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- ---
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- license: apache-2.0
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- tags:
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- - text-to-image
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- ---
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- # AuraFlow v0.2
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-
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- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6380ebb8471a4550ff255c62/jMkLXPFVNGdUb7P4nNTqX.png)
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-
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-
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- AuraFlow v0.2 is the fully open-sourced largest flow-based text-to-image generation model. The model was trained with more compute compared to the previous version, [AuraFlow-v0.1](https://huggingface.co/fal/AuraFlow)
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-
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- This model achieves state-of-the-art results on GenEval. Read our [blog post](https://blog.fal.ai/auraflow/) for more technical details. You can also check out the comparison with other models on this gallery [page](https://cloneofsimo.github.io/compare_aura_sd3/).
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-
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- The model is currently in beta. We are working on improving it and the community's feedback is important.
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- Join [fal's Discord](https://discord.gg/fal-ai) to give us feedback and stay in touch with the model development.
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-
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- Credits: A huge thank you to [@cloneofsimo](https://twitter.com/cloneofsimo) and [@isidentical](https://twitter.com/isidentical) for bringing this project to life. It's incredible what two cracked engineers can achieve in
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- such a short period of time. We also extend our gratitude to the incredible researchers whose prior work laid the foundation for our efforts.
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-
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- ## Usage
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-
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- ```bash
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- $ pip install transformers accelerate protobuf sentencepiece
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- $ pip install git+https://github.com/huggingface/diffusers.git
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- ```
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-
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- ```python
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- from diffusers import AuraFlowPipeline
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- import torch
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-
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- pipeline = AuraFlowPipeline.from_pretrained(
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- "fal/AuraFlow-v0.2",
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- torch_dtype=torch.float16,
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- variant="fp16",
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- ).to("cuda")
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-
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- image = pipeline(
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- prompt="close-up portrait of a majestic iguana with vibrant blue-green scales, piercing amber eyes, and orange spiky crest. Intricate textures and details visible on scaly skin. Wrapped in dark hood, giving regal appearance. Dramatic lighting against black background. Hyper-realistic, high-resolution image showcasing the reptile's expressive features and coloration.",
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- height=1024,
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- width=1024,
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- num_inference_steps=50,
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- generator=torch.Generator().manual_seed(666),
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- guidance_scale=3.5,
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- ).images[0]
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-
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- image.save("output.png")
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- ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- "latent_channels": 4,
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- "latents_mean": null,
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- "latents_std": null,
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- "layers_per_block": 2,
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- "norm_num_groups": 32,
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- "out_channels": 3,
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- "sample_size": 1024,
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- "scaling_factor": 0.13025,
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- "shift_factor": null,
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- "up_block_types": [
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- "UpDecoderBlock2D",
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- "UpDecoderBlock2D",
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- "UpDecoderBlock2D",
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- "UpDecoderBlock2D"
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- ],
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- "use_post_quant_conv": true,
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- "use_quant_conv": true
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- }