Instructions to use UCLA-AGI/SPIN-Diffusion-iter1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use UCLA-AGI/SPIN-Diffusion-iter1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("UCLA-AGI/SPIN-Diffusion-iter1", 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
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pipeline_tag: text-to-image
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Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation (https://huggingface.co/papers/2402.10210)
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# SPIN-Diffusion-iter1
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This model is a self-play fine-tuned diffusion model at iteration 1 from [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) using synthetic data based on
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## Model Details
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pipeline_tag: text-to-image
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**Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation** (https://huggingface.co/papers/2402.10210)
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# SPIN-Diffusion-iter1
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This model is a self-play fine-tuned diffusion model at iteration 1 from [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) using synthetic data based on the winner images of the [yuvalkirstain/pickapic_v2](https://huggingface.co/datasets/yuvalkirstain/pickapic_v2) dataset.
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## Model Details
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