Create README.md

README.md

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+---
+license: creativeml-openrail-m
+base_model: SG161222/Realistic_Vision_V4.0
+datasets:
+- recastai/LAION-art-EN-improved-captions
+tags:
+- bksdm
+- bksdm-ttiny
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- diffusers
+inference: true
+---
+    
+# Text-to-image Distillation
+
+This pipeline was distilled from **SG161222/Realistic_Vision_V4.0** on a Subset of **recastai/LAION-art-EN-improved-captions** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Portrait of a pretty girl']: 
+
+![val_imgs_grid](./val_imgs_grid.png)
+
+
+This Pipeline is based upon [the paper](https://arxiv.org/pdf/2305.15798.pdf). Training Code can be found [here](https://github.com/segmind/BKSDM).
+
+## Pipeline usage
+
+You can use the pipeline like so:
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipeline = DiffusionPipeline.from_pretrained("Warlord-K/BKSDM-Tiny-125K", torch_dtype=torch.float16)
+#Load LoRA finetune
+pipeline.load_lora_weights("segmind/tiny_lora_mxtun3_style", weight_name="sd15_tiny_mxtun3_style_lora.safetensors")
+prompt = "Portrait of a pretty girl"
+image = pipeline(prompt).images[0]
+image.save("my_image.png")
+```
+
+## Training info
+
+These are the key hyperparameters used during training:
+
+* Steps: 125000
+* Learning rate: 1e-4
+* Batch size: 32
+* Gradient accumulation steps: 4
+* Image resolution: 512
+* Mixed-precision: fp16
+