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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 Now use the export_to_gif() function to turn the list of image frames into a gif of the 3D object. Copied 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. Copied from ... |
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. Copied 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 ... |
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... |
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: Copied 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: Copied 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! |
Latent Consistency Model Multistep Scheduler Overview Multistep and onestep scheduler (Algorithm 3) introduced alongside latent consistency models in the paper Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference by Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. |
This scheduler should be able to generate good samples from LatentConsistencyModelPipeline in 1-8 steps. LCMScheduler class diffusers.LCMScheduler < source > ( num_train_timesteps: int = 1000 beta_start: float = 0.00085 beta_end: float = 0.012 beta_schedule: str = 'scaled_linear' trained_betas: Union = None original... |
The number of diffusion steps to train the model. beta_start (float, defaults to 0.0001) — |
The starting beta value of inference. beta_end (float, defaults to 0.02) — |
The final beta value. beta_schedule (str, defaults to "linear") — |
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from |
linear, scaled_linear, or squaredcos_cap_v2. trained_betas (np.ndarray, optional) — |
Pass an array of betas directly to the constructor to bypass beta_start and beta_end. original_inference_steps (int, optional, defaults to 50) — |
The default number of inference steps used to generate a linearly-spaced timestep schedule, from which we |
will ultimately take num_inference_steps evenly spaced timesteps to form the final timestep schedule. clip_sample (bool, defaults to True) — |
Clip the predicted sample for numerical stability. clip_sample_range (float, defaults to 1.0) — |
The maximum magnitude for sample clipping. Valid only when clip_sample=True. set_alpha_to_one (bool, defaults to True) — |
Each diffusion step uses the alphas product value at that step and at the previous one. For the final step |
there is no previous alpha. When this option is True the previous alpha product is fixed to 1, |
otherwise it uses the alpha value at step 0. steps_offset (int, defaults to 0) — |
An offset added to the inference steps. You can use a combination of offset=1 and |
set_alpha_to_one=False to make the last step use step 0 for the previous alpha product like in Stable |
Diffusion. prediction_type (str, defaults to epsilon, optional) — |
Prediction type of the scheduler function; can be epsilon (predicts the noise of the diffusion process), |
sample (directly predicts the noisy sample) or v_prediction` (see section 2.4 of Imagen |
Video paper). thresholding (bool, defaults to False) — |
Whether to use the “dynamic thresholding” method. This is unsuitable for latent-space diffusion models such |
as Stable Diffusion. dynamic_thresholding_ratio (float, defaults to 0.995) — |
The ratio for the dynamic thresholding method. Valid only when thresholding=True. sample_max_value (float, defaults to 1.0) — |
The threshold value for dynamic thresholding. Valid only when thresholding=True. timestep_spacing (str, defaults to "leading") — |
The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and |
Sample Steps are Flawed for more information. timestep_scaling (float, defaults to 10.0) — |
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