---
license: apache-2.0
base_model:
- black-forest-labs/FLUX.1-dev
base_model_relation: quantized
pipeline_tag: text-to-image
---


# Elastic model: Fastest self-serving models. FLUX.1-dev.

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. 

* __L__: Near lossless model, with less than 1% degradation obtained on corresponding benchmarks.

* __M__: Faster model, with accuracy degradation less than 1.5%.

* __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.

-----


![image/png](https://cdn-uploads.huggingface.co/production/uploads/67991798ae62bd1f17cc22ed/2FXY0tqSGqZq76j5Tz4Vi.png)
![image/png](https://cdn-uploads.huggingface.co/production/uploads/6799fc8e150f5a4014b030ca/CuuzzA_csoRnzbaZq1U1x.png)

## Inference

Currently, our demo model only supports 1024x1024, 768x768 and 512x512 outputs without batching (for B200 - only 1024x1024). 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 FluxPipeline

mode_name = 'black-forest-labs/FLUX.1-dev'
hf_token = ''
device = torch.device("cuda")

pipeline = FluxPipeline.from_pretrained(
    mode_name,
    torch_dtype=torch.bfloat16,
    token=hf_token,
    mode='S'
)
pipeline.to(device)

prompts = ["Kitten eating a banana"]
output = pipeline(prompt=prompts)

for prompt, output_image in zip(prompts, output.images):
    output_image.save((prompt.replace(' ', '_') + '.png'))
```

### Installation


__System requirements:__
* GPUs: H100, L40s, 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 elastic_models[nvidia]\
 --index-url https://thestage.jfrog.io/artifactory/api/pypi/pypi-thestage-ai-production/simple\
 --extra-index-url https://pypi.nvidia.com\
 --extra-index-url https://pypi.org/simple

# or for blackwell support
pip install elastic_models[blackwell]\
 --index-url https://thestage.jfrog.io/artifactory/api/pypi/pypi-thestage-ai-production/simple\
 --extra-index-url https://pypi.nvidia.com\
 --extra-index-url https://pypi.org/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, SSIM and CLIP score. PSNR and SSIM were computed using outputs of original model.
| Metric/Model  | S | M | L | XL | Original |
|---------------|---|---|---|----|----------|
| PSNR          | 30.22 | 30.24 | 30.38 | inf  | inf |
| SSIM          | 0.72 | 0.72 | 0.76 | 1.0  | 1.0 |
| CLIP          | 12.49 | 12.51 | 12.69 | 12.41  | 12.41|


### Latency benchmarks

Time in seconds to generate one image 1024x1024
| GPU/Model | S   | M | L | XL | Original |
|-----------|-----|---|---|----|----------|
| H100      | 2.71 | 3.0 | 3.18 | 4.17  | 6.46 | 
| L40s      | 8.5  | 9.29 | 9.29 | 13.2  | 16|
| B200      | 1.89  | 2.04 | 2.12 | 2.23  | 4.4|
| GeForce RTX 5090      | 5.53  | - | - | -  | -|


## 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