#
Shap-E

Shap-E is a conditional model for generating 3D assets which could be used for video game development, interior design, and architecture. It is trained on a large dataset of 3D assets, and post-processed to render more views of each object and produce 16K instead of 4K point clouds. The Shap-E model is trained in two steps:

1. an encoder accepts the point clouds and rendered views of a 3D asset and outputs the parameters of implicit functions that represent the asset
2. a diffusion model is trained on the latents produced by the encoder to generate either neural radiance fields (NeRFs) or a textured 3D mesh, making it easier to render and use the 3D asset in downstream applications

This guide will show you how to use Shap-E to start generating your own 3D assets!

Before you begin, make sure you have the following libraries installed:

    # uncomment to install the necessary libraries in Colab
    #!pip install -q diffusers transformers accelerate trimesh

##
Text-to-3D

To generate a gif of a 3D object, pass a text prompt to the ShapEPipeline. The pipeline generates a list of image frames which are used to create the 3D object.

    import torch
    from diffusers import ShapEPipeline

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

    pipe = ShapEPipeline.from_pretrained("openai/shap-e", torch_dtype=torch.float16, variant="fp16")
    pipe = pipe.to(device)

    guidance_scale = 15.0
    prompt = ["A firecracker", "A birthday cupcake"]

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

이제 export_to_gif() 함수를 사용해 이미지 프레임 리스트를 3D 오브젝트의 gif로 변환합니다.

    from diffusers.utils import export_to_gif

    export_to_gif(images[0], "firecracker_3d.gif")
    export_to_gif(images[1], "cake_3d.gif")

prompt = "A firecracker"
prompt = "A birthday cupcake"

##
Image-to-3D

To generate a 3D object from another image, use the ShapEImg2ImgPipeline. You can use an existing image or generate an entirely new one. Let’s use the Kandinsky 2.1 model to generate a new image.

    from diffusers import DiffusionPipeline
    import torch

    prior_pipeline = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
    pipeline = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16, use_safetensors=True).to("cuda")

    prompt = "A cheeseburger, white background"

    image_embeds, negative_image_embeds = prior_pipeline(prompt, guidance_scale=1.0).to_tuple()
    image = pipeline(
        prompt,
        image_embeds=image_embeds,
        negative_image_embeds=negative_image_embeds,
    ).images[0]

    image.save("burger.png")

Pass the cheeseburger to the ShapEImg2ImgPipeline to generate a 3D representation of it.

    from PIL import Image
    from diffusers import ShapEImg2ImgPipeline
    from diffusers.utils import export_to_gif

    pipe = ShapEImg2ImgPipeline.from_pretrained("openai/shap-e-img2img", torch_dtype=torch.float16, variant="fp16").to("cuda")

    guidance_scale = 3.0
    image = Image.open("burger.png").resize((256, 256))

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

    gif_path = export_to_gif(images[0], "burger_3d.gif")

cheeseburger
3D cheeseburger

##
Generate mesh

Shap-E is a flexible model that can also generate textured mesh outputs to be rendered for downstream applications. In this example, you’ll convert the output into a `glb` file because the 🤗 Datasets library supports mesh visualization of `glb` files which can be rendered by the Dataset viewer.

You can generate mesh outputs for both the ShapEPipeline and ShapEImg2ImgPipeline by specifying the `output_type` parameter as `"mesh"`:

    import torch
    from diffusers import ShapEPipeline

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

    pipe = ShapEPipeline.from_pretrained("openai/shap-e", torch_dtype=torch.float16, variant="fp16")
    pipe = pipe.to(device)

    guidance_scale = 15.0
    prompt = "A birthday cupcake"

    images = pipe(prompt, guidance_scale=guidance_scale, num_inference_steps=64, frame_size=256, output_type="mesh").images

Use the `export_to_ply()` function to save the mesh output as a `ply` file:

You can optionally save the mesh output as an `obj` file with the `export_to_obj()` function. The ability to save the mesh output in a variety of formats makes it more flexible for downstream usage!

    from diffusers.utils import export_to_ply

    ply_path = export_to_ply(images[0], "3d_cake.ply")
    print(f"Saved to folder: {ply_path}")

Then you can convert the `ply` file to a `glb` file with the trimesh library:

    import trimesh

    mesh = trimesh.load("3d_cake.ply")
    mesh_export = mesh.export("3d_cake.glb", file_type="glb")

By default, the mesh output is focused from the bottom viewpoint but you can change the default viewpoint by applying a rotation transform:

    import trimesh
    import numpy as np

    mesh = trimesh.load("3d_cake.ply")
    rot = trimesh.transformations.rotation_matrix(-np.pi / 2, [1, 0, 0])
    mesh = mesh.apply_transform(rot)
    mesh_export = mesh.export("3d_cake.glb", file_type="glb")

Upload the mesh file to your dataset repository to visualize it with the Dataset viewer!

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