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Shap-E

The Shap-E model was proposed in Shap-E: Generating Conditional 3D Implicit Functions by Alex Nichol and Heewoo Jun from OpenAI.

The abstract from the paper is:

We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space.

The original codebase can be found at openai/shap-e.

See the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.

ShapEPipeline

class diffusers.ShapEPipeline

< source >

( prior: PriorTransformer text_encoder: CLIPTextModelWithProjection tokenizer: CLIPTokenizer scheduler: HeunDiscreteScheduler shap_e_renderer: ShapERenderer )

Parameters

prior (PriorTransformer) — The canonical unCLIP prior to approximate the image embedding from the text embedding.
text_encoder (CLIPTextModelWithProjection) — Frozen text-encoder.
tokenizer (CLIPTokenizer) — A CLIPTokenizer to tokenize text.
scheduler (HeunDiscreteScheduler) — A scheduler to be used in combination with the prior model to generate image embedding.
shap_e_renderer (ShapERenderer) — Shap-E renderer projects the generated latents into parameters of a MLP to create 3D objects with the NeRF rendering method.

Pipeline for generating latent representation of a 3D asset and rendering with the NeRF method.

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: str num_images_per_prompt: int = 1 num_inference_steps: int = 25 generator: Union = None latents: Optional = None guidance_scale: float = 4.0 frame_size: int = 64 output_type: Optional = 'pil' return_dict: bool = True ) → ShapEPipelineOutput or tuple

Parameters

prompt (str or List[str]) — The prompt or prompts to guide the image generation.
num_images_per_prompt (int, optional, defaults to 1) — The number of images to generate per prompt.
num_inference_steps (int, optional, defaults to 25) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
generator (torch.Generator or List[torch.Generator], optional) — A torch.Generator 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 is generated by sampling using the supplied random generator.
guidance_scale (float, optional, defaults to 4.0) — 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.
frame_size (int, optional, default to 64) — The width and height of each image frame of the generated 3D output.
output_type (str, optional, defaults to "pil") — The output format of the generated image. Choose between "pil" (PIL.Image.Image), "np" (np.array), "latent" (torch.Tensor), or mesh (MeshDecoderOutput).
return_dict (bool, optional, defaults to True) — Whether or not to return a ShapEPipelineOutput instead of a plain tuple.

Returns

ShapEPipelineOutput or tuple

If return_dict is True, ShapEPipelineOutput is returned, otherwise a tuple is returned where the first element is a list with the generated images.

The call function to the pipeline for generation.

Examples:

>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import export_to_gif

>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

>>> repo = "openai/shap-e"
>>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)

>>> guidance_scale = 15.0
>>> prompt = "a shark"

>>> images = pipe(
...     prompt,
...     guidance_scale=guidance_scale,
...     num_inference_steps=64,
...     frame_size=256,
... ).images

>>> gif_path = export_to_gif(images[0], "shark_3d.gif")

ShapEImg2ImgPipeline

class diffusers.ShapEImg2ImgPipeline

< source >

( prior: PriorTransformer image_encoder: CLIPVisionModel image_processor: CLIPImageProcessor scheduler: HeunDiscreteScheduler shap_e_renderer: ShapERenderer )

Parameters

prior (PriorTransformer) — The canonical unCLIP prior to approximate the image embedding from the text embedding.
image_encoder (CLIPVisionModel) — Frozen image-encoder.
image_processor (CLIPImageProcessor) — A CLIPImageProcessor to process images.
scheduler (HeunDiscreteScheduler) — A scheduler to be used in combination with the prior model to generate image embedding.
shap_e_renderer (ShapERenderer) — Shap-E renderer projects the generated latents into parameters of a MLP to create 3D objects with the NeRF rendering method.

Pipeline for generating latent representation of a 3D asset and rendering with the NeRF method from an image.

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 >

( image: Union num_images_per_prompt: int = 1 num_inference_steps: int = 25 generator: Union = None latents: Optional = None guidance_scale: float = 4.0 frame_size: int = 64 output_type: Optional = 'pil' return_dict: bool = True ) → ShapEPipelineOutput or tuple

Parameters

image (torch.FloatTensor, PIL.Image.Image, np.ndarray, List[torch.FloatTensor], List[PIL.Image.Image], or List[np.ndarray]) — Image or tensor representing an image batch to be used as the starting point. Can also accept image latents as image, but if passing latents directly it is not encoded again.
num_images_per_prompt (int, optional, defaults to 1) — The number of images to generate per prompt.
num_inference_steps (int, optional, defaults to 25) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
generator (torch.Generator or List[torch.Generator], optional) — A torch.Generator 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 is generated by sampling using the supplied random generator.
guidance_scale (float, optional, defaults to 4.0) — 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.
frame_size (int, optional, default to 64) — The width and height of each image frame of the generated 3D output.
output_type (str, optional, defaults to "pil") — The output format of the generated image. Choose between "pil" (PIL.Image.Image), "np" (np.array), "latent" (torch.Tensor), or mesh (MeshDecoderOutput).
return_dict (bool, optional, defaults to True) — Whether or not to return a ShapEPipelineOutput instead of a plain tuple.

Returns

ShapEPipelineOutput or tuple

If return_dict is True, ShapEPipelineOutput is returned, otherwise a tuple is returned where the first element is a list with the generated images.

The call function to the pipeline for generation.

Examples:

>>> from PIL import Image
>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import export_to_gif, load_image

>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

>>> repo = "openai/shap-e-img2img"
>>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)

>>> guidance_scale = 3.0
>>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
>>> image = load_image(image_url).convert("RGB")

>>> images = pipe(
...     image,
...     guidance_scale=guidance_scale,
...     num_inference_steps=64,
...     frame_size=256,
... ).images

>>> gif_path = export_to_gif(images[0], "corgi_3d.gif")