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7b73541
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901c32e
fix: replace anchor tags with image tags in the "About" section for better visual representation of comparison images
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app.py
CHANGED
@@ -65,14 +65,14 @@ with gr.Blocks("ParityError/Interstellar") as demo:
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Due to their size, state-of-the-art models such as FLUX take more than 6 seconds to generate a single image on a high-end H100 GPU.
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While compression techniques can reduce inference time, their impact on quality often remains unclear.
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<
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To bring more transparency around the quality of compressed models:
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- We release “juiced” endpoints for popular image generation models on Replicate, making it easy to play around with our compressed models.
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- We assess the quality of compressed FLUX-APIs from Replicate, fal, Fireworks AI and Together AI according to different benchmarks.
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<
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FLUX-juiced was obtained using a combination of compilation and caching algorithms and we are proud to say that it consistently outperforms alternatives, while delivering performance on par with the original model.
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This combination is available in our Pruna Pro package and can be applied to almost every image generation model.
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Due to their size, state-of-the-art models such as FLUX take more than 6 seconds to generate a single image on a high-end H100 GPU.
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While compression techniques can reduce inference time, their impact on quality often remains unclear.
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<img src="https://huggingface.co/datasets/PrunaAI/documentation-images/resolve/main/inferbench/spider_comparison.png" alt="Spider Comparison">
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To bring more transparency around the quality of compressed models:
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- We release “juiced” endpoints for popular image generation models on Replicate, making it easy to play around with our compressed models.
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- We assess the quality of compressed FLUX-APIs from Replicate, fal, Fireworks AI and Together AI according to different benchmarks.
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<img src="https://huggingface.co/datasets/PrunaAI/documentation-images/resolve/main/inferbench/speed_comparison.png" alt="Speed Comparison">
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FLUX-juiced was obtained using a combination of compilation and caching algorithms and we are proud to say that it consistently outperforms alternatives, while delivering performance on par with the original model.
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This combination is available in our Pruna Pro package and can be applied to almost every image generation model.
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