Instructions to use jasperai/flash-sd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jasperai/flash-sd with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("jasperai/flash-sd") 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
Update README.md
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README.md
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@@ -16,7 +16,7 @@ This model is a **26.4M** LoRA distilled version of SD1.5 model that is able to
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<img style="width:700px;" src="images/
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# How to use?
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# Training Details
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The model was trained for 20k iterations on 2 H100 GPUs (representing approx. **26 hours** of training). Please refer to the [paper]() for further parameters details.
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**Metrics on COCO 2017 validation set (Table 1)**
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- FID-5k: 22.6 (2 NFE) / 22.5 (4 NFE)
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<p align="center">
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<img style="width:700px;" src="images/flash_sd.jpg">
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</p>
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# How to use?
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</p>
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# Training Details
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The model was trained for 20k iterations on 2 H100 GPUs (representing approx. a total **26 GPU hours** of training). Please refer to the [paper]() for further parameters details.
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**Metrics on COCO 2017 validation set (Table 1)**
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- FID-5k: 22.6 (2 NFE) / 22.5 (4 NFE)
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