diff --git a/docs/source/api/pipelines/stable_diffusion.mdx b/docs/source/api/pipelines/stable_diffusion.mdx index 9884cbb207..cd50c3d5c3 100644 --- a/docs/source/api/pipelines/stable_diffusion.mdx +++ b/docs/source/api/pipelines/stable_diffusion.mdx @@ -95,3 +95,10 @@ If you want to use all possible use cases in a single `DiffusionPipeline` you ca - __call__ - enable_attention_slicing - disable_attention_slicing + + +## StableDiffusionUpscalePipeline +[[autodoc]] StableDiffusionUpscalePipeline + - __call__ + - enable_attention_slicing + - disable_attention_slicing diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 4a6661b6b3..912ae232a7 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -75,6 +75,7 @@ if is_torch_available() and is_transformers_available(): StableDiffusionInpaintPipelineLegacy, StableDiffusionPipeline, StableDiffusionPipelineSafe, + StableDiffusionUpscalePipeline, VersatileDiffusionDualGuidedPipeline, VersatileDiffusionImageVariationPipeline, VersatileDiffusionPipeline, diff --git a/src/diffusers/pipeline_utils.py b/src/diffusers/pipeline_utils.py index d2c5516220..35ebd536c5 100644 --- a/src/diffusers/pipeline_utils.py +++ b/src/diffusers/pipeline_utils.py @@ -554,7 +554,9 @@ class DiffusionPipeline(ConfigMixin): init_dict = {k: v for k, v in init_dict.items() if load_module(k, v)} if len(unused_kwargs) > 0: - logger.warning(f"Keyword arguments {unused_kwargs} not recognized.") + logger.warning( + f"Keyword arguments {unused_kwargs} are not expected by {pipeline_class.__name__} and will be ignored." + ) # import it here to avoid circular import from diffusers import pipelines @@ -680,8 +682,8 @@ class DiffusionPipeline(ConfigMixin): @staticmethod def _get_signature_keys(obj): parameters = inspect.signature(obj.__init__).parameters - required_parameters = {k: v for k, v in parameters.items() if v.default is not True} - optional_parameters = set({k for k, v in parameters.items() if v.default is True}) + required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty} + optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty}) expected_modules = set(required_parameters.keys()) - set(["self"]) return expected_modules, optional_parameters diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index 9f4cef4b73..c5aba30204 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -24,6 +24,7 @@ if is_torch_available() and is_transformers_available(): StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy, StableDiffusionPipeline, + StableDiffusionUpscalePipeline, ) from .stable_diffusion_safe import StableDiffusionPipelineSafe from .versatile_diffusion import ( diff --git a/src/diffusers/pipelines/stable_diffusion/__init__.py b/src/diffusers/pipelines/stable_diffusion/__init__.py index 3c012dbab8..0136ab565b 100644 --- a/src/diffusers/pipelines/stable_diffusion/__init__.py +++ b/src/diffusers/pipelines/stable_diffusion/__init__.py @@ -40,6 +40,7 @@ if is_transformers_available() and is_torch_available(): from .pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy + from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline from .safety_checker import StableDiffusionSafetyChecker if is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0.dev0"): diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py new file mode 100644 index 0000000000..7ccb43d46c --- /dev/null +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py @@ -0,0 +1,551 @@ +# 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.enable_xformers_memory_efficient_attention + def enable_xformers_memory_efficient_attention(self): + r""" + Enable memory efficient attention as implemented in xformers. + + When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference + time. Speed up at training time is not guaranteed. + + Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention + is used. + """ + self.unet.set_use_memory_efficient_attention_xformers(True) + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_xformers_memory_efficient_attention + def disable_xformers_memory_efficient_attention(self): + r""" + Disable memory efficient attention as implemented in xformers. + """ + self.unet.set_use_memory_efficient_attention_xformers(False) + + @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 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 + + @torch.no_grad() + 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_tensor = 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 + for i, t in enumerate(self.progress_bar(timesteps_tensor)): + # 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 