Update README.md

README.md

@@ -32,13 +32,13 @@ metrics:
 
 # About the model
 -----------------
-This model is a fine-tune of Stable Diffusion v1.5, trained on the [Imaginary Network Expanded Dataset](https://github.com/Sygil-Dev/INE-dataset), with the big advantage of allowing the use of multiple namespaces (labeled tags) to control various parts of the final generation.
+This model is a fine-tune of Stable Diffusion, trained on the [Imaginary Network Expanded Dataset](https://github.com/Sygil-Dev/INE-dataset), with the big advantage of allowing the use of multiple namespaces (labeled tags) to control various parts of the final generation.
 While current models usually are prone to “context errors” and need substantial negative prompting to set them on the right track, the use of namespaces in this model (eg. “species:seal” or “studio:dc”) stop the model from misinterpreting a seal as the singer Seal, or DC Comics as Washington DC. 
 This model is also able to understand other languages besides English, currently it can partially understand prompts in Chinese, Japanese and Spanish. More training is already being done in order to have the model completely understand those languages and have it work just like how it works with English prompts.
 
 As the model is fine-tuned on a wide variety of content, it’s able to generate many types of images and compositions, and easily outperforms the original model when it comes to portraits, architecture, reflections, fantasy, concept art, anime, landscapes and a lot more without being hyper-specialized like other community fine-tunes that are currently available. 
 
-**Note: The prompt engineering techniques needed are slightly different from other fine-tunes and the original SD 1.5, so while you can still use your favorite prompts, for best results you might need to tweak them to make use of namespaces. A more detailed guide will be available shortly, but the examples here and this [Dataset Explorer](https://huggingface.co/spaces/Sygil/INE-dataset-explorer) should be able to start you off on the right track.
+**Note: The prompt engineering techniques needed are slightly different from other fine-tunes and the original Stable Diffusion model, so while you can still use your favorite prompts, for best results you might need to tweak them to make use of namespaces. A more detailed guide will be available later on, but you can use the tags and namespaces found here [Dataset Explorer](https://huggingface.co/spaces/Sygil/INE-dataset-explorer) should be able to start you off on the right track.
 
 If you find our work useful, please consider supporting us on [OpenCollective](https://opencollective.com/sygil_dev)! 
 
@@ -81,8 +81,8 @@ image.save("fantasy_forest_illustration.png")
 - If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed).
 
 ## Available Checkpoints:
-  - [Sygil Diffusion v0.1](https://huggingface.co/Sygil/Sygil-Diffusion/blob/main/sygil-diffusion-v0.1.ckpt): Trained for 800,000 steps
-  - [sygil-diffusion-v0.2_1708350_lora.ckpt](https://huggingface.co/Sygil/Sygil-Diffusion/blob/main/sygil-diffusion-v0.2_1708350_lora.ckpt): Resumed from Sygil Diffusion v0.1 and now up to 1.70 million steps.
+  - [Sygil Diffusion v0.1](https://huggingface.co/Sygil/Sygil-Diffusion/blob/main/sygil-diffusion-v0.1.ckpt): Trained on Stable Diffusion 1.5 for 800,000 steps.
+  - [sygil-diffusion-v0.2](https://huggingface.co/Sygil/Sygil-Diffusion/blob/main/sygil-diffusion-v0.2.ckpt): Resumed from Sygil Diffusion v0.1 and trained for a total of 1.77 million steps.
 
 ## Training
 
@@ -92,7 +92,7 @@ The model was trained on the following dataset:
 
 **Hardware and others**
 - **Hardware:** 1 x Nvidia RTX 3050 8GB GPU
-- **Hours Trained:** 610 hours approximately.
+- **Hours Trained:** 630 hours approximately.
 - **Optimizer:** AdamW
 - **Adam Beta 1**: 0.9
 - **Adam Beta 2**: 0.999
@@ -105,7 +105,7 @@ The model was trained on the following dataset:
 - **Lora unet Learning Rate**: 1e-7
 - **Lora Text Encoder Learning Rate**: 1e-7
 - **Resolution**: 512 pixels
-- **Total Training Steps:** 1,489,983
+- **Total Training Steps:** 1,770,717
 
 Developed by: [ZeroCool94](https://github.com/ZeroCool940711) at [Sygil-Dev](https://github.com/Sygil-Dev/)