Text-to-Image
Diffusers
English
controllable text-to-image generation
diffusion models
3D layout control
occlusion reasoning
Instructions to use va1bhavagrawa1/seethrough3d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use va1bhavagrawa1/seethrough3d with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("va1bhavagrawa1/seethrough3d", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit History
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Move metadata to YAML header and add paper links (#1)
- Move metadata to YAML header and add paper links (7233a73ec8fd5b399d6a90f94542d7ad8d410c73) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>a1158c3
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updated download checkpoint procedure
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corrected imports
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updated gitignore
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Upload checkpoints/seethrough3d_release/lora.safetensors with huggingface_hub
Verified signature03efb65 -
commit
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updated README
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Add Hugging Face Dataset button to README
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made some changes
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Update README with Gradio Wiki link and fix notebook path
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Added wiki for the Gradio Demo interface
24054e5 -
setup repo as python package
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updated README.md
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added README.md
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removed extra files
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updated requirements.txt
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working demo, organized files
fa94fe3
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made some corrections
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working training code with both the stages
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first commit
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