Instructions to use ldhnam/architecture_v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ldhnam/architecture_v4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ldhnam/architecture_v4", 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
Download diffusion_pytorch_model.bin from ldhnam/architecture_v4: direct link, hf CLI and curl.
- Browser
- Download file 1.46 GB
-
https://huggingface.co/ldhnam/architecture_v4/resolve/main/diffusion_pytorch_model.bin
- Command line
-
hf download hf://ldhnam/architecture_v4/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/ldhnam/architecture_v4/resolve/main/diffusion_pytorch_model.bin
1.46 GB
- Xet hash:
- 26f798a903f2278efd9e990041c5929fe4c461630a7f3ab75c2ced8a3522f558
- Size of remote file:
- 1.46 GB
- SHA256:
- 37c5ea8ad0f49a6653c5e94348947a34a984519d2871d0296bd99725351d3e0b
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