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| # Copyright 2022 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. | |
| import inspect | |
| from typing import Callable, List, Optional, Union | |
| import numpy as np | |
| import torch | |
| import PIL | |
| from diffusers.utils import is_accelerate_available | |
| from transformers import CLIPTextModel, CLIPTokenizer | |
| from ...models import AutoencoderKL, UNet2DConditionModel | |
| from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput | |
| from ...schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| def preprocess(image): | |
| # resize to multiple of 64 | |
| width, height = image.size | |
| width = width - width % 64 | |
| height = height - height % 64 | |
| image = image.resize((width, height)) | |
| image = np.array(image.convert("RGB")) | |
| image = image[None].transpose(0, 3, 1, 2) | |
| image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 | |
| return image | |
| class StableDiffusionUpscalePipeline(DiffusionPipeline): | |
| r""" | |
| Pipeline for text-guided image super-resolution using Stable Diffusion 2. | |
| 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. | |
| text_encoder ([`CLIPTextModel`]): | |
| Frozen text-encoder. Stable Diffusion uses the text portion of | |
| [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically | |
| the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. | |
| tokenizer (`CLIPTokenizer`): | |
| Tokenizer of class | |
| [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). | |
| unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. | |
| low_res_scheduler ([`SchedulerMixin`]): | |
| A scheduler used to add initial noise to the low res conditioning image. It must be an instance of | |
| [`DDPMScheduler`]. | |
| scheduler ([`SchedulerMixin`]): | |
| A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of | |
| [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. | |
| """ | |
| def __init__( | |
| self, | |
| vae: AutoencoderKL, | |
| text_encoder: CLIPTextModel, | |
| tokenizer: CLIPTokenizer, | |
| unet: UNet2DConditionModel, | |
| low_res_scheduler: DDPMScheduler, | |
| scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], | |
| max_noise_level: int = 350, | |
| ): | |
| super().__init__() | |
| self.register_modules( | |
| vae=vae, | |
| text_encoder=text_encoder, | |
| tokenizer=tokenizer, | |
| unet=unet, | |
| low_res_scheduler=low_res_scheduler, | |
| scheduler=scheduler, | |
| ) | |
| self.register_to_config(max_noise_level=max_noise_level) | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_attention_slicing | |
| def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"): | |
| r""" | |
| Enable sliced attention computation. | |
| When this option is enabled, the attention module will split the input tensor in slices, to compute attention | |
| in several steps. This is useful to save some memory in exchange for a small speed decrease. | |
| Args: | |
| slice_size (`str` or `int`, *optional*, defaults to `"auto"`): | |
| When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If | |
| a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, | |
| `attention_head_dim` must be a multiple of `slice_size`. | |
| """ | |
| if slice_size == "auto": | |
| if isinstance(self.unet.config.attention_head_dim, int): | |
| # half the attention head size is usually a good trade-off between | |
| # speed and memory | |
| slice_size = self.unet.config.attention_head_dim // 2 | |
| else: | |
| # if `attention_head_dim` is a list, take the smallest head size | |
| slice_size = min(self.unet.config.attention_head_dim) | |
| self.unet.set_attention_slice(slice_size) | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_attention_slicing | |
| def disable_attention_slicing(self): | |
| r""" | |
| Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go | |
| back to computing attention in one step. | |
| """ | |
| # set slice_size = `None` to disable `attention slicing` | |
| self.enable_attention_slicing(None) | |
| 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. | |
| """ | |
| 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}") | |
| for cpu_offloaded_model in [self.unet, self.text_encoder]: | |
| if cpu_offloaded_model is not None: | |
| cpu_offload(cpu_offloaded_model, device) | |
| # 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 self.device != torch.device("meta") or 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._encode_prompt | |
| def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt): | |
| r""" | |
| Encodes the prompt into text encoder hidden states. | |
| Args: | |
| prompt (`str` or `list(int)`): | |
| 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]`): | |
| The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored | |
| if `guidance_scale` is less than `1`). | |
