Instructions to use SPRIGHT-T2I/spright-t2i-sd2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SPRIGHT-T2I/spright-t2i-sd2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SPRIGHT-T2I/spright-t2i-sd2", 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
- Draw Things
- DiffusionBee
updating FID
#2
by agneet - opened
README.md
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@@ -115,8 +115,8 @@ The following table compares our SPRIGHT-T2I model with SD 2.1 across multiple s
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|Method |OA(%) β|VISOR-4(%) β|T2I-CompBench β|FID β|CCMD β|
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|SD v2.1 |47.83 |4.70 |0.1507 |
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|SPRIGHT-T2I (ours)|60.68 |16.15 |0.2133 |
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Our key findings are:
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- Increased the Object Accuracy (OA) score by 26.86%, indicating that we are much better at generating objects mentioned in the input prompt
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|Method |OA(%) β|VISOR-4(%) β|T2I-CompBench β|FID β|CCMD β|
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|------------------|-------|------------|---------------|-----|------|
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|SD v2.1 |47.83 |4.70 |0.1507 |21.646|1.060 |
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|SPRIGHT-T2I (ours)|60.68 |16.15 |0.2133 |16.149|0.512 |
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Our key findings are:
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- Increased the Object Accuracy (OA) score by 26.86%, indicating that we are much better at generating objects mentioned in the input prompt
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