encoder: CLIPTextModelWithProjection tokenizer: CLIPTokenizer feature_extractor: CLIPImageProcessor image_encoder: CLIPVisionModelWithProjection super_res_first: UNet2DModel super_res_last: UNet2DModel decoder_scheduler: UnCLIPScheduler super_res_scheduler: UnCLIPScheduler # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline.__init__ def __init__( self, decoder: UNet2DConditionModel, text_encoder: CLIPTextModelWithProjection, tokenizer: CLIPTokenizer, text_proj: UnCLIPTextProjModel, feature_extractor: CLIPImageProcessor, image_encoder: CLIPVisionModelWithProjection, super_res_first: UNet2DModel, super_res_last: UNet2DModel, decoder_scheduler: UnCLIPScheduler, super_res_scheduler: UnCLIPScheduler, ): super().__init__() self.register_modules( decoder=decoder, text_encoder=text_encoder, tokenizer=tokenizer, text_proj=text_proj, feature_extractor=feature_extractor, image_encoder=image_encoder, super_res_first=super_res_first, super_res_last=super_res_last, decoder_scheduler=decoder_scheduler, super_res_scheduler=super_res_scheduler, ) # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents def prepare_latents(self, shape, dtype, device, generator, latents, scheduler): if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: if latents.shape != shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") latents = latents.to(device) latents = latents * scheduler.init_noise_sigma return latents # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline._encode_prompt def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance): batch_size = len(prompt) if isinstance(prompt, list) else 1 # get prompt text embeddings text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, return_tensors="pt", ) text_input_ids = text_inputs.input_ids text_mask = text_inputs.attention_mask.bool().to(device) text_encoder_output = self.text_encoder(text_input_ids.to(device)) prompt_embeds = text_encoder_output.text_embeds text_encoder_hidden_states = text_encoder_output.last_hidden_state prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0) text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0) text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0) if do_classifier_free_guidance: uncond_tokens = [""] * batch_size max_length = text_input_ids.shape[-1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) uncond_text_mask = uncond_input.attention_mask.bool().to(device) negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device)) negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state # 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.repeat(1, num_images_per_prompt) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len) seq_len = uncond_text_encoder_hidden_states.shape[1] uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1) uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view( batch_size * num_images_per_prompt, seq_len, -1 ) uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0) # done duplicates # 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]) text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]) text_mask = torch.cat([uncond_text_mask, text_mask]) return prompt_embeds, text_encoder_hidden_states, text_mask # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline._encode_image def _encode_image(self, image, device, num_images_per_prompt, image_embeddings: Optional[torch.Tensor] = None): dtype = next(self.image_encoder.parameters()).dtype if image_embeddings is None: if not isinstance(image, torch.Tensor): image = self.feature_extractor(images=image, return_tensors="pt").pixel_values image = image.to(device=device, dtype=dtype) image_embeddings = self.image_encoder(image).image_embeds image_embeddings = image_embeddings.repeat_interleave(num_images_per_prompt, dim=0) return image_embeddings # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's models 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. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") models = [ self.decoder, self.text_proj, self.text_encoder, self.super_res_first, self.super_res_last, ] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) @property # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._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 self.device != torch.device("meta") or not hasattr(self.decoder, "_hf_hook"): return self.device for module in self.decoder.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 @torch.no_grad() def __call__( self, image: Optional[Union[List[PIL.Image.Image], torch.FloatTensor]] = None, steps: int = 5, decoder_num_inference_steps: int = 25, super_res_num_inference_steps: int = 7, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, image_embeddings: Optional[torch.Tensor] = None, decoder_latents: Optional[torch.FloatTensor] = None, super_res_latents: Optional[torch.FloatTensor] = None, decoder_guidance_scale: float = 8.0, output_type: Optional[str] = "pil", return_dict: bool = True, ): """ Function invoked when calling the pipeline for generation. Args: image (`List[PIL.Image.Image]` or `torch.FloatTensor`): The images to use for the image interpolation. Only accepts a list of two PIL Images or If you provide a tensor, it needs to comply with the configuration of [this](https://huggingface.co/fusing/karlo-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json) `CLIPImageProcessor` while still having a shape of two in the 0th dimension. Can be left to `None` only when `image_embeddings` are passed. steps (`int`, *optional*, defaults to 5): The number of interpolation images to generate. decoder_num_inference_steps (`int`, *optional*, defaults to 25): The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality image at the expense of slower inference. super_res_num_inference_steps (`int`, *optional*, defaults to 7): The number of denoising steps for super resolution. More denoising steps usually lead to a higher quality image at the expense of slower inference. 