| """ | |
| batch_size = len(prompt) if isinstance(prompt, list) else 1 | |
| text_inputs = self.tokenizer( | |
| prompt, | |
| padding="max_length", | |
| max_length=self.tokenizer.model_max_length, | |
| truncation=True, | |
| return_tensors="pt", | |
| ) | |
| text_input_ids = text_inputs.input_ids | |
| untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids | |
| if not torch.equal(text_input_ids, untruncated_ids): | |
| removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.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.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 | |
| text_embeddings = self.text_encoder( | |
| text_input_ids.to(device), | |
| attention_mask=attention_mask, | |
| ) | |
| text_embeddings = text_embeddings[0] | |
| # duplicate text embeddings for each generation per prompt, using mps friendly method | |
| bs_embed, seq_len, _ = text_embeddings.shape | |
| text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1) | |
| text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) | |
| # get unconditional embeddings for classifier free guidance | |
| if do_classifier_free_guidance: | |
| 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 = text_input_ids.shape[-1] | |
| uncond_input = self.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 | |
| uncond_embeddings = self.text_encoder( | |
| uncond_input.input_ids.to(device), | |
| attention_mask=attention_mask, | |
| ) | |
| uncond_embeddings = uncond_embeddings[0] | |
| # duplicate unconditional embeddings for each generation per prompt, using mps friendly method | |
| seq_len = uncond_embeddings.shape[1] | |
| uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1) | |
| uncond_embeddings = uncond_embeddings.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 | |
| text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) | |
| return text_embeddings | |
| # 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 | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents with 0.18215->0.08333 | |
| def decode_latents(self, latents): | |
| latents = 1 / 0.08333 * latents | |
| image = self.vae.decode(latents).sample | |
| image = (image / 2 + 0.5).clamp(0, 1) | |
| # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 | |
| image = image.cpu().permute(0, 2, 3, 1).float().numpy() | |
| return image | |
| def check_inputs(self, prompt, image, noise_level, callback_steps): | |
| if 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 ( | |
| not isinstance(image, torch.Tensor) | |
| and not isinstance(image, PIL.Image.Image) | |
| and not isinstance(image, list) | |
| ): | |
| raise ValueError( | |
| f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or `list` but is {type(image)}" | |
| ) | |
| # verify batch size of prompt and image are same if image is a list or tensor | |
| if isinstance(image, list) or isinstance(image, torch.Tensor): | |
| if isinstance(prompt, str): | |
| batch_size = 1 | |
| else: | |
| batch_size = len(prompt) | |
| if isinstance(image, list): | |
| image_batch_size = len(image) | |
| else: | |
| image_batch_size = image.shape[0] | |
| if batch_size != image_batch_size: | |
| raise ValueError( | |
| f"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}." | |
| " Please make sure that passed `prompt` matches the batch size of `image`." | |
| ) | |
| # check noise level | |
| if noise_level > self.config.max_noise_level: | |
| raise ValueError(f"`noise_level` has to be <= {self.config.max_noise_level} but is {noise_level}") | |
| 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)}." | |
| ) | |
| def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): | |
| shape = (batch_size, num_channels_latents, height, width) | |
| if latents is None: | |
| if device.type == "mps": | |
| # randn does not work reproducibly on mps | |
| latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device) | |
| else: | |
| latents = torch.randn(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) | |
| # scale the initial noise by the standard deviation required by the scheduler | |
| latents = latents * self.scheduler.init_noise_sigma | |
| return latents | |
| def __call__( | |
| self, | |
| prompt: Union[str, List[str]], | |
| image: Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]], | |
| num_inference_steps: int = 75, | |
| guidance_scale: float = 9.0, | |
| noise_level: int = 20, | |
| negative_prompt: Optional[Union[str, List[str]]] = None, | |
| num_images_per_prompt: Optional[int] = 1, | |
| eta: float = 0.0, | |
| generator: Optional[torch.Generator] = None, | |
| latents: Optional[torch.FloatTensor] = None, | |
| output_type: Optional[str] = "pil", | |
| return_dict: bool = True, | |
| callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, | |
| callback_steps: Optional[int] = 1, | |
| ): | |
| r""" | |
