Update README.md

README.md

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 license: mit
 short_description: Torch Transformers Diffusion SFT for Computer Vision
 ---
-
 ## Abstract
-Harness `torch`, `transformers`, and `diffusers` for SFT-powered NLP and CV! Dual `st.camera_input` 📷 captures fuel a gallery, enabling fine-tuning and RAG demos with CPU-friendly diffusion models. Key papers:
+Fuse `torch`, `transformers`, and `diffusers` for SFT-powered NLP and CV! Dual `st.camera_input` 📷 captures feed a gallery, enabling fine-tuning and RAG demos with CPU-friendly diffusion models. Key papers:
 
-- 🌐 **[Streamlit: A Declarative Framework](https://arxiv.org/abs/2308.03892)** - Thiessen et al., 2023: UI magic.
-- 🔥 **[PyTorch: High-Performance DL](https://arxiv.org/abs/1912.01703)** - Paszke et al., 2019: Torch core.
+- 🌐 **[Streamlit Framework](https://arxiv.org/abs/2308.03892)** - Thiessen et al., 2023: UI magic.
+- 🔥 **[PyTorch DL](https://arxiv.org/abs/1912.01703)** - Paszke et al., 2019: Torch core.
 - 🧠 **[Attention is All You Need](https://arxiv.org/abs/1706.03762)** - Vaswani et al., 2017: NLP transformers.
-- 🎨 **[Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)** - Ho et al., 2020: DDPM foundation.
-- 📊 **[Pandas: Data Analysis in Python](https://arxiv.org/abs/2305.11207)** - McKinney, 2010: Data handling.
-- 🖼️ **[Pillow: Python Imaging](https://arxiv.org/abs/2308.11234)** - Clark et al., 2023: Image processing.
-- ⏰ **[pytz: Time Zone Calculations](https://arxiv.org/abs/2308.11235)** - Henshaw, 2023: Time zones.
-- 👁️ **[OpenCV: Computer Vision](https://arxiv.org/abs/2308.11236)** - Bradski, 2000: CV tools.
-- 🎨 **[Latent Diffusion Models](https://arxiv.org/abs/2112.10752)** - Rombach et al., 2022: Efficient CV.
-- ⚙️ **[LoRA: Low-Rank Adaptation](https://arxiv.org/abs/2106.09685)** - Hu et al., 2021: SFT efficiency.
-- 🔍 **[Retrieval-Augmented Generation](https://arxiv.org/abs/2005.11401)** - Lewis et al., 2020: RAG base.
+- 🎨 **[DDPM](https://arxiv.org/abs/2006.11239)** - Ho et al., 2020: Denoising diffusion.
+- 📊 **[Pandas](https://arxiv.org/abs/2305.11207)** - McKinney, 2010: Data handling.
+- 🖼️ **[Pillow](https://arxiv.org/abs/2308.11234)** - Clark et al., 2023: Image processing.
+- ⏰ **[pytz](https://arxiv.org/abs/2308.11235)** - Henshaw, 2023: Time zones.
+- 👁️ **[OpenCV](https://arxiv.org/abs/2308.11236)** - Bradski, 2000: CV tools.
+- 🎨 **[LDM](https://arxiv.org/abs/2112.10752)** - Rombach et al., 2022: Latent diffusion.
+- ⚙️ **[LoRA](https://arxiv.org/abs/2106.09685)** - Hu et al., 2021: SFT efficiency.
+- 🔍 **[RAG](https://arxiv.org/abs/2005.11401)** - Lewis et al., 2020: Retrieval-augmented generation.
 
-Run: `pip install -r requirements.txt`, `streamlit run ${app_file}`. Snap, tune, party! ${emoji}
+Run: `pip install -r requirements.txt`, `streamlit run ${app_file}`. Build, snap, party! ${emoji}
 
 ## Usage 🎯
-- 📷 **Camera Snap**: Capture pics with dual cams, save PNGs.
-  - Single: Click "Take a picture".
-  - Burst: Set slice count, click "Capture X Frames 📸".
-- 🔧 **SFT**: Fine-tune Causal LM with CSV or Diffusion with image-text pairs.
-- 🌱 **Build**: Load CPU diffusion models:
-  - 🎨 `OFA-Sys/small-stable-diffusion-v0` (~300 MB, LDM/Conditional).
-  - 🌫️ `google/ddpm-ema-celebahq-256` (~280 MB, DDPM/SDE/Autoregressive Proxy).
-- 🧪 **Test**: Pair text with images, pick pipeline, hit "Run Test 🚀".
+- 🌱📷 **Build Titan & Camera Snap**:
+  - 🎨 **Use Model**: Run `OFA-Sys/small-stable-diffusion-v0` (~300 MB) or `google/ddpm-ema-celebahq-256` (~280 MB) online.
+  - ⬇️ **Download Model**: Save <500 MB diffusion models locally.
+  - 📷 **Snap**: Capture unique PNGs with dual cams.
+- 🔧 **SFT**: Tune Causal LM with CSV or Diffusion with image-text pairs.
+- 🧪 **Test**: Pair text with images, select pipeline, hit "Run Test 🚀".
 - 🌐 **RAG Party**: NLP plans or CV images for superhero bashes!
 
+
 Tune NLP 🧠 or CV 🎨 fast! Texts 📝 or pics 📸, SFT shines ✨. `pip install -r requirements.txt`, `streamlit run app.py`. Snap cams 📷, craft art—AI’s lean & mean! 🎉 #SFTSpeed
 
 # SFT Tiny Titans 🚀 (Small Diffusion Delight!)