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import logging |
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import pathlib |
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import gradio as gr |
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import pandas as pd |
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from gt4sd.algorithms.generation.diffusion import ( |
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DiffusersGenerationAlgorithm, |
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DDPMGenerator, |
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DDIMGenerator, |
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ScoreSdeGenerator, |
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LDMTextToImageGenerator, |
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LDMGenerator, |
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StableDiffusionGenerator, |
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) |
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from gt4sd.algorithms.registry import ApplicationsRegistry |
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logger = logging.getLogger(__name__) |
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logger.addHandler(logging.NullHandler()) |
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def run_inference(model_type: str, prompt: str): |
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config = eval(f"{model_type}(prompt={prompt})") |
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if config.modality != "token2image" and prompt != "": |
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raise ValueError( |
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f"{model_type} is an unconditional generative model, please remove prompt (not={prompt})" |
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) |
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model = DiffusersGenerationAlgorithm(config) |
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image = list(model.sample(1))[0] |
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return image |
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if __name__ == "__main__": |
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all_algos = ApplicationsRegistry.list_available() |
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algos = [ |
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x["algorithm_application"] |
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for x in list(filter(lambda x: "Diff" in x["algorithm_name"], all_algos)) |
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] |
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algos = [a for a in algos if not "GeoDiff" in a] |
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metadata_root = pathlib.Path(__file__).parent.joinpath("model_cards") |
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examples = pd.read_csv(metadata_root.joinpath("examples.csv"), header=None).fillna( |
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"" |
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) |
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with open(metadata_root.joinpath("article.md"), "r") as f: |
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article = f.read() |
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with open(metadata_root.joinpath("description.md"), "r") as f: |
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description = f.read() |
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demo = gr.Interface( |
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fn=run_inference, |
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title="Diffusion-based image generators", |
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inputs=[ |
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gr.Dropdown( |
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algos, label="Diffusion model", value="StableDiffusionGenerator" |
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), |
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gr.Textbox(label="Text prompt", placeholder="A blue tree", lines=1), |
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], |
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outputs=gr.outputs.Image(type="pil"), |
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article=article, |
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description=description, |
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examples=examples.values.tolist(), |
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) |
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demo.launch(debug=True, show_error=True) |
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