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--- |
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base_model: |
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- stabilityai/stable-diffusion-3.5-medium |
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tags: |
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- art |
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--- |
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# Bokeh 3.5 Medium |
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<div align="center"> |
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<img src="ad2.jpg" alt="00205_" width="620"/> |
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</div> |
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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, fine detail preservation, and enhanced controllability. |
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This model is released under the Stability Community License. |
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For more details, visit [Tensor.Art](https://tensor.art) or [TusiArt](https://tusiart.com) to explore additional resources and useful information. |
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## Overview |
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- **Continue-training on SD3.5M**, leveraging a large-scale **500W high-resolution dataset**, carefully curated for aesthetic quality. |
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- **Supports hybrid short/long caption training** for enhanced natural language understanding. |
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- **Short Captions:** Focus on core image features. |
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- **Long Captions:** Provide broader scene context and atmospheric details. |
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- **Recommended Resolutions:** |
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`1920x1024`, `1728x1152`, `1152x1728`, `1280x1664`, `1440x1440` |
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- **Best Quality Training Resolution:** `1440x1440` |
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- **Supports LoRA fine-tuning.** |
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## Advantages |
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### 🖼️ High-Quality Image Generation |
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- **State-of-the-art visual fidelity** with improved detail extraction and **aesthetic consistency**. |
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- **Enhanced resolution support** up to **200W pixels**, ensuring highly detailed image outputs. |
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- **Carefully curated dataset** ensures better composition, lighting, and overall artistic appeal. |
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### 🎯 Powerful Custom Fine-Tuning |
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- **Exceptional LoRA training support**, making it highly effective for: |
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- Photography |
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- 3D Rendering |
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- Illustration |
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- Concept Art |
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### ⚡ Efficient Inference & Training |
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- **Low hardware requirements for inference:** |
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- **Medium model:** 9GB VRAM (without T5) |
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- **Full weights inference:** 16GB VRAM (suitable for local deployment) |
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- **LoRA fine-tuning VRAM requirement:** 12GB - 32GB |
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## Known Issues |
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- **Potential human anatomy inconsistencies.** |
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- **Limited ability to generate photorealistic images.** |
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- **Some concepts may suffer from aesthetic quality issues.** |
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## Prompting Guide |
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### Use a structured prompt combining: |
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- **Main subject** (e.g., `"Close-up of a macaw"`) |
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- **Detailed features** (e.g., `"vivid feathers, sharp beak"`) |
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- **Background environment** (e.g., `"dimly lit environment"`) |
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- **Atmospheric description** (e.g., `"soft warm lighting, cinematic mood"`) |
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### Best Practices: |
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- **Avoid overly complex prompts**, as the model already has strong text encoding. Overloading details can cause **T5 hallucination artifacts**, reducing image quality. |
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- **Do not use excessively short prompts** (e.g., single words or 2-3 tokens) unless combined with **LoRA or Image2Image (i2i)** techniques. |
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- **Avoid mixing too many unrelated concepts**, as this can lead to visual distortions and unwanted artifacts. |
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- **Optimal token length:** **30-70 tokens**. |
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### Negative Prompting |
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- **Negative prompts strongly influence image quality.** |
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- Ensure they **do not contradict the main subject** to avoid degrading the output. |
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## Example Output |
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Using diffusers： |
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```python |
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import torch |
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from diffusers import StableDiffusion3Pipeline |
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pipe = StableDiffusion3Pipeline.from_pretrained("/mnt/share/pcm_outputs/bokeh_3.5_medium", torch_dtype=torch.bfloat16) |
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pipe = pipe.to("cuda") |
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image = pipe( |
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"Close-up of a macaw, dimly lit environment", |
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num_inference_steps=28, |
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guidance_scale=4, |
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height=1920, |
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width=1024, |
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).images[0] |
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image.save("macaw.jpg") |
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``` |
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Using comfyui: |
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To use this workflow in **ComfyUI**, download the JSON file and load it: |
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[Download Workflow](bk_workflow.json) |
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## Recommended Training Configuration |
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For **LoRA fine-tuning**, the following tools and settings are recommended: |
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### 🔧 Training Tools |
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- **Kohya_ss:** [GitHub Repository](https://github.com/bmaltais/kohya_ss.git) |
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- **Simple Tuner:** [GitHub Repository](https://github.com/bghira/SimpleTuner) |
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### ⚙️ Suggested Training Settings |
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```bash |
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--Resolution 1440x1440 |
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--t5xxl_max_token_length 154 |
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--optimizer_type AdamW8bit |
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--mmdit_lr 1e-4 |
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--text_encoder_lr 5e-5 |
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``` |
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## Contact |
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* Website: https://tensor.art https://tusiart.com |
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* Developed by: TensorArt |
