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from typing import Dict, List, Any |
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import base64 |
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import logging |
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import tensorflow as tf |
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from tensorflow import keras |
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from keras_cv.models.generative.stable_diffusion.text_encoder import TextEncoder |
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from keras_cv.models.generative.stable_diffusion.clip_tokenizer import SimpleTokenizer |
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from keras_cv.models.generative.stable_diffusion.constants import _UNCONDITIONAL_TOKENS |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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self.MAX_PROMPT_LENGTH = 77 |
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self.tokenizer = SimpleTokenizer() |
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self.text_encoder = TextEncoder(self.MAX_PROMPT_LENGTH) |
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text_encoder_weights_fpath = keras.utils.get_file( |
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origin="https://huggingface.co/fchollet/stable-diffusion/resolve/main/kcv_encoder.h5", |
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file_hash="4789e63e07c0e54d6a34a29b45ce81ece27060c499a709d556c7755b42bb0dc4", |
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) |
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self.text_encoder.load_weights(text_encoder_weights_fpath) |
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self.pos_ids = tf.convert_to_tensor([list(range(self.MAX_PROMPT_LENGTH))], dtype=tf.int32) |
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def _get_unconditional_context(self): |
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unconditional_tokens = tf.convert_to_tensor( |
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[_UNCONDITIONAL_TOKENS], dtype=tf.int32 |
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) |
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unconditional_context = self.text_encoder.predict_on_batch( |
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[unconditional_tokens, self.pos_ids] |
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) |
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return unconditional_context |
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def encode_text(self, prompt): |
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inputs = self.tokenizer.encode(prompt) |
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if len(inputs) > self.MAX_PROMPT_LENGTH: |
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raise ValueError( |
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f"Prompt is too long (should be <= {self.MAX_PROMPT_LENGTH} tokens)" |
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) |
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phrase = inputs + [49407] * (self.MAX_PROMPT_LENGTH - len(inputs)) |
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phrase = tf.convert_to_tensor([phrase], dtype=tf.int32) |
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context = self.text_encoder.predict_on_batch([phrase, self.pos_ids]) |
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return context |
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def get_contexts(self, encoded_text, batch_size): |
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encoded_text = tf.squeeze(encoded_text) |
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if encoded_text.shape.rank == 2: |
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encoded_text = tf.repeat( |
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tf.expand_dims(encoded_text, axis=0), batch_size, axis=0 |
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) |
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context = encoded_text |
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unconditional_context = tf.repeat( |
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self._get_unconditional_context(), batch_size, axis=0 |
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) |
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return context, unconditional_context |
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def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]: |
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prompt = data.pop("inputs", data) |
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batch_size = data.pop("batch_size", 1) |
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encoded_text = self.encode_text(prompt) |
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context, unconditional_context = self.get_contexts(encoded_text, batch_size) |
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context_b64 = base64.b64encode(context.numpy().tobytes()) |
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context_b64str = context_b64.decode() |
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unconditional_context_b64 = base64.b64encode(unconditional_context.numpy().tobytes()) |
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unconditional_context_b64str = unconditional_context_b64.decode() |
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return {"context_b64str": context_b64str, "unconditional_context_b64str": unconditional_context_b64str} |
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