Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
5.54k
google/flan-t5-large variant. projection_model (AudioLDM2ProjectionModel) β€”
A trained model used to linearly project the hidden-states from the first and second text encoder models
and insert learned SOS and EOS token embeddings. The projected hidden-states from the two text encoders are
concatenated to give the input to the language model. language_model (GPT2Model) β€”
An auto-regressive language model used to generate a sequence of hidden-states conditioned on the projected
outputs from the two text encoders. tokenizer (RobertaTokenizer) β€”
Tokenizer to tokenize text for the first frozen text-encoder. tokenizer_2 (T5Tokenizer) β€”
Tokenizer to tokenize text for the second frozen text-encoder. feature_extractor (ClapFeatureExtractor) β€”
Feature extractor to pre-process generated audio waveforms to log-mel spectrograms for automatic scoring. unet (UNet2DConditionModel) β€”
A UNet2DConditionModel to denoise the encoded audio latents. scheduler (SchedulerMixin) β€”
A scheduler to be used in combination with unet to denoise the encoded audio latents. Can be one of
DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. vocoder (SpeechT5HifiGan) β€”
Vocoder of class SpeechT5HifiGan to convert the mel-spectrogram latents to the final audio waveform. Pipeline for text-to-audio generation using AudioLDM2. This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.). __call__ < source > ( prompt: Union = None audio_length_in_s: Optional = None num_inference_steps: int = 200 guidance_scale: float = 3.5 negative_prompt: Union = None num_waveforms_per_prompt: Optional = 1 eta: float = 0.0 gene...
The prompt or prompts to guide audio generation. If not defined, you need to pass prompt_embeds. audio_length_in_s (int, optional, defaults to 10.24) β€”
The length of the generated audio sample in seconds. num_inference_steps (int, optional, defaults to 200) β€”
The number of denoising steps. More denoising steps usually lead to a higher quality audio at the
expense of slower inference. guidance_scale (float, optional, defaults to 3.5) β€”
A higher guidance scale value encourages the model to generate audio that is closely linked to the text
prompt at the expense of lower sound quality. Guidance scale is enabled when guidance_scale > 1. negative_prompt (str or List[str], optional) β€”
The prompt or prompts to guide what to not include in audio generation. If not defined, you need to
pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1). num_waveforms_per_prompt (int, optional, defaults to 1) β€”
The number of waveforms to generate per prompt. If num_waveforms_per_prompt > 1, then automatic
scoring is performed between the generated outputs and the text prompt. This scoring ranks the
generated waveforms based on their cosine similarity with the text input in the joint text-audio
embedding space. eta (float, optional, defaults to 0.0) β€”
Corresponds to parameter eta (Ξ·) from the DDIM paper. Only applies
to the DDIMScheduler, and is ignored in other schedulers. generator (torch.Generator or List[torch.Generator], optional) β€”
A torch.Generator to make
generation deterministic. latents (torch.FloatTensor, optional) β€”
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for spectrogram
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random generator. prompt_embeds (torch.FloatTensor, optional) β€”
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the prompt input argument. negative_prompt_embeds (torch.FloatTensor, optional) β€”
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, negative_prompt_embeds are generated from the negative_prompt input argument. generated_prompt_embeds (torch.FloatTensor, optional) β€”
Pre-generated text embeddings from the GPT2 langauge model. Can be used to easily tweak text inputs,
e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input
argument. negative_generated_prompt_embeds (torch.FloatTensor, optional) β€”
Pre-generated negative text embeddings from the GPT2 language model. Can be used to easily tweak text
inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be computed from
negative_prompt input argument. attention_mask (torch.LongTensor, optional) β€”
Pre-computed attention mask to be applied to the prompt_embeds. If not provided, attention mask will
be computed from prompt input argument. negative_attention_mask (torch.LongTensor, optional) β€”
Pre-computed attention mask to be applied to the negative_prompt_embeds. If not provided, attention
mask will be computed from negative_prompt input argument. max_new_tokens (int, optional, defaults to None) β€”
Number of new tokens to generate with the GPT2 language model. If not provided, number of tokens will
be taken from the config of the model. return_dict (bool, optional, defaults to True) β€”
Whether or not to return a StableDiffusionPipelineOutput instead of a
plain tuple. callback (Callable, optional) β€”
A function that calls every callback_steps steps during inference. The function is called with the
following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor). callback_steps (int, optional, defaults to 1) β€”
The frequency at which the callback function is called. If not specified, the callback is called at
every step. cross_attention_kwargs (dict, optional) β€”
A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined in
self.processor. output_type (str, optional, defaults to "np") β€”
The output format of the generated audio. Choose between "np" to return a NumPy np.ndarray or
"pt" to return a PyTorch torch.Tensor object. Set to "latent" to return the latent diffusion
model (LDM) output. Returns
StableDiffusionPipelineOutput or tuple
If return_dict is True, StableDiffusionPipelineOutput is returned,
otherwise a tuple is returned where the first element is a list with the generated audio.
The call function to the pipeline for generation. Examples: Copied >>> import scipy
>>> import torch
>>> from diffusers import AudioLDM2Pipeline
>>> repo_id = "cvssp/audioldm2"
>>> pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")
>>> # define the prompts
>>> prompt = "The sound of a hammer hitting a wooden surface."
>>> negative_prompt = "Low quality."
>>> # set the seed for generator
>>> generator = torch.Generator("cuda").manual_seed(0)
>>> # run the generation
>>> audio = pipe(
... prompt,
... negative_prompt=negative_prompt,
... num_inference_steps=200,
... audio_length_in_s=10.0,
... num_waveforms_per_prompt=3,
... generator=generator,
... ).audios
>>> # save the best audio sample (index 0) as a .wav file
>>> scipy.io.wavfile.write("techno.wav", rate=16000, data=audio[0]) disable_vae_slicing < source > ( ) Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to
computing decoding in one step. enable_model_cpu_offload < source > ( gpu_id = 0 ) Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to enable_sequential_cpu_offload, this method moves one whole model at a time to the GPU when its forward
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
enable_sequential_cpu_offload, but performance is much better due to the iterative execution of the unet. enable_vae_slicing < source > ( ) Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. encode_prompt < source > ( prompt device num_waveforms_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None generated_prompt_embeds: Optio...
prompt to be encoded device (torch.device) β€”
torch device num_waveforms_per_prompt (int) β€”
number of waveforms that should be generated per prompt do_classifier_free_guidance (bool) β€”
whether to use classifier free guidance or not negative_prompt (str or List[str], optional) β€”