Instructions to use ldhnam/architecture_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ldhnam/architecture_v3 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_v3", 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_v3: direct link, hf CLI and curl.
- Browser
- Download file 1.46 GB
-
https://huggingface.co/ldhnam/architecture_v3/resolve/main/diffusion_pytorch_model.bin
- Command line
-
hf download hf://ldhnam/architecture_v3/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/ldhnam/architecture_v3/resolve/main/diffusion_pytorch_model.bin
1.46 GB
- Xet hash:
- 0a3292ff1b990a3976ffe06282cf472db26c113bcc9cd0c8ec1565e5f4a9e065
- Size of remote file:
- 1.46 GB
- SHA256:
- 18b08a86e5e2f5f69781aa90fca73727506cc89e31ff6d93dc38d0e36591b0e5
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