Instructions to use DhruvDecoder/model_3d_diffuser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DhruvDecoder/model_3d_diffuser with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("DhruvDecoder/model_3d_diffuser", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
MVDream-hf
modified from https://github.com/KokeCacao/mvdream-hf.
convert weights
# download original ckpt
wget https://huggingface.co/MVDream/MVDream/resolve/main/sd-v2.1-base-4view.pt
wget https://raw.githubusercontent.com/bytedance/MVDream/main/mvdream/configs/sd-v2-base.yaml
# convert
python convert_mvdream_to_diffusers.py --checkpoint_path ./sd-v2.1-base-4view.pt --dump_path ./weights --original_config_file ./sd-v2-base.yaml --half --to_safetensors --test
run pipeline
import torch
import kiui
from mvdream.pipeline_mvdream import MVDreamStableDiffusionPipeline
pipe = MVDreamStableDiffusionPipeline.from_pretrained('./weights', torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt) # np.ndarray [4, 256, 256, 3]
kiui.vis.plot_image(image)