readme: ---
  license: creativeml-openrail-m
  tags:
  - pytorch
  - diffusers
  - stable-diffusion
  - text-to-image
  - diffusion-models-class
  - dreambooth-hackathon
  - landscape
  widget:
  - text: isometric starcraft ashworld terrain
  datasets:
  - wdcqc/starcraft-remastered-melee-maps
  library_name: diffusers
  ---
  
  # DreamBooth model for Starcraft:Remastered terrain
  
  ![INDEX_FULL.JPG](https://huggingface.co/wdcqc/starcraft-terrain-64x64/resolve/main/outputs/index_full.jpg)
  
  This is a Stable Diffusion model fine-tuned on Starcraft terrain images with DreamBooth. It can be used by adding the `instance_prompt`: **isometric starcraft _tileset_ terrain**
  
  The _tileset_ should be one of `ashworld`, `badlands`, `desert`, `ice`, `jungle`, `platform`, `twilight` or `installation`, which corresponds to Starcraft terrain tilesets.
  
  It was trained on 64x64 terrain images from 1,808 melee maps including original Blizzard maps and those downloaded from Battle.net, scmscx.com and broodwarmaps.net.
  
  Run it on Huggingface Spaces:
  
  https://huggingface.co/spaces/wdcqc/wfd
  
  Or use this notebook on Colab:
  
  https://colab.research.google.com/github/wdcqc/WaveFunctionDiffusion/blob/remaster/colab/WaveFunctionDiffusion_Demo.ipynb
  
  In addition to Dreambooth, a custom VAE model (`AutoencoderTile`) for each tileset is trained to decode the latents to tileset probabilities ("waves") and generate as Starcraft maps.
  
  A WFC Guidance, inspired by the Wave Function Collapse algorithm, is also added to the pipeline. For more information about guidance please see this page: [Fine-Tuning, Guidance and Conditioning](https://github.com/huggingface/diffusion-models-class/tree/main/unit2)
  
  This model was created as part of the DreamBooth Hackathon. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
  
  ## Description
  
  
  This is a Stable Diffusion model fine-tuned on starcraft terrain images for the landscape theme.
  
  GitHub: https://github.com/wdcqc/WaveFunctionDiffusion
  
  
  ## Usage
  
  First clone the git repository:
  
  ```bash
  git clone https://github.com/wdcqc/WaveFunctionDiffusion.git
  ```
  
  Then create a Jupyter notebook under the repository folder:
  
  ```python
  # Load pipeline
  from wfd.wf_diffusers import WaveFunctionDiffusionPipeline
  from wfd.wf_diffusers import AutoencoderTile
  from wfd.scmap import find_tile_data, get_tileset_keyword
  
  # Tilesets: ashworld, badlands, desert, ice, jungle, platform, twilight, install
  tileset = "ice"
  
  # The data files are located in wfd/scmap/tile_data/wfc
  wfc_data_path = find_tile_data("wfc/{}_64x64.npz".format(tileset))
  
  # Use CUDA (otherwise it will take 15 minutes)
  device = "cuda"
  
  tilenet = AutoencoderTile.from_pretrained(
      "wdcqc/starcraft-terrain-64x64",
      subfolder="tile_vae_{}".format(tileset)
  ).to(device)
  pipeline = WaveFunctionDiffusionPipeline.from_pretrained(
      "wdcqc/starcraft-terrain-64x64",
      tile_vae = tilenet,
      wfc_data_path = wfc_data_path
  )
  pipeline.to(device)
  
  # Double speed (only works for CUDA)
  pipeline.set_precision("half")
  
  # Generate pipeline output
  # need to include the dreambooth keywords "isometric starcraft {tileset_keyword} terrain"
  tileset_keyword = get_tileset_keyword(tileset)
  pipeline_output = pipeline(
      "lost temple, isometric starcraft {} terrain".format(tileset_keyword),
      num_inference_steps = 50,
      guidance_scale = 3.5,
      wfc_guidance_start_step = 20,
      wfc_guidance_strength = 5,
      wfc_guidance_final_steps = 20,
      wfc_guidance_final_strength = 10,
  )
  image = pipeline_output.images[0]
  
  # Display raw generated image
  from IPython.display import display
  display(image)
  
  # Display generated image as tiles
  wave = pipeline_output.waves[0]
  tile_result = wave.argmax(axis=2)
  
  from wfd.scmap import demo_map_image
  display(demo_map_image(tile_result, wfc_data_path = wfc_data_path))
  
  # Generate map file
  from wfd.scmap import tiles_to_scx
  import random, time
  
  tiles_to_scx(
      tile_result,
      "outputs/{}_{}_{:04d}.scx".format(tileset, time.strftime("%Y%m%d_%H%M%S"), random.randint(0, 1e4)),
      wfc_data_path = wfc_data_path
  )
  
  # Open the generated map file in `outputs` folder with Scmdraft 2
  ```