Instructions to use chenguolin/DiffSplat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use chenguolin/DiffSplat with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("chenguolin/DiffSplat", 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
Ctrl+K
- elevest_gobj265k_b_C25
- gsdiff_gobj83k_pas_fp16__render
- gsdiff_gobj83k_pas_fp16_image__render
- gsdiff_gobj83k_sd15__render
- gsdiff_gobj83k_sd15__render__canny
- gsdiff_gobj83k_sd15__render__depth
- gsdiff_gobj83k_sd15__render__normal
- gsdiff_gobj83k_sd15_image__render
- gsdiff_gobj83k_sd35m__render
- gsdiff_gobj83k_sd35m_image__render
- gsrecon_gobj265k_cnp_even4
- gsvae_gobj265k_sd
- gsvae_gobj265k_sd3
- gsvae_gobj265k_sdxl_fp16
- 1.52 kB
- 736 Bytes