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) diff --git a/src/diffusers/utils/dummy_torch_and_transformers_objects.py b/src/diffusers/utils/dummy_torch_and_transformers_objects.py index d255c174c7..2d932d2405 100644 --- a/src/diffusers/utils/dummy_torch_and_transformers_objects.py +++ b/src/diffusers/utils/dummy_torch_and_transformers_objects.py @@ -154,6 +154,21 @@ class StableDiffusionPipelineSafe(metaclass=DummyObject): requires_backends(cls, ["torch", "transformers"]) +class StableDiffusionUpscalePipeline(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + class VersatileDiffusionDualGuidedPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] diff --git a/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py b/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py new file mode 100644 index 0000000000..2092e153ee --- /dev/null +++ b/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py @@ -0,0 +1,315 @@ +# coding=utf-8 +# Copyright 2022 HuggingFace Inc. +# +# 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 gc +import random +import unittest + +import numpy as np +import torch + +from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNet2DConditionModel +from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device +from diffusers.utils.testing_utils import require_torch_gpu +from PIL import Image +from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer + +from ...test_pipelines_common import PipelineTesterMixin + + +torch.backends.cuda.matmul.allow_tf32 = False + + +class StableDiffusionUpscalePipelineFastTests(PipelineTesterMixin, unittest.TestCase): + def tearDown(self): + # clean up the VRAM after each test + super().tearDown() + gc.collect() + torch.cuda.empty_cache() + + @property + def dummy_image(self): + batch_size = 1 + num_channels = 3 + sizes = (32, 32) + + image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device) + return image + + @property + def dummy_cond_unet_upscale(self): + torch.manual_seed(0) + model = UNet2DConditionModel( + block_out_channels=(32, 32, 64), + layers_per_block=2, + sample_size=32, + in_channels=7, + out_channels=4, + down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"), + up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"), + cross_attention_dim=32, + # SD2-specific config below + attention_head_dim=8, + use_linear_projection=True, + only_cross_attention=(True, True, False), + num_class_embeds=100, + ) + return model + + @property + def dummy_vae(self): + torch.manual_seed(0) + model = AutoencoderKL( + block_out_channels=[32, 32, 64], + in_channels=3, + out_channels=3, + down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"], + up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"], + latent_channels=4, + ) + return model + + @property + def dummy_text_encoder(self): + torch.manual_seed(0) + config = CLIPTextConfig( + bos_token_id=0, + eos_token_id=2, + hidden_size=32, + intermediate_size=37, + layer_norm_eps=1e-05, + num_attention_heads=4, + num_hidden_layers=5, + pad_token_id=1, + vocab_size=1000, + # SD2-specific config below + hidden_act="gelu", + projection_dim=512, + ) + return CLIPTextModel(config) + + def test_stable_diffusion_upscale(self): + device = "cpu" # ensure determinism for the device-dependent torch.Generator + unet = self.dummy_cond_unet_upscale + low_res_scheduler = DDPMScheduler() + scheduler = DDIMScheduler(prediction_type="v_prediction") + vae = self.dummy_vae + text_encoder = self.dummy_text_encoder + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0] + low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64)) + + # make sure here that pndm scheduler skips prk + sd_pipe = StableDiffusionUpscalePipeline( + unet=unet, + low_res_scheduler=low_res_scheduler, + scheduler=scheduler, + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + max_noise_level=350, + ) + sd_pipe = sd_pipe.to(device) + sd_pipe.set_progress_bar_config(disable=None) + + prompt = "A painting of a squirrel eating a burger" + generator = torch.Generator(device=device).manual_seed(0) + output = sd_pipe( + [prompt], + image=low_res_image, + generator=generator, + guidance_scale=6.0, + noise_level=20, + num_inference_steps=2, + output_type="np", + ) + + image = output.images + + generator = torch.Generator(device=device).manual_seed(0) + image_from_tuple = sd_pipe( + [prompt], + image=low_res_image, + generator=generator, + guidance_scale=6.0, + noise_level=20, + num_inference_steps=2, + output_type="np", + return_dict=False, + )[0] + + image_slice = image[0, -3:, -3:, -1] + image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1] + + expected_height_width = low_res_image.size[0] * 4 + assert image.shape == (1, expected_height_width, expected_height_width, 3) + expected_slice = np.array([0.2562, 0.3606, 0.4204, 0.4469, 0.4822, 0.4647, 0.5315, 0.5748, 0.5606]) + + assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 + assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 + + @unittest.skipIf(torch_device != "cuda", "This test requires a GPU") + def test_stable_diffusion_upscale_fp16(self): + """Test that stable diffusion upscale works with fp16""" + unet = self.dummy_cond_unet_upscale + low_res_scheduler = DDPMScheduler() + scheduler = DDIMScheduler(prediction_type="v_prediction") + vae = self.dummy_vae + text_encoder = self.dummy_text_encoder + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0] + low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64)) + + # put models in fp16, except vae as it overflows in fp16 + unet = unet.half() + text_encoder = text_encoder.half() + + # make sure here that pndm scheduler skips prk + sd_pipe = StableDiffusionUpscalePipeline( + unet=unet, + low_res_scheduler=low_res_scheduler, + scheduler=scheduler, + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + max_noise_level=350, + ) + sd_pipe = sd_pipe.to(torch_device) + sd_pipe.set_progress_bar_config(disable=None) + + prompt = "A painting of a squirrel eating a burger" + generator = torch.Generator(device=torch_device).manual_seed(0) + image = sd_pipe( + [prompt], + image=low_res_image, + generator=generator, + num_inference_steps=2, + output_type="np", + ).images + + expected_height_width = low_res_image.size[0] * 4 + assert image.shape == (1, expected_height_width, expected_height_width, 3) + + +@slow +@require_torch_gpu +class StableDiffusionUpscalePipelineIntegrationTests(unittest.TestCase): + def tearDown(self): + # clean up the VRAM after each test + super().tearDown() + gc.collect() + torch.cuda.empty_cache() + + def test_stable_diffusion_upscale_pipeline(self): + image = load_image( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" + "/sd2-upscale/low_res_cat.png" + ) + expected_image = load_numpy( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale" + "/upsampled_cat.npy" + ) + + model_id = "stabilityai/stable-diffusion-x4-upscaler" + pipe = StableDiffusionUpscalePipeline.from_pretrained(model_id) + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + pipe.enable_attention_slicing() + + prompt = "a cat sitting on a park bench" + + generator = torch.Generator(device=torch_device).manual_seed(0) + output = pipe( + prompt=prompt, + image=image, + generator=generator, + output_type="np", + ) + image = output.images[0] + + assert image.shape == (512, 512, 3) + assert np.abs(expected_image - image).max() < 1e-3 + + def test_stable_diffusion_upscale_pipeline_fp16(self): + image = load_image( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" + "/sd2-upscale/low_res_cat.png" + ) + expected_image = load_numpy( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale" + "/upsampled_cat_fp16.npy" + ) + + model_id = "stabilityai/stable-diffusion-x4-upscaler" + pipe = StableDiffusionUpscalePipeline.from_pretrained( + model_id, + revision="fp16", + torch_dtype=torch.float16, + ) + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + pipe.enable_attention_slicing() + + prompt = "a cat sitting on a park bench" + + generator = torch.Generator(device=torch_device).manual_seed(0) + output = pipe( + prompt=prompt, + image=image, + generator=generator, + output_type="np", + ) + image = output.images[0] + + assert image.shape == (512, 512, 3) + assert np.abs(expected_image - image).max() < 5e-1 + + def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self): + torch.cuda.empty_cache() + torch.cuda.reset_max_memory_allocated() + torch.cuda.reset_peak_memory_stats() + + image = load_image( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" + "/sd2-upscale/low_res_cat.png" + ) + + model_id = "stabilityai/stable-diffusion-x4-upscaler" + pipe = StableDiffusionUpscalePipeline.from_pretrained( + model_id, + revision="fp16", + torch_dtype=torch.float16, + ) + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + pipe.enable_attention_slicing(1) + pipe.enable_sequential_cpu_offload() + + prompt = "a cat sitting on a park bench" + + generator = torch.Generator(device=torch_device).manual_seed(0) + _ = pipe( + prompt=prompt, + image=image, + generator=generator, + num_inference_steps=5, + output_type="np", + ) + + mem_bytes = torch.cuda.max_memory_allocated() + # make sure that less than 2.65 GB is allocated + assert mem_bytes < 2.65 * 10**9