| ---
|
| library_name: pytorch
|
| tags:
|
| - autoregressive
|
| - image-generation
|
| - aerial
|
| - controllable-generation
|
| license: apache-2.0
|
| pipeline_tag: image-to-image
|
| ---
|
|
|
| # CondRef-AR: Condition-as-a-Reference Randomized Autoregressive Modelling for Controllable Aerial Image Generation
|
| [](https://jin-pu.github.io/CondRef-AR)
|
| [](https://jin-pu.github.io/CondRef-AR)
|
| [](https://huggingface.co/PuTorch/CondRef-AR)
|
| [](https://github.com/Jin-Pu/CondRef-AR)
|
|
|
| This repository contains the code and pretrained models for **CondRef-AR**, a controllable aerial image generation model using condition-as-a-reference randomized autoregressive modeling. The model generates high-quality aerial images based on input conditions such as sketches or segmentation maps.
|
|
|
| 
|
|
|
|
|
| ## Quickstart
|
|
|
| ```python
|
| import json, torch
|
| from CondRefAR.pipeline import CondRefARPipeline
|
| from transformers import AutoTokenizer, T5EncoderModel
|
|
|
| device = "cuda" if torch.cuda.is_available() else "cpu"
|
| dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
|
|
|
| gpt_cfg = json.load(open("configs/gpt_config.json"))
|
| vq_cfg = json.load(open("configs/vq_config.json"))
|
| pipe = CondRefARPipeline.from_pretrained(".", gpt_cfg, vq_cfg, device=device, torch_dtype=dtype)
|
|
|
| tok = AutoTokenizer.from_pretrained("google/flan-t5-xl")
|
| enc = T5EncoderModel.from_pretrained("google/flan-t5-xl", torch_dtype=dtype).to(device).eval()
|
|
|
| prompt = "Aaerial view of a forested area with a river running through it. On the right side of the image, there is a small town or village with a red-roofed building."
|
| control = "assets/examples/example2.jpg"
|
|
|
| from PIL import Image, ImageOps
|
| control_img = Image.open(control).convert("RGB")
|
|
|
| inputs = tok([prompt], return_tensors="pt", padding="max_length", truncation=True, max_length=120)
|
| with torch.no_grad():
|
| emb = enc(input_ids=inputs["input_ids"].to(device), attention_mask=inputs["attention_mask"].to(device)).last_hidden_state
|
|
|
| imgs = pipe(emb, control_img, cfg_scale=4, temperature=1.0, top_k=2000, top_p=1.0)
|
| imgs[0].save("sample.png")
|
| ```
|
|
|
| ## Sample Results
|
| By varying the input conditions and prompts, CondRef-AR can generate diverse aerial images:
|
| 
|
|
|
| ConRef-AR can generate continuous, plausible, and high-resolution sequences of land-use change images based on a series of temporal semantic condition graphs. As shown in the figure below, the model successfully simulates the entire process—from a pristine forest gradually transforming into a modern residential urban area:
|
|
|
| 
|
| <div align="center">
|
|
|
| | Control image | Aerial image |
|
| |---|---|
|
| | <img src="assets/control_img.gif" alt="control animation" width="100%"/> | <img src="assets/aerial_img.gif" alt="aerial animation" width="100%"/> |
|
|
|
| </div>
|
|
|
| ## Files
|
| - `weights/sketch-gpt-xl.safetensors`, `weights/vq-16.safetensors`: pretrained weight
|
| - `configs/*.json`: model hyperparameters.
|
| - `CondRefAR/*`: inference code and pipeline.
|
| - `assets/example`: example images.
|
| - `app.py`: Gradio demo.
|
|
|
| ## Notes
|
| - Requires a GPU with bfloat16 support for best speed; CPU works but slow.
|
| - CFG params: `cfg_scale`, `temperature`, `top_k`, `top_p` control quality vs diversity.
|
| - If you have any questions, please open an issue, or contact putorch@outlook.com.
|
|
|
| ## License
|
| Apache-2.0 |