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. image_embeddings (`torch.Tensor`, *optional*): Pre-defined image embeddings that can be derived from the image encoder. Pre-defined image embeddings can be passed for tasks like image interpolations. `image` can the be left to `None`. decoder_latents (`torch.FloatTensor` of shape (batch size, channels, height, width), *optional*): Pre-generated noisy latents to be used as inputs for the decoder. super_res_latents (`torch.FloatTensor` of shape (batch size, channels, super res height, super res width), *optional*): Pre-generated noisy latents to be used as inputs for the decoder. decoder_guidance_scale (`float`, *optional*, defaults to 4.0): 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. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated 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.ImagePipelineOutput`] instead of a plain tuple. """ batch_size = steps device = self._execution_device if isinstance(image, List): if len(image) != 2: raise AssertionError( f"Expected 'image' List to be of size 2, but passed 'image' length is {len(image)}" ) elif not (isinstance(image[0], PIL.Image.Image) and isinstance(image[0], PIL.Image.Image)): raise AssertionError( f"Expected 'image' List to contain PIL.Image.Image, but passed 'image' contents are {type(image[0])} and {type(image[1])}" ) elif isinstance(image, torch.FloatTensor): if image.shape[0] != 2: raise AssertionError( f"Expected 'image' to be torch.FloatTensor of shape 2 in 0th dimension, but passed 'image' size is {image.shape[0]}" ) elif isinstance(image_embeddings, torch.Tensor): if image_embeddings.shape[0] != 2: raise AssertionError( f"Expected 'image_embeddings' to be torch.FloatTensor of shape 2 in 0th dimension, but passed 'image_embeddings' shape is {image_embeddings.shape[0]}" ) else: raise AssertionError( f"Expected 'image' or 'image_embeddings' to be not None with types List[PIL.Image] or Torch.FloatTensor respectively. Received {type(image)} and {type(image_embeddings)} repsectively" ) original_image_embeddings = self._encode_image( image=image, device=device, num_images_per_prompt=1, image_embeddings=image_embeddings ) image_embeddings = [] for interp_step in torch.linspace(0, 1, steps): temp_image_embeddings = slerp( interp_step, original_image_embeddings[0], original_image_embeddings[1] ).unsqueeze(0) image_embeddings.append(temp_image_embeddings) image_embeddings = torch.cat(image_embeddings).to(device) do_classifier_free_guidance = decoder_guidance_scale > 1.0 prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt( prompt=["" for i in range(steps)], device=device, num_images_per_prompt=1, do_classifier_free_guidance=do_classifier_free_guidance, ) text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj( image_embeddings=image_embeddings, prompt_embeds=prompt_embeds, text_encoder_hidden_states=text_encoder_hidden_states, do_classifier_free_guidance=do_classifier_free_guidance, ) if device.type == "mps": # HACK: MPS: There is a panic when padding bool tensors, # so cast to int tensor for the pad and back to bool afterwards text_mask = text_mask.type(torch.int) decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1) decoder_text_mask = decoder_text_mask.type(torch.bool) else: decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True) self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device) decoder_timesteps_tensor = self.decoder_scheduler.timesteps num_channels_latents = self.decoder.in_channels height = self.decoder.sample_size width = self.decoder.sample_size decoder_latents = self.prepare_latents( (batch_size, num_channels_latents, height, width), text_encoder_hidden_states.dtype, device, generator, decoder_latents, self.decoder_scheduler, ) for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents noise_pred = self.decoder( sample=latent_model_input, timestep=t, encoder_hidden_states=text_encoder_hidden_states, class_labels=additive_clip_time_embeddings, attention_mask=decoder_text_mask, ).sample if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1) noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1) noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond) noise_pred = torch.cat([noise_pred, predicted_variance], dim=1) if i + 1 == decoder_timesteps_tensor.shape[0]: prev_timestep = None else: prev_timestep = decoder_timesteps_tensor[i + 1] # compute the previous noisy sample x_t -> x_t-1 decoder_latents = self.decoder_scheduler.step( noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator ).prev_sample decoder_latents = decoder_latents.clamp(-1, 1) image_small = decoder_latents # done decoder # super res self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device) super_res_timesteps_tensor = self.super_res_scheduler.timesteps channels = self.super_res_first.in_channels // 2 height = self.super_res_first.sample_size width = self.super_res_first.sample_size super_res_latents = self.prepare_latents( (batch_size, channels, height, width), image_small.dtype, device, generator, super_res_latents, self.super_res_scheduler, ) if device.type == "mps": # MPS does not support many interpolations image_upscaled = F.interpolate(image_small, size=[height, width]) else: interpolate_antialias = {} if "antialias" in inspect.signature(F.interpolate).parameters: interpolate_antialias["antialias"] = True image_upscaled = F.interpolate( image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias ) for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)): # no classifier free guidance if i == super_res_timesteps_tensor.shape[0] - 1: unet = self.super_res_last else: unet = self.super_res_first latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1) noise_pred = unet( sample=latent_model_input, timestep=t, ).sample if i + 1 == super_res_timesteps_tensor.shape[0]: prev_timestep = None else: prev_timestep = super_res_timesteps_tensor[i + 1] # compute the previous noisy sample x_t -> x_t-1 super_res_latents = self.super_res_scheduler.step( noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator ).prev_sample image = super_res_latents # done super res # post processing image = image * 0.5 + 0.5 image = image.clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image,) return ImagePipelineOutput(images=image)