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--- |
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license: apache-2.0 |
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base_model: |
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- stabilityai/stable-diffusion-3.5-large |
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base_model_relation: quantized |
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pipeline_tag: text-to-image |
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--- |
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# Elastic model: Fastest self-serving models. Stable Diffusion 3.5 Large. |
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Elastic models are the models produced by TheStage AI ANNA: Automated Neural Networks Accelerator. ANNA allows you to control model size, latency and quality with a simple slider movement. For each model, ANNA produces a series of optimized models: |
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* __XL__: Mathematically equivalent neural network, optimized with our DNN compiler. |
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* __S__: The fastest model, with accuracy degradation less than 2%. |
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__Goals of Elastic Models:__ |
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* Provide the fastest models and service for self-hosting. |
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* Provide flexibility in cost vs quality selection for inference. |
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* Provide clear quality and latency benchmarks. |
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* Provide interface of HF libraries: transformers and diffusers with a single line of code. |
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* Provide models supported on a wide range of hardware, which are pre-compiled and require no JIT. |
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> It's important to note that specific quality degradation can vary from model to model. For instance, with an S model, you can have 0.5% degradation as well. |
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----- |
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## Inference |
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Currently, our demo model supports 512x512 - 1024x1024 and batch sizes 1-4. This will be updated in the near future. |
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To infer our models, you just need to replace `diffusers` import with `elastic_models.diffusers`: |
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```python |
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import torch |
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from elastic_models.diffusers import StableDiffusion3Pipeline |
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model_name = 'stabilityai/stable-diffusion-3.5-large' |
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hf_token = '' |
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device = torch.device("cuda") |
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pipeline = StableDiffusion3Pipeline.from_pretrained( |
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model_name, |
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torch_dtype=torch.bfloat16, |
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token=hf_token, |
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mode='S' |
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) |
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pipeline.to(device) |
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prompts = ["A cat holding a sign that says hello world"] |
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output = pipeline(prompt=prompts) |
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for prompt, output_image in zip(prompts, output.images): |
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output_image.save((prompt.replace(' ', '_') + '.png')) |
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``` |
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### Installation |
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__System requirements:__ |
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* GPUs: H100, B200 |
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* CPU: AMD, Intel |
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* Python: 3.10-3.12 |
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To work with our models just run these lines in your terminal: |
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```shell |
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pip install thestage |
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pip install 'thestage-elastic-models[nvidia]' --extra-index-url https://thestage.jfrog.io/artifactory/api/pypi/pypi-thestage-ai-production/simple |
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# or for blackwell support |
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pip install 'thestage-elastic-models[blackwell]' --extra-index-url https://thestage.jfrog.io/artifactory/api/pypi/pypi-thestage-ai-production/simple |
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pip install -U --pre torch --index-url https://download.pytorch.org/whl/nightly/cu128 |
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pip install -U --pre torchvision --index-url https://download.pytorch.org/whl/nightly/cu128 |
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pip install flash_attn==2.7.3 --no-build-isolation |
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pip uninstall apex |
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``` |
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Then go to [app.thestage.ai](https://app.thestage.ai), login and generate API token from your profile page. Set up API token as follows: |
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```shell |
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thestage config set --api-token <YOUR_API_TOKEN> |
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``` |
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Congrats, now you can use accelerated models! |
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---- |
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## Benchmarks |
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Benchmarking is one of the most important procedures during model acceleration. We aim to provide clear performance metrics for models using our algorithms. |
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### Quality benchmarks |
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For quality evaluation we have used: PSNR and SSIM. PSNR and SSIM were computed using outputs of original model. |
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| Metric/Model | S | XL | Original | |
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|---------------|---|----|----------| |
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| PSNR | 20.78 | 29.13 | inf | |
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| SSIM | 0.81 | 0.95 | 1.0 | |
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### Latency benchmarks |
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Time in seconds to generate one image 1024x1024 |
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| GPU/Model | S | XL | Original | |
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|-----------|-----|----|----------| |
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| H100 | 3.10 | 3.80 | 6.55 | |
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| B200 | 1.76 | 2.27 | 4.81 | |
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## Links |
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* __Platform__: [app.thestage.ai](https://app.thestage.ai) |
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<!-- * __Elastic models Github__: [app.thestage.ai](app.thestage.ai) --> |
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* __Subscribe for updates__: [TheStageAI X](https://x.com/TheStageAI) |
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* __Contact email__: [email protected] |
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