[Examples] add speech to image pipeline example (#897)
* First draft * created the SpeechToImagePipeline class * Corrected speech_to_image_diffusion.py style * Added safety checker * Corrected style * Adding examples to README
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Mikail Duzenli
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@ -13,6 +13,7 @@ If a community doesn't work as expected, please open an issue and ping the autho
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| Stable Diffusion Interpolation | Interpolate the latent space of Stable Diffusion between different prompts/seeds | [Stable Diffusion Interpolation](#stable-diffusion-interpolation) | - | [Nate Raw](https://github.com/nateraw/) |
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| Stable Diffusion Mega | **One** Stable Diffusion Pipeline with all functionalities of [Text2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py), [Image2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) and [Inpainting](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | [Stable Diffusion Mega](#stable-diffusion-mega) | - | [Patrick von Platen](https://github.com/patrickvonplaten/) |
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| Long Prompt Weighting Stable Diffusion | **One** Stable Diffusion Pipeline without tokens length limit, and support parsing weighting in prompt. | [Long Prompt Weighting Stable Diffusion](#long-prompt-weighting-stable-diffusion) | - | [SkyTNT](https://github.com/SkyTNT) |
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| Speech to Image | Using automatic-speech-recognition to transcribe text and Stable Diffusion to generate images | [Speech to Image](#speech-to-image) | - | [Mikail Duzenli](https://github.com/MikailINTech)
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To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
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```py
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@ -216,3 +217,50 @@ pipe.text2img(prompt,negative_prompt=neg_prompt, width=512, height=512, max_embe
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```
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if you see `Token indices sequence length is longer than the specified maximum sequence length for this model ( *** > 77 ) . Running this sequence through the model will result in indexing errors`. Do not worry, it is normal.
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### Speech to Image
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The following code can generate an image from an audio sample using pre-trained OpenAI whisper-small and Stable Diffusion.
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```Python
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import torch
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import matplotlib.pyplot as plt
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from datasets import load_dataset
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from diffusers import DiffusionPipeline
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from transformers import (
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WhisperForConditionalGeneration,
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WhisperProcessor,
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)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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audio_sample = ds[3]
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text = audio_sample["text"].lower()
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speech_data = audio_sample["audio"]["array"]
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model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to(device)
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processor = WhisperProcessor.from_pretrained("openai/whisper-small")
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diffuser_pipeline = DiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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custom_pipeline="speech_to_image_diffusion",
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speech_model=model,
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speech_processor=processor,
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revision="fp16",
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torch_dtype=torch.float16,
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)
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diffuser_pipeline.enable_attention_slicing()
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diffuser_pipeline = diffuser_pipeline.to(device)
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output = diffuser_pipeline(speech_data)
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plt.imshow(output.images[0])
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```
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This example produces the following image:
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@ -0,0 +1,261 @@
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import inspect
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from typing import Callable, List, Optional, Union
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import torch
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from diffusers import (
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AutoencoderKL,
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DDIMScheduler,
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DiffusionPipeline,
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LMSDiscreteScheduler,
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PNDMScheduler,
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UNet2DConditionModel,
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)
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from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
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from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
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from diffusers.utils import logging
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from transformers import (
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CLIPFeatureExtractor,
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CLIPTextModel,
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CLIPTokenizer,
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WhisperForConditionalGeneration,
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WhisperProcessor,
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)
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class SpeechToImagePipeline(DiffusionPipeline):
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def __init__(
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self,
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speech_model: WhisperForConditionalGeneration,
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speech_processor: WhisperProcessor,
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vae: AutoencoderKL,
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text_encoder: CLIPTextModel,
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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
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safety_checker: StableDiffusionSafetyChecker,
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feature_extractor: CLIPFeatureExtractor,
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):
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super().__init__()
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if safety_checker is None:
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logger.warn(
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f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
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" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
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" results in services or applications open to the public. Both the diffusers team and Hugging Face"
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" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
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" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
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" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
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)
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self.register_modules(
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speech_model=speech_model,
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speech_processor=speech_processor,
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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feature_extractor=feature_extractor,
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)
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def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
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if slice_size == "auto":
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slice_size = self.unet.config.attention_head_dim // 2
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self.unet.set_attention_slice(slice_size)
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def disable_attention_slicing(self):
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self.enable_attention_slicing(None)
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@torch.no_grad()
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def __call__(
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self,
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audio,
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sampling_rate=16_000,
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height: int = 512,
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width: int = 512,
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num_inference_steps: int = 50,
