Update README.md

README.md

@@ -1,19 +1,4 @@
-# flux-schnell-edge-inference
+Transformer copy of slobers/Flux.1.Schnella
 
-This holds the baseline for the FLUX Schnel NVIDIA GeForce RTX 4090 contest, which can be forked freely and optimized
-
-Some recommendations are as follows:
-- Installing dependencies should be done in `pyproject.toml`, including git dependencies
-- HuggingFace models should be specified in the `models` array in the `pyproject.toml` file, and will be downloaded before benchmarking
-    - The pipeline does **not** have internet access so all dependencies and models must be included in the `pyproject.toml`
-    - Compiled models should be hosted on HuggingFace and included in the `models` array in the `pyproject.toml` (rather than compiling during loading). Loading time matters far more than file sizes
-- Avoid changing `src/main.py`, as that includes mostly protocol logic. Most changes should be in `models` and `src/pipeline.py`
-- Ensure the entire repository (excluding dependencies and HuggingFace models) is under 16MB
-
-For testing, you need a docker container with pytorch and ubuntu 22.04.
-You can download your listed dependencies with `uv`, installed with:
-```bash
-pipx ensurepath
-pipx install uv
-```
-You can then relock with `uv lock`, and then run with `uv run start_inference`
++ +vae encoder pruned to 0.15%
++ +vae decoder pruned to 2%