From 49de8142f523aef3f6adfd0c33a9a160aa7400c0 Mon Sep 17 00:00:00 2001 From: Volpeon Date: Sun, 2 Oct 2022 12:56:58 +0200 Subject: WIP: img2img --- .../clip_guided_stable_diffusion.py | 294 --------------------- 1 file changed, 294 deletions(-) delete mode 100644 pipelines/stable_diffusion/clip_guided_stable_diffusion.py (limited to 'pipelines/stable_diffusion/clip_guided_stable_diffusion.py') diff --git a/pipelines/stable_diffusion/clip_guided_stable_diffusion.py b/pipelines/stable_diffusion/clip_guided_stable_diffusion.py deleted file mode 100644 index eff74b5..0000000 --- a/pipelines/stable_diffusion/clip_guided_stable_diffusion.py +++ /dev/null @@ -1,294 +0,0 @@ -import inspect -import warnings -from typing import List, Optional, Union - -import torch -from torch import nn -from torch.nn import functional as F - -from diffusers.configuration_utils import FrozenDict -from diffusers import AutoencoderKL, DiffusionPipeline, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel -from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput -from diffusers.utils import logging -from torchvision import transforms -from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer -from schedulers.scheduling_euler_a import EulerAScheduler, CFGDenoiserForward - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -class CLIPGuidedStableDiffusion(DiffusionPipeline): - def __init__( - self, - vae: AutoencoderKL, - text_encoder: CLIPTextModel, - tokenizer: CLIPTokenizer, - unet: UNet2DConditionModel, - scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler, EulerAScheduler], - **kwargs, - ): - super().__init__() - - if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: - warnings.warn( - f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" - f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " - "to update the config accordingly as leaving `steps_offset` might led to incorrect results" - " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," - " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" - " file", - DeprecationWarning, - ) - new_config = dict(scheduler.config) - new_config["steps_offset"] = 1 - scheduler._internal_dict = FrozenDict(new_config) - - self.register_modules( - vae=vae, - text_encoder=text_encoder, - tokenizer=tokenizer, - unet=unet, - scheduler=scheduler, - ) - - 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": - # half the attention head size is usually a good trade-off between - # speed and memory - slice_size = self.unet.config.attention_head_dim // 2 - self.unet.set_attention_slice(slice_size) - - 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) - - @torch.no_grad() - def __call__( - self, - prompt: Union[str, List[str]], - negative_prompt: Optional[Union[str, List[str]]] = None, - height: Optional[int] = 512, - width: Optional[int] = 512, - num_inference_steps: Optional[int] = 50, - guidance_scale: Optional[float] = 7.5, - eta: Optional[float] = 0.0, - generator: Optional[torch.Generator] = None, - latents: Optional[torch.FloatTensor] = None, - output_type: Optional[str] = "pil", - return_dict: bool = True, - ): - r""" - Function invoked when calling the pipeline for generation. - - Args: - prompt (`str` or `List[str]`): - The prompt or prompts to guide the image generation. - height (`int`, *optional*, defaults to 512): - The height in pixels of the generated image. - width (`int`, *optional*, defaults to 512): - The width in pixels of the generated image. - 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. - 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. - - 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`. - """ - - if isinstance(prompt, str): - batch_size = 1 - elif isinstance(prompt, list): - batch_size = len(prompt) - else: - raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") - - if negative_prompt is None: - negative_prompt = [""] * batch_size - elif isinstance(negative_prompt, str): - negative_prompt = [negative_prompt] * batch_size - elif isinstance(negative_prompt, list): - if len(negative_prompt) != batch_size: - raise ValueError( - f"`prompt` and `negative_prompt` have to be the same length, but are {len(prompt)} and {len(negative_prompt)}") - else: - raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}") - - if height % 8 != 0 or width % 8 != 0: - raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") - - # 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 - - if text_input_ids.shape[-1] > self.tokenizer.model_max_length: - removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length:]) - 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}" - ) - print(f"Too many tokens: {removed_text}") - text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length] - text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0] - - # 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 - # get unconditional embeddings for classifier free guidance - if do_classifier_free_guidance: - max_length = text_input_ids.shape[-1] - uncond_input = self.tokenizer( - negative_prompt, padding="max_length", max_length=max_length, return_tensors="pt" - ) - uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] - - # 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]) - - # get the initial random noise unless the user supplied it - - # Unlike in other pipelines, latents need to be generated in the target device - # for 1-to-1 results reproducibility with the CompVis implementation. - # However this currently doesn't work in `mps`. - latents_device = "cpu" if self.device.type == "mps" else self.device - latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8) - if latents is None: - latents = torch.randn( - latents_shape, - generator=generator, - device=latents_device, - dtype=text_embeddings.dtype, - ) - else: - if latents.shape != latents_shape: - raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") - latents = latents.to(self.device) - - # set timesteps - self.scheduler.set_timesteps(num_inference_steps) - - # Some schedulers like PNDM have timesteps as arrays - # It's more optimzed to move all timesteps to correct device beforehand - if torch.is_tensor(self.scheduler.timesteps): - timesteps_tensor = self.scheduler.timesteps.to(self.device) - else: - timesteps_tensor = torch.tensor(self.scheduler.timesteps.copy(), device=self.device) - - # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas - if isinstance(self.scheduler, LMSDiscreteScheduler): - latents = latents * self.scheduler.sigmas[0] - elif isinstance(self.scheduler, EulerAScheduler): - sigma = self.scheduler.timesteps[0] - latents = latents * sigma - - # 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] - scheduler_step_args = set(inspect.signature(self.scheduler.step).parameters.keys()) - accepts_eta = "eta" in scheduler_step_args - extra_step_kwargs = {} - if accepts_eta: - extra_step_kwargs["eta"] = eta - accepts_generator = "generator" in scheduler_step_args - if generator is not None and accepts_generator: - extra_step_kwargs["generator"] = generator - - 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 - if isinstance(self.scheduler, LMSDiscreteScheduler): - sigma = self.scheduler.sigmas[i] - # the model input needs to be scaled to match the continuous ODE formulation in K-LMS - latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5) - - noise_pred = None - if isinstance(self.scheduler, EulerAScheduler): - sigma = t.reshape(1) - sigma_in = torch.cat([sigma] * latent_model_input.shape[0]) - # noise_pred = model(latent_model_input,sigma_in,uncond_embeddings, text_embeddings,guidance_scale) - noise_pred = CFGDenoiserForward(self.unet, latent_model_input, sigma_in, - text_embeddings, guidance_scale, quantize=True, DSsigmas=self.scheduler.DSsigmas) - # noise_pred = self.unet(latent_model_input, sigma_in, encoder_hidden_states=text_embeddings).sample - else: - # predict the noise residual - noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).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 - if isinstance(self.scheduler, LMSDiscreteScheduler): - latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample - elif isinstance(self.scheduler, EulerAScheduler): - if i < self.scheduler.timesteps.shape[0] - 1: # avoid out of bound error - t_prev = self.scheduler.timesteps[i+1] - latents = self.scheduler.step(noise_pred, t, t_prev, latents, **extra_step_kwargs).prev_sample - else: - latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample - - # scale and decode the image latents with vae - latents = 1 / 0.18215 * latents - image = self.vae.decode(latents).sample - - image = (image / 2 + 0.5).clamp(0, 1) - image = image.cpu().permute(0, 2, 3, 1).numpy() - - if output_type == "pil": - image = self.numpy_to_pil(image) - - if not return_dict: - return (image, None) - - return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None) -- cgit v1.2.3-54-g00ecf