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