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README.md

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+---
+license: creativeml-openrail-m
+tags:
+- pytorch
+- diffusers
+- stable-diffusion
+- text-to-image
+- diffusion-models-class
+- dreambooth-hackathon
+- landscape
+widget:
+- text: isometric scspace terrain, corgi
+---
+
+# DreamBooth model for Starcraft:Remastered terrain
+
+This is a Stable Diffusion model fine-tuned on Starcraft terrain images on the Space Platform tileset with DreamBooth. It can be used by modifying the `instance_prompt`: **isometric scspace terrain**
+
+It was trained on 32x32 terrain images from 265 melee maps including original Blizzard maps and those downloaded from scmscx.com and broodwarmaps.com.
+
+To run the demo with the ability to generate map files directly/with more coherence, use this notebook on Colab:
+
+<ADD_NOTEBOOK_LINK_HERE>
+
+In addition to Dreambooth, a custom VAE model (`AutoencoderTile`) is trained to encode and decode the latents to/from tileset probabilities ("waves") and then generated 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.
+
+
+## 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
+
+wfc_data_path = "tile_data/wfc/platform_32x32.npz"
+
+# Use CUDA (otherwise it will take 15 minutes)
+device = "cuda"
+
+tilenet = AutoencoderTile.from_pretrained(
+    "wdcqc/starcraft-platform-terrain-32x32",
+    subfolder="tile_vae"
+).to(device)
+pipeline = WaveFunctionDiffusionPipeline.from_pretrained(
+    "wdcqc/starcraft-platform-terrain-32x32",
+    tile_vae = tilenet,
+    wfc_data_path = wfc_data_path
+)
+pipeline.to(device)
+
+# Generate pipeline output
+# need to include the dreambooth keyword "isometric scspace terrain"
+pipeline_output = pipeline(
+    "isometric scspace terrain, corgi",
+    num_inference_steps = 50,
+    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/generated_{}_{:04d}.scx".format(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
+```