Update README.md

README.md

@@ -1,3 +1,67 @@
 ---
-license: openrail
+license: creativeml-openrail-m
+base_model: SG161222/Realistic_Vision_V4.0
+datasets:
+- recastai/LAION-art-EN-improved-captions
+tags:
+- 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 tiny-sd model. 
+
+![val_imgs_grid](./grid_tiny.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("segmind/tiny-sd-mxtune", torch_dtype=torch.float16)
+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
+
+## Finetune info
+
+These are the key hyperparameters used during training:
+
+* Steps: 8000 / 100000
+* Learning rate: 1e-5
+* Batch size: 24
+* Gradient accumulation steps: 1
+* Image resolution: 768
+* Mixed-precision: fp16
+
+## Speed Comparision
+
+We have observed that the distilled models are upto 80% faster than the Base SD1.5 Models. Below is a comparision on an A100 80GB.
+
+![graph](./graph.png)
+![comparision](./comparision1.png)
+
+Below is the code for benchmarking the speeds
+