updating readme

README.md

@@ -23,7 +23,7 @@ Then install all required libraries, preferably inside a virtual environment:
     source .venv/bin/activate
     pip install -r requirements.txt
 
-Then run the web app:
+Then run the web app either via Python itself or through Gradio:
 
     python app.py
 
@@ -31,3 +31,46 @@ You can use Gradio to run the web app which offers a file watcher in case you ma
 
     gradio app.py
 
+# Walk-Through
+
+## Patience is king
+If you start the process for a given model the first time, it may take a while, because the backend needs to download the full model. Depending on the model, this requires **multiple GigaByte** of space on your device. This only happens **once**, except on *Huggingface*, where the server cache will be purged. 
+
+## Steps
+This describes the minimal required steps to utilize this interface. Most default parameters don't need to be changed in order to work properly.
+
+1. Select a **device**. A decent **gpu** is recommended. 
+2. Choose a **model**. If a fine-tuned model requires a trigger token, it will be added automatically. The **safety checker** options allows you to not render nsfw content, but in some cases this also produces black images for not harmful content.
+2. You *may* select a different scheduler. The scheduler controls **quality** and **performance**. 
+3. Now you can define your **prompt** and **negative prompt**. 
+4. The value for **inference steps** controls how many iteration your generation process will run. The higher this value, the longer the process takes and the better the image quality is. You should start with a *lower value* to see how the model interpretes your prompt. As soon as you got a satisfying result, increase this value to produce high quality output. 
+5. The **manual seed** is a way to either force randomisastion or creation of the same output every time you run theprocess. Keep this field empty to enable random outputs. 
+6. Use **guidance scale** to define how strict the model interprets your prompt.
+7. Hit **run**!
+
+## Hints
+The **re-run** button runs the process again and only applies changes you made to the **Inference settings** section. While the **run** button execute the whole process from the scratch.
+
+You have two options to persist your selected configuration: Either you **copy the code** to an environment where you can execute Python code (Google Colab). Or, after every succesful run, head to the bottom of the page. There's a table containing a link to this interface containing the whole configuration. 
+
+# Areas
+## Model specific settings
+This allows you to select any model hosted on Huggingface. Some models are fine-tuned and require a **trigger token** to be activated, like https://huggingface.co/sd-dreambooth-library/herge-style.
+
+**Refiner** is a way to improve the quality of your image by re-processing it a second time. 
+
+The pipeline supports a way to prevent nswf-content to be created. I figured this does not always work properly, so those to options allow you to disable this feature. 
+
+## Scheduler/Solver
+
+This is the part of the process, that manipulates the output from the model every loop/epoch.
+
+## Auto Encoder
+The auto encoder is responsible for the encoding and decoding process from the input to the output. **VAE slicing** and **VAE tiling** are parameters to improve performance here.
+
+## Adapters
+Adapters allow you to modify or control the output, e.g. apply specific styles. This interface supports **Textual inversion** and **LoRA**
+
+# Customization
+
+Update the file ```appConfig.json``` to add more models. Some models need you to accept their license agreement before you can access them, like https://huggingface.co/stabilityai/stable-diffusion-3-medium.