Update README.md

README.md

@@ -12,21 +12,20 @@ license_link: LICENSE
 <img src="ad3.jpg" alt="00205_" />
 </div> 
 
-Bokeh 3.5 Medium is a **Continue-training** model built upon the **stable diffusion 3.5 medium** foundation, further refined using a **500W high-resolution open-source dataset** with rigorous **aesthetic curation**. This ensures outstanding image quality with **DSLR-level fidelity** for natural images, fine detail preservation, and enhanced controllability.
+Bokeh 3.5 Medium is based on **Stable Diffusion 3.5 Medium** as its foundation model, using a 5M high-resolution open-source dataset that underwent rigorous quality and **aesthetic screening** for post-training, ensuring **excellent image quality**, **high fidelity of natural images**, preservation of fine **details**, and enhanced **controllability**.
 
 This model is released under the Stability Community License.
 For more details, visit [Tensor.Art](https://tensor.art) or [TusiArt](https://tusiart.com) to explore additional resources and useful information.
 
 ## Overview
 
-- **Continue-training on SD3.5M**, leveraging a large-scale **500W high-resolution dataset**, carefully curated for aesthetic quality.
-- **Supports hybrid short/long caption training** for enhanced natural language understanding.
-  - **Short Captions:** Focus on core image features.
-  - **Long Captions:** Provide broader scene context and atmospheric details.
+- Continued training on **SD3.5M**, utilizing carefully curated high-resolution training data to achieve excellent image quality.
+- Trained with mixed short/long natural language captions.
+  - **Short Captions:** Focus on the core subject content of the image.
+  - **Long Captions:** Provide broader descriptions of the scene environment and atmosphere.
 - **Recommended Resolutions:**  
   `1920x1024`, `1728x1152`, `1152x1728`, `1280x1664`, `1440x1440`
-- **Best Quality Training Resolution:** `1440x1440`
-- **Supports LoRA fine-tuning.**
+- Powerful customized **fine-tuning performance** that can be widely used for **downstream production tasks**.
 
 ## Advantages
 
@@ -61,20 +60,9 @@ For more details, visit [Tensor.Art](https://tensor.art) or [TusiArt](https://tu
 - **Main subject** (e.g., `"Close-up of a macaw"`)  
 - **Detailed features** (e.g., `"vivid feathers, sharp beak"`)  
 - **Background environment** (e.g., `"dimly lit environment"`)  
-- **Atmospheric description** (e.g., `"soft warm lighting, cinematic mood"`)  
-
-### Best Practices:
-- **Avoid overly complex prompts**, as the model already has strong text encoding. Overloading details can cause **T5 hallucination artifacts**, reducing image quality.  
-- **Do not use excessively short prompts** (e.g., single words or 2-3 tokens) unless combined with **LoRA or Image2Image (i2i)** techniques.  
-- **Avoid mixing too many unrelated concepts**, as this can lead to visual distortions and unwanted artifacts.  
+- **Atmospheric description** (e.g., `"soft warm lighting, cinematic mood"`)   
 - **Optimal token length:** **30-70 tokens**.  
 
-### Negative Prompting
-- **Negative prompts strongly influence image quality.**  
-- Ensure they **do not contradict the main subject** to avoid degrading the output.  
-
-
-
 ## Example Output
 Using diffusers：
 ```python
@@ -106,15 +94,6 @@ For **LoRA fine-tuning**, the following tools and settings are recommended:
 - **Kohya_ss:** [GitHub Repository](https://github.com/bmaltais/kohya_ss.git)
 - **Simple Tuner:** [GitHub Repository](https://github.com/bghira/SimpleTuner)
 
-### ⚙️ Suggested Training Settings
-```bash
---Resolution 1440x1440
---t5xxl_max_token_length 154
---optimizer_type AdamW8bit
---mmdit_lr 1e-4
---text_encoder_lr 5e-5
-```
-
 ## Contact
 * Website: https://tensor.art  https://tusiart.com
 * Developed by: TensorArt