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guidance_scale: float = 7.5,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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num_images_per_prompt: Optional[int] = 1,
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eta: float = 0.0,
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generator: Optional[torch.Generator] = None,
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latents: Optional[torch.FloatTensor] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
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callback_steps: Optional[int] = 1,
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**kwargs,
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):
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inputs = self.speech_processor.feature_extractor(
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audio, return_tensors="pt", sampling_rate=sampling_rate
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).input_features.to(self.device)
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predicted_ids = self.speech_model.generate(inputs, max_length=480_000)
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prompt = self.speech_processor.tokenizer.batch_decode(predicted_ids, skip_special_tokens=True, normalize=True)[
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0
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]
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if isinstance(prompt, str):
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batch_size = 1
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elif isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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if height % 8 != 0 or width % 8 != 0:
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raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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if (callback_steps is None) or (
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callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
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):
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raise ValueError(
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f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
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f" {type(callback_steps)}."
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)
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# get prompt text embeddings
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
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removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
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text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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bs_embed, seq_len, _ = text_embeddings.shape
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text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
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text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = guidance_scale > 1.0
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance:
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uncond_tokens: List[str]
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if negative_prompt is None:
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uncond_tokens = [""]
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elif type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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elif isinstance(negative_prompt, str):
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uncond_tokens = [negative_prompt]
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elif batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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else:
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uncond_tokens = negative_prompt
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max_length = text_input_ids.shape[-1]
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uncond_input = self.tokenizer(
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uncond_tokens,
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padding="max_length",
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max_length=max_length,
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truncation=True,
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return_tensors="pt",
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)
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uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
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# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
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seq_len = uncond_embeddings.shape[1]
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uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
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uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
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# get the initial random noise unless the user supplied it
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# Unlike in other pipelines, latents need to be generated in the target device
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# for 1-to-1 results reproducibility with the CompVis implementation.
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# However this currently doesn't work in `mps`.
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latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
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latents_dtype = text_embeddings.dtype
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if latents is None:
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if self.device.type == "mps":
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# randn does not exist on mps
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latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
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self.device
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)
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else:
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latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
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else:
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if latents.shape != latents_shape:
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raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
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latents = latents.to(self.device)
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# set timesteps
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self.scheduler.set_timesteps(num_inference_steps)
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# Some schedulers like PNDM have timesteps as arrays
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# It's more optimized to move all timesteps to correct device beforehand
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timesteps_tensor = self.scheduler.timesteps.to(self.device)
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# scale the initial noise by the standard deviation required by the scheduler
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latents = latents * self.scheduler.init_noise_sigma
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# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
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# and should be between [0, 1]
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accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
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extra_step_kwargs = {}
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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for i, t in enumerate(self.progress_bar(timesteps_tensor)):
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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
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latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
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# predict the noise residual
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noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
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# perform guidance
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if do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
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# compute the previous noisy sample x_t -> x_t-1
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latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
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# call the callback, if provided
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if callback is not None and i % callback_steps == 0:
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callback(i, t, latents)
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latents = 1 / 0.18215 * latents
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image = self.vae.decode(latents).sample
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image = (image / 2 + 0.5).clamp(0, 1)
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# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
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image = image.cpu().permute(0, 2, 3, 1).float().numpy()
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if output_type == "pil":
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image = self.numpy_to_pil(image)
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if not return_dict:
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return image
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return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)
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