|
| 1 | +import inspect |
| 2 | +from typing import Callable, List, Optional, Union |
| 3 | + |
| 4 | +import torch |
| 5 | + |
| 6 | +from diffusers import ( |
| 7 | + AutoencoderKL, |
| 8 | + DDIMScheduler, |
| 9 | + DiffusionPipeline, |
| 10 | + LMSDiscreteScheduler, |
| 11 | + PNDMScheduler, |
| 12 | + UNet2DConditionModel, |
| 13 | +) |
| 14 | +from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput |
| 15 | +from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker |
| 16 | +from diffusers.utils import logging |
| 17 | +from transformers import ( |
| 18 | + CLIPFeatureExtractor, |
| 19 | + CLIPTextModel, |
| 20 | + CLIPTokenizer, |
| 21 | + WhisperForConditionalGeneration, |
| 22 | + WhisperProcessor, |
| 23 | +) |
| 24 | + |
| 25 | + |
| 26 | +logger = logging.get_logger(__name__) # pylint: disable=invalid-name |
| 27 | + |
| 28 | + |
| 29 | +class SpeechToImagePipeline(DiffusionPipeline): |
| 30 | + def __init__( |
| 31 | + self, |
| 32 | + speech_model: WhisperForConditionalGeneration, |
| 33 | + speech_processor: WhisperProcessor, |
| 34 | + vae: AutoencoderKL, |
| 35 | + text_encoder: CLIPTextModel, |
| 36 | + tokenizer: CLIPTokenizer, |
| 37 | + unet: UNet2DConditionModel, |
| 38 | + scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], |
| 39 | + safety_checker: StableDiffusionSafetyChecker, |
| 40 | + feature_extractor: CLIPFeatureExtractor, |
| 41 | + ): |
| 42 | + super().__init__() |
| 43 | + |
| 44 | + if safety_checker is None: |
| 45 | + logger.warn( |
| 46 | + f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" |
| 47 | + " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" |
| 48 | + " results in services or applications open to the public. Both the diffusers team and Hugging Face" |
| 49 | + " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" |
| 50 | + " it only for use-cases that involve analyzing network behavior or auditing its results. For more" |
| 51 | + " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." |
| 52 | + ) |
| 53 | + |
| 54 | + self.register_modules( |
| 55 | + speech_model=speech_model, |
| 56 | + speech_processor=speech_processor, |
| 57 | + vae=vae, |
| 58 | + text_encoder=text_encoder, |
| 59 | + tokenizer=tokenizer, |
| 60 | + unet=unet, |
| 61 | + scheduler=scheduler, |
| 62 | + feature_extractor=feature_extractor, |
| 63 | + ) |
| 64 | + |
| 65 | + def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"): |
| 66 | + if slice_size == "auto": |
| 67 | + slice_size = self.unet.config.attention_head_dim // 2 |
| 68 | + self.unet.set_attention_slice(slice_size) |
| 69 | + |
| 70 | + def disable_attention_slicing(self): |
| 71 | + self.enable_attention_slicing(None) |
| 72 | + |
| 73 | + @torch.no_grad() |
| 74 | + def __call__( |
| 75 | + self, |
| 76 | + audio, |
| 77 | + sampling_rate=16_000, |
| 78 | + height: int = 512, |
| 79 | + width: int = 512, |
| 80 | + num_inference_steps: int = 50, |
| 81 | + guidance_scale: float = 7.5, |
| 82 | + negative_prompt: Optional[Union[str, List[str]]] = None, |
| 83 | + num_images_per_prompt: Optional[int] = 1, |
| 84 | + eta: float = 0.0, |
| 85 | + generator: Optional[torch.Generator] = None, |
| 86 | + latents: Optional[torch.FloatTensor] = None, |
| 87 | + output_type: Optional[str] = "pil", |
| 88 | + return_dict: bool = True, |
| 89 | + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, |
| 90 | + callback_steps: Optional[int] = 1, |
| 91 | + **kwargs, |
| 92 | + ): |
| 93 | + inputs = self.speech_processor.feature_extractor( |
| 94 | + audio, return_tensors="pt", sampling_rate=sampling_rate |
| 95 | + ).input_features.to(self.device) |
| 96 | + predicted_ids = self.speech_model.generate(inputs, max_length=480_000) |
| 97 | + |
| 98 | + prompt = self.speech_processor.tokenizer.batch_decode(predicted_ids, skip_special_tokens=True, normalize=True)[ |
| 99 | + 0 |
| 100 | + ] |
| 101 | + |
| 102 | + if isinstance(prompt, str): |
| 103 | + batch_size = 1 |
| 104 | + elif isinstance(prompt, list): |
| 105 | + batch_size = len(prompt) |
| 106 | + else: |
| 107 | + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") |
| 108 | + |
| 109 | + if height % 8 != 0 or width % 8 != 0: |
| 110 | + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") |
| 111 | + |
| 112 | + if (callback_steps is None) or ( |
| 113 | + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) |
| 114 | + ): |
| 115 | + raise ValueError( |
| 116 | + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" |
| 117 | + f" {type(callback_steps)}." |
| 118 | + ) |
| 119 | + |
| 120 | + # get prompt text embeddings |
| 121 | + text_inputs = self.tokenizer( |
| 122 | + prompt, |
| 123 | + padding="max_length", |
| 124 | + max_length=self.tokenizer.model_max_length, |
| 125 | + return_tensors="pt", |
| 126 | + ) |
| 127 | + text_input_ids = text_inputs.input_ids |
| 128 | + |
| 129 | + if text_input_ids.shape[-1] > self.tokenizer.model_max_length: |
| 130 | + removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]) |
| 131 | + logger.warning( |
| 132 | + "The following part of your input was truncated because CLIP can only handle sequences up to" |
| 133 | + f" {self.tokenizer.model_max_length} tokens: {removed_text}" |
| 134 | + ) |
| 135 | + text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length] |
| 136 | + text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0] |
| 137 | + |
| 138 | + # duplicate text embeddings for each generation per prompt, using mps friendly method |
| 139 | + bs_embed, seq_len, _ = text_embeddings.shape |
| 140 | + text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1) |
