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 checkpoint-45000/pytorch_model.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/checkpoint-45000/pytorch_model.bin
- Command line
-
hf download hf://erkam/sd-clevr-scene-graph/checkpoint-45000/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/erkam/sd-clevr-scene-graph/resolve/main/checkpoint-45000/pytorch_model.bin
3.42 MB
- Xet hash:
- 070df80272520c1a0157b6892b34863c049bbb3b9554d0f49ae512b336f840aa
- Size of remote file:
- 3.42 MB
- SHA256:
- ff5f5efa138e1175b5f6582bc7fd9b303825f31eb0b966a66e0676a66445b057
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