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update conversion script for SANA-1.5 and SANA-Sprint #11082
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90fe8e3
1. update conversion script for sana1.5;
lawrence-cj 9721078
seperate sana and sana-sprint conversion scripts;
lawrence-cj f2e058b
update for upstream
lawrence-cj 065628f
Merge branch 'sana-sprint' into sana-sprint-js
lawrence-cj d35bba3
fix the } bug
lawrence-cj a26ece2
add a doc for SanaSprintPipeline;
lawrence-cj 1f47187
minor update;
lawrence-cj e68f539
make style && make quality
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<!-- Copyright 2024 The HuggingFace Team. All rights reserved. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. --> | ||
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# SanaSprintPipeline | ||
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<div class="flex flex-wrap space-x-1"> | ||
<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/> | ||
</div> | ||
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[SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillation](https://huggingface.co/papers/2503.09641) from NVIDIA and MIT HAN Lab, by Junsong Chen, Shuchen Xue, Yuyang Zhao, Jincheng Yu, Sayak Paul, Junyu Chen, Han Cai, Enze Xie, Song Han | ||
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The abstract from the paper is: | ||
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*This paper presents SANA-Sprint, an efficient diffusion model for ultra-fast text-to-image (T2I) generation. SANA-Sprint is built on a pre-trained foundation model and augmented with hybrid distillation, dramatically reducing inference steps from 20 to 1-4. We introduce three key innovations: (1) We propose a training-free approach that transforms a pre-trained flow-matching model for continuous-time consistency distillation (sCM), eliminating costly training from scratch and achieving high training efficiency. Our hybrid distillation strategy combines sCM with latent adversarial distillation (LADD): sCM ensures alignment with the teacher model, while LADD enhances single-step generation fidelity. (2) SANA-Sprint is a unified step-adaptive model that achieves high-quality generation in 1-4 steps, eliminating step-specific training and improving efficiency. (3) We integrate ControlNet with SANA-Sprint for real-time interactive image generation, enabling instant visual feedback for user interaction. SANA-Sprint establishes a new Pareto frontier in speed-quality tradeoffs, achieving state-of-the-art performance with 7.59 FID and 0.74 GenEval in only 1 step — outperforming FLUX-schnell (7.94 FID / 0.71 GenEval) while being 10× faster (0.1s vs 1.1s on H100). It also achieves 0.1s (T2I) and 0.25s (ControlNet) latency for 1024×1024 images on H100, and 0.31s (T2I) on an RTX 4090, showcasing its exceptional efficiency and potential for AI-powered consumer applications (AIPC). Code and pre-trained models will be open-sourced.* | ||
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<Tip> | ||
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Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. | ||
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</Tip> | ||
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This pipeline was contributed by [lawrence-cj](https://github.com/lawrence-cj), [shuchen Xue](https://github.com/scxue) and [Enze Xie](https://github.com/xieenze). The original codebase can be found [here](https://github.com/NVlabs/Sana). The original weights can be found under [hf.co/Efficient-Large-Model](https://huggingface.co/Efficient-Large-Model/). | ||
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Available models: | ||
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| Model | Recommended dtype | | ||
|:-------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------:| | ||
| [`Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers) | `torch.bfloat16` | | ||
| [`Efficient-Large-Model/Sana_Sprint_0.6B_1024px_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_Sprint_0.6B_1024px_diffusers) | `torch.bfloat16` | | ||
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Refer to [this](https://huggingface.co/collections/Efficient-Large-Model/sana-sprint-67d6810d65235085b3b17c76) collection for more information. | ||
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Note: The recommended dtype mentioned is for the transformer weights. The text encoder must stay in `torch.bfloat16` and VAE weights must stay in `torch.bfloat16` or `torch.float32` for the model to work correctly. Please refer to the inference example below to see how to load the model with the recommended dtype. | ||
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## Quantization | ||
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Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model. | ||
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Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`SanaSprintPipeline`] for inference with bitsandbytes. | ||
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```py | ||
import torch | ||
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, SanaTransformer2DModel, SanaSprintPipeline | ||
from transformers import BitsAndBytesConfig as BitsAndBytesConfig, AutoModel | ||
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quant_config = BitsAndBytesConfig(load_in_8bit=True) | ||
text_encoder_8bit = AutoModel.from_pretrained( | ||
"Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers", | ||
subfolder="text_encoder", | ||
quantization_config=quant_config, | ||
torch_dtype=torch.bfloat16, | ||
) | ||
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quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True) | ||
transformer_8bit = SanaTransformer2DModel.from_pretrained( | ||
"Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers", | ||
subfolder="transformer", | ||
quantization_config=quant_config, | ||
torch_dtype=torch.bfloat16, | ||
) | ||
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pipeline = SanaSprintPipeline.from_pretrained( | ||
"Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers", | ||
text_encoder=text_encoder_8bit, | ||
transformer=transformer_8bit, | ||
torch_dtype=torch.bfloat16, | ||
device_map="balanced", | ||
) | ||
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prompt = "a tiny astronaut hatching from an egg on the moon" | ||
image = pipeline(prompt).images[0] | ||
image.save("sana.png") | ||
``` | ||
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## SanaSprintPipeline | ||
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[[autodoc]] SanaSprintPipeline | ||
- all | ||
- __call__ | ||
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## SanaPipelineOutput | ||
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[[autodoc]] pipelines.sana.pipeline_output.SanaPipelineOutput |
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