| 141 | + text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) |
| 142 | + |
| 143 | + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) |
| 144 | + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` |
| 145 | + # corresponds to doing no classifier free guidance. |
| 146 | + do_classifier_free_guidance = guidance_scale > 1.0 |
| 147 | + # get unconditional embeddings for classifier free guidance |
| 148 | + if do_classifier_free_guidance: |
| 149 | + uncond_tokens: List[str] |
| 150 | + if negative_prompt is None: |
| 151 | + uncond_tokens = [""] |
| 152 | + elif type(prompt) is not type(negative_prompt): |
| 153 | + raise TypeError( |
| 154 | + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" |
| 155 | + f" {type(prompt)}." |
| 156 | + ) |
| 157 | + elif isinstance(negative_prompt, str): |
| 158 | + uncond_tokens = [negative_prompt] |
| 159 | + elif batch_size != len(negative_prompt): |
| 160 | + raise ValueError( |
| 161 | + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" |
| 162 | + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" |
| 163 | + " the batch size of `prompt`." |
| 164 | + ) |
| 165 | + else: |
| 166 | + uncond_tokens = negative_prompt |
| 167 | + |
| 168 | + max_length = text_input_ids.shape[-1] |
| 169 | + uncond_input = self.tokenizer( |
| 170 | + uncond_tokens, |
| 171 | + padding="max_length", |
| 172 | + max_length=max_length, |
| 173 | + truncation=True, |
| 174 | + return_tensors="pt", |
| 175 | + ) |
| 176 | + uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] |
| 177 | + |
| 178 | + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method |
| 179 | + seq_len = uncond_embeddings.shape[1] |
| 180 | + uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1) |
| 181 | + uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1) |
| 182 | + |
| 183 | + # For classifier free guidance, we need to do two forward passes. |
| 184 | + # Here we concatenate the unconditional and text embeddings into a single batch |
| 185 | + # to avoid doing two forward passes |
| 186 | + text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) |
| 187 | + |
| 188 | + # get the initial random noise unless the user supplied it |
| 189 | + |
| 190 | + # Unlike in other pipelines, latents need to be generated in the target device |
| 191 | + # for 1-to-1 results reproducibility with the CompVis implementation. |
| 192 | + # However this currently doesn't work in `mps`. |
| 193 | + latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8) |
| 194 | + latents_dtype = text_embeddings.dtype |
| 195 | + if latents is None: |
| 196 | + if self.device.type == "mps": |
| 197 | + # randn does not exist on mps |
| 198 | + latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to( |
| 199 | + self.device |
| 200 | + ) |
| 201 | + else: |
| 202 | + latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype) |
| 203 | + else: |
| 204 | + if latents.shape != latents_shape: |
| 205 | + raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") |
| 206 | + latents = latents.to(self.device) |
| 207 | + |
| 208 | + # set timesteps |
| 209 | + self.scheduler.set_timesteps(num_inference_steps) |
| 210 | + |
| 211 | + # Some schedulers like PNDM have timesteps as arrays |
| 212 | + # It's more optimized to move all timesteps to correct device beforehand |
| 213 | + timesteps_tensor = self.scheduler.timesteps.to(self.device) |
| 214 | + |
| 215 | + # scale the initial noise by the standard deviation required by the scheduler |
| 216 | + latents = latents * self.scheduler.init_noise_sigma |
| 217 | + |
| 218 | + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature |
| 219 | + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. |
| 220 | + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 |
| 221 | + # and should be between [0, 1] |
| 222 | + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) |
| 223 | + extra_step_kwargs = {} |
| 224 | + if accepts_eta: |
| 225 | + extra_step_kwargs["eta"] = eta |
| 226 | + |
| 227 | + for i, t in enumerate(self.progress_bar(timesteps_tensor)): |
| 228 | + # expand the latents if we are doing classifier free guidance |
| 229 | + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents |
| 230 | + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) |
| 231 | + |
| 232 | + # predict the noise residual |
| 233 | + noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample |
| 234 | + |
| 235 | + # perform guidance |
| 236 | + if do_classifier_free_guidance: |
| 237 | + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) |
| 238 | + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) |
| 239 | + |
| 240 | + # compute the previous noisy sample x_t -> x_t-1 |
| 241 | + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample |
| 242 | + |
| 243 | + # call the callback, if provided |
| 244 | + if callback is not None and i % callback_steps == 0: |
| 245 | + callback(i, t, latents) |
| 246 | + |
| 247 | + latents = 1 / 0.18215 * latents |
| 248 | + image = self.vae.decode(latents).sample |
| 249 | + |
| 250 | + image = (image / 2 + 0.5).clamp(0, 1) |
| 251 | + |
| 252 | + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 |
| 253 | + image = image.cpu().permute(0, 2, 3, 1).float().numpy() |
| 254 | + |
| 255 | + if output_type == "pil": |
| 256 | + image = self.numpy_to_pil(image) |
| 257 | + |
| 258 | + if not return_dict: |
| 259 | + return image |
| 260 | + |
| 261 | + return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None) |
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