54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
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import tempfile
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from PIL import Image
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from cog import BasePredictor, Path, Input
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from min_dalle.generate_image import load_dalle_bart_metadata, tokenize_text
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from min_dalle.load_params import load_dalle_bart_flax_params
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from min_dalle.min_dalle_torch import generate_image_tokens_torch, detokenize_torch
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class Predictor(BasePredictor):
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def setup(self):
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self.model_path = {
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"mini": "pretrained/dalle_bart_mini",
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"mega": "pretrained/dalle_bart_mega",
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}
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self.configs = {
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k: load_dalle_bart_metadata(self.model_path[k])
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for k in self.model_path.keys()
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}
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def predict(
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self,
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text: str = Input(
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description="Text for generating images.",
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),
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model: str = Input(
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choices=["mini", "mega"],
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description="Choose mini or mega model.",
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),
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seed: int = Input(
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description="Specify the seed.",
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),
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) -> Path:
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config, vocab, merges = self.configs[model]
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text_tokens = tokenize_text(text, config, vocab, merges)
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params_dalle_bart = load_dalle_bart_flax_params(self.model_path[model])
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image_token_count = config["image_length"]
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image_tokens = generate_image_tokens_torch(
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text_tokens=text_tokens,
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seed=seed,
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config=config,
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params=params_dalle_bart,
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image_token_count=image_token_count,
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)
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image = detokenize_torch(image_tokens, is_torch=True)
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image = Image.fromarray(image)
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out_path = Path(tempfile.mkdtemp()) / "output.png"
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image.save(str(out_path))
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return out_path
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