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diffusers-qa-chatbot-artifacts

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provided, text embeddings will be generated from prompt input argument. negative_prompt_embeds (torch.FloatTensor, optional) β€”
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input
argument. pooled_prompt_embeds (torch.FloatTensor, optional) β€”
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting.
If not provided, pooled text embeddings will be generated from prompt input argument. negative_pooled_prompt_embeds (torch.FloatTensor, optional) β€”
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from negative_prompt
input argument. lora_scale (float, optional) β€”
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. clip_skip (int, optional) β€”
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings. Encodes the prompt into text encoder hidden states. get_guidance_scale_embedding < source > ( w embedding_dim = 512 dtype = torch.float32 ) β†’ torch.FloatTensor Parameters timesteps (torch.Tensor) β€”
generate embedding vectors at these timesteps embedding_dim (int, optional, defaults to 512) β€”
dimension of the embeddings to generate
dtype β€”
data type of the generated embeddings Returns
torch.FloatTensor
Embedding vectors with shape (len(timesteps), embedding_dim)
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
MusicLDM MusicLDM was proposed in MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies by Ke Chen, Yusong Wu, Haohe Liu, Marianna Nezhurina, Taylor Berg-Kirkpatrick, Shlomo Dubnov.
MusicLDM takes a text prompt as input and predicts the corresponding music sample. Inspired by Stable Diffusion and AudioLDM,
MusicLDM is a text-to-music latent diffusion model (LDM) that learns continuous audio representations from CLAP
latents. MusicLDM is trained on a corpus of 466 hours of music data. Beat-synchronous data augmentation strategies are applied to the music samples, both in the time domain and in the latent space. Using beat-synchronous data augmentation strategies encourages the model to interpolate between the training samples, but ...
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. text_encoder (ClapModel) β€”
Frozen text-audio embedding model (ClapTextModel), specifically the
laion/clap-htsat-unfused variant. tokenizer (PreTrainedTokenizer) β€”
A RobertaTokenizer to tokenize text. feature_extractor (ClapFeatureExtractor) β€”
Feature extractor to compute mel-spectrograms from audio waveforms. 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. Pipeline for text-to-audio generation using MusicLDM. 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 = 2.0 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 2.0) 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, the text encoding
model is a joint text-audio model (ClapModel), and the tokenizer is a
[~transformers.ClapProcessor], then automatic scoring will be performed between the generated outputs
and the input text. This scoring ranks the generated waveforms based on their cosine similarity to 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 image
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. return_dict (bool, optional, defaults to True) β€”
Whether or not to return a AudioPipelineOutput 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
AudioPipelineOutput or tuple
If return_dict is True, AudioPipelineOutput 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 >>> from diffusers import MusicLDMPipeline
>>> import torch
>>> import scipy
>>> repo_id = "ucsd-reach/musicldm"
>>> pipe = MusicLDMPipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")
>>> prompt = "Techno music with a strong, upbeat tempo and high melodic riffs"
>>> audio = pipe(prompt, num_inference_steps=10, audio_length_in_s=5.0).audios[0]
>>> # save the audio sample as a .wav file
>>> scipy.io.wavfile.write("techno.wav", rate=16000, data=audio) 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.
Activation functions Customized activation functions for supporting various models in πŸ€— Diffusers. GELU class diffusers.models.activations.GELU < source > ( dim_in: int dim_out: int approximate: str = 'none' bias: bool = True ) Parameters dim_in (int) β€” The number of channels in the input. dim_out (int) β€” Th...
paper.
Loading and Adding Custom Pipelines
Diffusers allows you to conveniently load any custom pipeline from the Hugging Face Hub as well as any official community pipeline
via the DiffusionPipeline class.
Loading custom pipelines from the Hub
Custom pipelines can be easily loaded from any model repository on the Hub that defines a diffusion pipeline in a pipeline.py file.