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
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Download README.md from erkam/sd-clevr-scene-graph: direct link, hf CLI and curl.
- Browser
- Download file 579 Bytes
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https://huggingface.co/erkam/sd-clevr-scene-graph/resolve/main/README.md
- Command line
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hf download hf://erkam/sd-clevr-scene-graph/README.md
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curl -L -o README.md https://huggingface.co/erkam/sd-clevr-scene-graph/resolve/main/README.md
579 Bytes
| license: creativeml-openrail-m | |
| base_model: stabilityai/stable-diffusion-2 | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| - lora | |
| inference: true | |
| # LoRA text2image fine-tuning - https://huggingface.co/erkam/sd-clevr-scene-graph | |
| These are LoRA adaption weights for https://huggingface.co/erkam/sd-clevr-scene-graph. The weights were fine-tuned on the erkam/clevr-with-depth dataset. You can find some example images in the following. | |
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