Instructions to use erkam/sd-clevr-scene-graph with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use erkam/sd-clevr-scene-graph with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("erkam/sd-clevr-scene-graph") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download pytorch_lora_weights.bin from erkam/sd-clevr-scene-graph: direct link, hf CLI and curl.
- Browser
- Download file 3.42 MB
-
https://huggingface.co/erkam/sd-clevr-scene-graph/resolve/main/pytorch_lora_weights.bin
- Command line
-
hf download hf://erkam/sd-clevr-scene-graph/pytorch_lora_weights.bin
-
curl -L -o pytorch_lora_weights.bin https://huggingface.co/erkam/sd-clevr-scene-graph/resolve/main/pytorch_lora_weights.bin
3.42 MB
- Xet hash:
- a7b3c5ad6bf02f075ac5470c1e167bb21f39189443fd939403ed3c32d73e031d
- Size of remote file:
- 3.42 MB
- SHA256:
- 0a517779e642c27ce4c5f27067703ff48593a2e02a49cdaa5d2daa6381e4dc5b
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