Instructions to use haopt/scflow_t2i with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use haopt/scflow_t2i with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("haopt/scflow_t2i", 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
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<emp><sup>†</sup>Work done while at VinAI Research</emp>
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## Model
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We present a distilled Text-to-Image (T2I) model that supports both few-step and single-step generation. Distilled from XCLiu/instaflow_0_9B_from_sd_1_5, our model achieves an FID of 11.91 for 1-NFE generation on the COCO2014 benchmark.
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<emp><sup>†</sup>Work done while at VinAI Research</emp>
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## Model details
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We present a distilled Text-to-Image (T2I) model that supports both few-step and single-step generation. Distilled from XCLiu/instaflow_0_9B_from_sd_1_5, our model achieves an FID of 11.91 for 1-NFE generation on the COCO2014 benchmark.
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