[Report] [Official Demo] [Demo by @yvrjsharma] [Google Colab] [Replicate demo]
You will need torch
(recommended 2.0
or higher), diffusers
(recommended 0.20.2
), and transformers
to start. If you are using torch
1.x
, it is recommended to install xformers
to compute attention in the model efficiently. The code also runs on older versions of diffusers
, but you may see a decrease in model performance.
And you are all set! We provide a custom pipeline for diffusers
, so no extra code is required.
To generate multi-view images from a single input image, you can run the following code (also see examples/img_to_mv.py):
import torch
import requests
from PIL import Image
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
# Load the pipeline
pipeline = DiffusionPipeline.from_pretrained(
"sudo-ai/zero123plus-v1.1", custom_pipeline="sudo-ai/zero123plus-pipeline",
torch_dtype=torch.float16
)
# Feel free to tune the scheduler!
# `timestep_spacing` parameter is not supported in older versions of `diffusers`
# so there may be performance degradations
# We recommend using `diffusers==0.20.2`
pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
pipeline.scheduler.config, timestep_spacing='trailing'
)
pipeline.to('cuda:0')
# Download an example image.
cond = Image.open(requests.get("https://d.skis.ltd/nrp/sample-data/lysol.png", stream=True).raw)
# Run the pipeline!
result = pipeline(cond, num_inference_steps=75).images[0]
# for general real and synthetic images of general objects
# usually it is enough to have around 28 inference steps
# for images with delicate details like faces (real or anime)
# you may need 75-100 steps for the details to construct
result.show()
result.save("output.png")
The above example requires ~5GB VRAM to run.
The input image needs to be square, and the recommended image resolution is >=320x320
.
By default, Zero123++ generates opaque images with a gray background (the zero
for Stable Diffusion VAE).
You may run an extra background removal pass like rembg
to remove the gray background.
# !pip install rembg
import rembg
result = rembg.remove(result)
result.show()
To run the depth ControlNet, you can use the following example (also see examples/depth_controlnet.py):
import torch
import requests
from PIL import Image
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler, ControlNetModel
# Load the pipeline
pipeline = DiffusionPipeline.from_pretrained(
"sudo-ai/zero123plus-v1.1", custom_pipeline="sudo-ai/zero123plus-pipeline",
torch_dtype=torch.float16
)
pipeline.add_controlnet(ControlNetModel.from_pretrained(
"sudo-ai/controlnet-zp11-depth-v1", torch_dtype=torch.float16
), conditioning_scale=0.75)
# Feel free to tune the scheduler
pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
pipeline.scheduler.config, timestep_spacing='trailing'
)
pipeline.to('cuda:0')
# Run the pipeline
cond = Image.open(requests.get("https://d.skis.ltd/nrp/sample-data/0_cond.png", stream=True).raw)
depth = Image.open(requests.get("https://d.skis.ltd/nrp/sample-data/0_depth.png", stream=True).raw)
result = pipeline(cond, depth_image=depth, num_inference_steps=36).images[0]
result.show()
result.save("output.png")
This example requires ~5.7GB VRAM to run.
The models are available at https://huggingface.co/sudo-ai:
sudo-ai/zero123plus-v1.1
, base Zero123++ model release (v1.1).sudo-ai/controlnet-zp11-depth-v1
depth ControlNet checkpoint release (v1) for Zero123++ (v1.1).
The source code for the diffusers custom pipeline is available in the diffusers-support directory.
Output views are a fixed set of camera poses:
- Azimuth (relative to input view):
30, 90, 150, 210, 270, 330
. - Elevation (absolute):
30, -20, 30, -20, 30, -20
.
You will need to install extra dependencies:
pip install -r requirements.txt
Then run streamlit run app.py
.
For Gradio Demo, you can run python gradio_app.py
.
[One-2-3-45] [One-2-3-45++] [Zero123]
If you found Zero123++ helpful, please cite our report:
@misc{shi2023zero123plus,
title={Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model},
author={Ruoxi Shi and Hansheng Chen and Zhuoyang Zhang and Minghua Liu and Chao Xu and Xinyue Wei and Linghao Chen and Chong Zeng and Hao Su},
year={2023},
eprint={2310.15110},
archivePrefix={arXiv},
primaryClass={cs.CV}
}