| Function invoked when calling the pipeline for generation. | |
| Args: | |
| prompt (`str` or `List[str]`): | |
| The prompt or prompts to guide the image generation. | |
| image (`PIL.Image.Image` or List[`PIL.Image.Image`] or `torch.FloatTensor`): | |
| `Image`, or tensor representing an image batch which will be upscaled. * | |
| 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 7.5): | |
| 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. 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. | |
| 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`, *optional*): | |
| A [torch generator](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`. | |
| 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.StableDiffusionPipelineOutput`] 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.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: | |
| [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] 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 `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" | |
| (nsfw) content, according to the `safety_checker`. | |
| """ | |
| # 1. Check inputs | |
| self.check_inputs(prompt, image, noise_level, callback_steps) | |
| # 2. Define call parameters | |
| batch_size = 1 if isinstance(prompt, str) else len(prompt) | |
| device = self._execution_device | |
| # 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. | |
| do_classifier_free_guidance = guidance_scale > 1.0 | |
| # 3. Encode input prompt | |
| text_embeddings = self._encode_prompt( | |
| prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt | |
| ) | |
| # 4. Preprocess image | |
| image = [image] if isinstance(image, PIL.Image.Image) else image | |
| if isinstance(image, list): | |
| image = [preprocess(img) for img in image] | |
| image = torch.cat(image, dim=0) | |
| image = image.to(dtype=text_embeddings.dtype, device=device) | |
| # 5. set timesteps | |
| self.scheduler.set_timesteps(num_inference_steps, device=device) | |
| timesteps = self.scheduler.timesteps | |
| # 5. Add noise to image | |
| noise_level = torch.tensor([noise_level], dtype=torch.long, device=device) | |
| if device.type == "mps": | |
| # randn does not work reproducibly on mps | |
| noise = torch.randn(image.shape, generator=generator, device="cpu", dtype=text_embeddings.dtype).to(device) | |
| else: | |
| noise = torch.randn(image.shape, generator=generator, device=device, dtype=text_embeddings.dtype) | |
| image = self.low_res_scheduler.add_noise(image, noise, noise_level) | |
| image = torch.cat([image] * 2) if do_classifier_free_guidance else image | |
| noise_level = torch.cat([noise_level] * 2) if do_classifier_free_guidance else noise_level | |
| # 6. Prepare latent variables | |
| height, width = image.shape[2:] | |
| num_channels_latents = self.vae.config.latent_channels | |
| latents = self.prepare_latents( | |
| batch_size * num_images_per_prompt, | |
| num_channels_latents, | |
| height, | |
| width, | |
| text_embeddings.dtype, | |
| device, | |
| generator, | |
| latents, | |
| ) | |
| # 7. Check that sizes of image and latents match | |
| num_channels_image = image.shape[1] | |
| if num_channels_latents + num_channels_image != self.unet.config.in_channels: | |
| raise ValueError( | |
| f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects" | |
| f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" | |
| f" `num_channels_image`: {num_channels_image} " | |
| f" = {num_channels_latents+num_channels_image}. Please verify the config of" | |
| " `pipeline.unet` or your `image` input." | |
| ) | |
| # 8. 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) | |
| # 9. 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): | |
| # expand the latents if we are doing classifier free guidance | |
| latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents | |
| # concat latents, mask, masked_image_latents in the channel dimension | |
| latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | |
| latent_model_input = torch.cat([latent_model_input, image], dim=1) | |
| # predict the noise residual | |
| noise_pred = self.unet( | |
| latent_model_input, t, encoder_hidden_states=text_embeddings, class_labels=noise_level | |
| ).sample | |
| # perform guidance | |
| if do_classifier_free_guidance: | |
| noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) | |
| noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) | |
| # 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) | |
| # 10. Post-processing | |
| # make sure the VAE is in float32 mode, as it overflows in float16 | |
| self.vae.to(dtype=torch.float32) | |
| image = self.decode_latents(latents.float()) | |
| # 11. Convert to PIL | |
| if output_type == "pil": | |
| image = self.numpy_to_pil(image) | |
| if not return_dict: | |
| return (image,) | |
| return ImagePipelineOutput(images=image) | |