Update README.md

README.md

@@ -17,6 +17,21 @@ This pipeline shows how to superimpose different text prompts from [Stable Diffu
 <img src="https://huggingface.co/superdiff/superdiff-sd-v1-4/resolve/main/superdiff_small.gif" alt="drawing" style="width:500px;">
 </p>
 
+## Requirements
+
+This pipeline can be run with the following packages & versions:
+- PyTorch 2.5.1
+- Diffusers 0.32.1
+- Accelerate 1.2.1
+- Transformers 4.47.1
+
+You can install these with:
+
+```
+pip install torch
+pip install diffusers accelerate transformers
+```
+
 
 ## Example usage
 
@@ -33,14 +48,14 @@ image.save("superdiff_output.png")
 
 Arguments that can be set by user in `pipeline()`:
 
-- `prompt_1`: text prompt describing first concept to superimpose (e.g. "a flamingo")
-- `prompt_2`: text prompt describing second concept to superimpose (e.g. "a candy cane")
-- `seed`: seed for random noise generator for reproducibility; for non-deterministic outputs, do not provide value
-- `num_inference_steps`: number of denoising steps (we recommend 1000!)
-- `batch_size`: batch size
-- `lift`: bias value that favours generation towards one prompt over the other
-- `guidance_scale`: scale for classifier-free guidance
-- `height`, `width`: height and width of generated images
+- `prompt_1` [required]: text prompt describing first concept to superimpose (e.g. "a flamingo")
+- `prompt_2`[required]: text prompt describing second concept to superimpose (e.g. "a candy cane")
+- `seed`[optional: default=None]: seed for random noise generator for reproducibility; for non-deterministic outputs, set to `None`
+- `num_inference_steps`[optional: default=1000]: number of denoising steps (we recommend 1000!)
+- `batch_size` [optional: default=1]: batch size
+- `lift` [optional: default=0.0]: bias value that favours generation towards one prompt over the other
+- `guidance_scale` [optional: default=7.5]: scale for classifier-free guidance
+- `height`, `width` [optional: default=512]: height and width of generated images
 
 To replicate images from Section 4.2 of the paper, you can use the following: