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diffusers-qa-chatbot-artifacts

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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) 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.
MultiDiffusion MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation is by Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. The abstract from the paper is: Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user contro...
For some GPUs with high performance, this can speedup the generation process and increase VRAM usage. To generate panorama-like images make sure you pass the width parameter accordingly. We recommend a width value of 2048 which is the default. Circular padding is applied to ensure there are no stitching artifacts when ...
But with circular padding, the right and the left parts are matching (circular_padding=True):
Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines. StableDiffusionPanoramaPipeline class diffusers.StableDiffusionPano...
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. text_encoder (CLIPTextModel) β€”
Frozen text-encoder (clip-vit-large-patch14). tokenizer (CLIPTokenizer) β€”
A CLIPTokenizer to tokenize text. unet (UNet2DConditionModel) β€”
A UNet2DConditionModel to denoise the encoded image latents. scheduler (SchedulerMixin) β€”
A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of
DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. safety_checker (StableDiffusionSafetyChecker) β€”
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the model card for more details
about a model’s potential harms. feature_extractor (CLIPImageProcessor) β€”
A CLIPImageProcessor to extract features from generated images; used as inputs to the safety_checker. Pipeline for text-to-image generation using MultiDiffusion. 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.). The pipeline also inherits the following loading methods: load_textual_inversion() for loading textual inversion embeddings load_lora_weights() for loading LoRA weights save_lora_weights() for saving LoRA weights load_ip_adapter(...
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds. height (int, optional, defaults to 512) β€”
The height in pixels of the generated image. width (int, optional, defaults to 2048) β€”
The width in pixels of the generated image. The width is kept high because the pipeline is supposed
generate panorama-like images. num_inference_steps (int, optional, defaults to 50) β€”
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) β€”
A higher guidance scale value encourages the model to generate images closely linked to the text
prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1. view_batch_size (int, optional, defaults to 1) β€”
The batch size to denoise split views. For some GPUs with high performance, higher view batch size can
speedup the generation and increase the VRAM usage. negative_prompt (str or List[str], optional) β€”
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1). num_images_per_prompt (int, optional, defaults to 1) β€”
The number of images to generate per prompt. 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.
ip_adapter_image β€” (PipelineImageInput, optional):
Optional image input to work with IP Adapters. output_type (str, optional, defaults to "pil") β€”
The output format of the generated image. Choose between PIL.Image or np.array. 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€”