README.md

---
library_name: diffusers
tags:
- pruna-ai
base_model:
- black-forest-labs/FLUX.1-Depth-dev
---

# Model Card for PrunaAI/FLUX.1-Fill-dev-smashed

This model was created using the [pruna](https://github.com/PrunaAI/pruna) library. Pruna is a model optimization framework built for developers, enabling you to deliver more efficient models with minimal implementation overhead.

## Usage

First things first, you need to install the pruna library:

```bash
pip install pruna
pip install git+https://github.com/asomoza/image_gen_aux.git

```

You can [use the diffusers library to load the model](https://huggingface.co/PrunaAI/FLUX.1-Fill-dev-smashed?library=diffusers) but this might not include all optimizations by default.

To ensure that all optimizations are applied, use the pruna library to load the model using the following code:

```python
from pruna import PrunaModel

import torch
from diffusers import FluxControlPipeline, FluxTransformer2DModel
from diffusers.utils import load_image
from image_gen_aux import DepthPreprocessor

pipe = PrunaModel.from_hub(
    "PrunaAI/FLUX.1-Fill-dev-smashed"
)
prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")

processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
control_image = processor(control_image)[0].convert("RGB")

image = pipe(
    prompt=prompt,
    control_image=control_image,
    height=1024,
    width=1024,
    num_inference_steps=30,
    guidance_scale=10.0,
    generator=torch.Generator().manual_seed(42),
).images[0]
image.save("output.png")

```

After loading the model, you can use the inference methods of the original model. Take a look at the [documentation](https://pruna.readthedocs.io/en/latest/index.html) for more usage information.

## Smash Configuration

The compression configuration of the model is stored in the `smash_config.json` file, which describes the optimization methods that were applied to the model.

```bash
{
    "batcher": null,
    "cacher": "fora",
    "compiler": "torch_compile",
    "factorizer": "qkv_diffusers",
    "pruner": null,
    "quantizer": null,
    "fora_interval": 2,
    "fora_start_step": 2,
    "torch_compile_backend": "inductor",
    "torch_compile_dynamic": null,
    "torch_compile_fullgraph": true,
    "torch_compile_make_portable": false,
    "torch_compile_max_kv_cache_size": 400,
    "torch_compile_mode": "default",
    "torch_compile_seqlen_manual_cuda_graph": 100,
    "torch_compile_target": "model",
    "batch_size": 1,
    "device": "cuda",
    "save_fns": [
        "save_before_apply",
        "save_before_apply"
    ],
    "load_fns": [
        "diffusers"
    ],
    "reapply_after_load": {
        "factorizer": "qkv_diffusers",
        "pruner": null,
        "quantizer": null,
        "cacher": "fora",
        "compiler": "torch_compile",
        "batcher": null
    }
}
```

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