#
LDM3D

LDM3D was proposed in LDM3D: Latent Diffusion Model for 3D by Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle Aflalo, Shao-Yen Tseng, Fabio Nonato, Matthias Muller, Vasudev Lal The abstract of the paper is the following:

This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. The LDM3D model is fine-tuned on a dataset of tuples containing an RGB image, depth map and caption, and validated through extensive experiments. We also develop an application called DepthFusion, which uses the generated RGB images and depth maps to create immersive and interactive 360-degree-view experiences using TouchDesigner. This technology has the potential to transform a wide range of industries, from entertainment and gaming to architecture and design. Overall, this paper presents a significant contribution to the field of generative AI and computer vision, and showcases the potential of LDM3D and DepthFusion to revolutionize content creation and digital experiences. A short video summarizing the approach can be found at this url.

Overview:

Pipeline | Tasks | Colab | Demo
--- | --- | --- | ---
pipeline_stable_diffusion_ldm3d.py | Text-to-Image Generation | - | -

##
Tips

- LDM3D generates both an image and a depth map from a given text prompt, compared to the existing txt-to-img diffusion models such as Stable Diffusion that generates only an image.
- With almost the same number of parameters, LDM3D achieves to create a latent space that can compress both the RGB images and the depth maps.

Running LDM3D is straighforward with the StableDiffusionLDM3DPipeline:

    >>> from diffusers import StableDiffusionLDM3DPipeline

    >>> pipe = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d")
    prompt ="A picture of some lemons on a table"
    output = pipe(prompt)
    rgb_image, depth_image = output.rgb, output.depth
    rgb_image[0].save("lemons_ldm3d_rgb.jpg")
    depth_image[0].save("lemons_ldm3d_depth.png")

##
StableDiffusionPipelineOutput

###
class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput

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( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] nsfw_content_detected: typing.Optional[typing.List[bool]] )

Parameters

- images (`List[PIL.Image.Image]` or `np.ndarray`) — List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
- nsfw_content_detected (`List[bool]`) — List of flags denoting whether the corresponding generated image likely represents “not-safe-for-work” (nsfw) content, or `None` if safety checking could not be performed.

Output class for Stable Diffusion pipelines.

####
__call__

( *args **kwargs )

Call self as a function.

##
StableDiffusionLDM3DPipeline

###
class diffusers.StableDiffusionLDM3DPipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor requires_safety_checker: bool = True )

Parameters

- vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode rgb and depth images to and from latent representations.
- text_encoder (`CLIPTextModel`) — Frozen text-encoder. Stable Diffusion uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.
- tokenizer (`CLIPTokenizer`) — Tokenizer of class CLIPTokenizer.
- unet (UNet2DConditionModel) — Conditional U-Net architecture to denoise the encoded rgb and depth latents.
- scheduler (SchedulerMixin) — 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`) — Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the model card for details.
- feature_extractor (`CLIPImageProcessor`) — Model that extracts features from generated images to be used as inputs for the `safety_checker`.

Pipeline for text-to-image and 3d generation using LDM3D. LDM3D: Latent Diffusion Model for 3D: https://arxiv.org/abs/2305.10853

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

In addition the pipeline inherits the following loading methods:

- Textual-Inversion: loaders.TextualInversionLoaderMixin.load_textual_inversion()
- LoRA: loaders.LoraLoaderMixin.load_lora_weights()
- Ckpt: loaders.FromSingleFileMixin.from_single_file()

as well as the following saving methods:

- LoRA: loaders.LoraLoaderMixin.save_lora_weights()

####
__call__

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( prompt: typing.Union[str, typing.List[str]] = None height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 49 guidance_scale: float = 5.0 negative_prompt: typing.Union[typing.List[str], str, NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: int = 1 cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None ) → StableDiffusionPipelineOutput or `tuple`

Parameters

- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead.
- height (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image.
- width (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image.
- num_inference_steps (`int`, optional, defaults to 50) — 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 5.0) — Guidance scale as defined in Classifier-Free Diffusion Guidance. `guidance_scale` is defined as `w` of equation 2. of Imagen Paper. Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- num_images_per_prompt (`int`, optional, defaults to 1) — The number of images to generate per prompt.
- eta (`float`, optional, defaults to 0.0) — Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to schedulers.DDIMScheduler, will be ignored for others.
- generator (`torch.Generator` or `List[torch.Generator]`, optional) — One or a list of torch generator(s) to make generation deterministic.
- latents (`torch.FloatTensor`, optional) — 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 will ge generated by sampling using the supplied random `generator`.
- prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated text embeddings. 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_prompt_embeds (`torch.FloatTensor`, optional) — 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.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generate image. Choose between PIL: `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple.
- callback (`Callable`, optional) — A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, optional, defaults to 1) — The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step.
- cross_attention_kwargs (`dict`, optional) — A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in diffusers.cross_attention.

Returns

StableDiffusionPipelineOutput or `tuple`

StableDiffusionPipelineOutput if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of` bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the` safety_checker`.

Function invoked when calling the pipeline for generation.

Examples:

    >>> import torch
    >>> from diffusers import StableDiffusionPipeline

    >>> pipe = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d")
    >>> pipe = pipe.to("cuda")

    >>> prompt = "a photo of an astronaut riding a horse on mars"
    >>> output = pipe(prompt)
    >>> rgb_image, depth_image = output.rgb, output.depth

####
disable_vae_slicing

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( )

Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step.

####
disable_vae_tiling

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( )

Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to computing decoding in one step.

####
enable_model_cpu_offload

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( 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_sequential_cpu_offload

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( gpu_id = 0 )

Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its` forward`method called. Note that offloading happens on a submodule basis. Memory savings are higher than with`enable_model_cpu_offload`, but performance is lower.

####
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.

####
enable_vae_tiling

< source >

( )

Enable tiled VAE decoding.

When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful to save a large amount of memory and to allow the processing of larger images.