Add Text2Image into Taskflow #2988
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| # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License" | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| import paddle | ||
| from PIL import Image | ||
| from ..transformers import AutoModelForImageGeneration, AutoTokenizer | ||
| from .task import Task | ||
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| usage = r""" | ||
| from paddlenlp import Taskflow | ||
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| text2imagegen = Taskflow("text2image_generation") | ||
| images = text2imagegen("风阁水帘今在眼，且来先看早梅红") | ||
| images[0].save("figure.png") | ||
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| """ | ||
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| tokenizer_kwargs = { | ||
| "dallebart": { | ||
| "max_length": 64, | ||
| "return_token_typd_ids": False, | ||
| "return_attention_mask": True | ||
| }, | ||
| "gpt": { | ||
| "max_length": 32, | ||
| "return_token_typd_ids": False, | ||
| "return_attention_mask": False | ||
| }, | ||
| } | ||
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| class Text2ImageGenerationTask(Task): | ||
| """ | ||
| The text2image generation model to generate the image. | ||
| Args: | ||
| task(string): The name of task. | ||
| model(string): The model name in the task. | ||
| kwargs (dict, optional): Additional keyword arguments passed along to the specific task. | ||
| """ | ||
|
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| def __init__(self, task, model="pai-painter-painting-base-zh", **kwargs): | ||
| super().__init__(task=task, model=model, **kwargs) | ||
| self._batch_size = kwargs.get("batch_size", 1) | ||
| self._temperature = kwargs.get("temperature", 1.) | ||
| self._top_k = kwargs.get("top_k", 32) | ||
| self._top_p = kwargs.get("top_p", 1.) | ||
| self._condition_scale = kwargs.get("condition_scale", 10.) | ||
| self._num_return_images = kwargs.get("num_return_images", 4) | ||
| self._use_faster = kwargs.get("use_faster", False) | ||
| self._use_fp16_decoding = kwargs.get("use_fp16_decoding", False) | ||
| self._construct_tokenizer(model) | ||
| self._construct_model(model) | ||
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| def _construct_model(self, model): | ||
| """ | ||
| Construct the inference model for the predictor. | ||
| """ | ||
| self._model = AutoModelForImageGeneration.from_pretrained(model) | ||
| self._model.eval() | ||
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| def _construct_tokenizer(self, model): | ||
| """ | ||
| Construct the tokenizer for the predictor. | ||
| """ | ||
| self._tokenizer = AutoTokenizer.from_pretrained(model) | ||
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| def _batchify(self, data, batch_size): | ||
| """ | ||
| Generate input batches. | ||
| """ | ||
|
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| def _parse_batch(batch_examples): | ||
| tokenizerd_inputs = self._tokenizer( | ||
| batch_examples, | ||
| return_tensors="pd", | ||
| padding="max_length", | ||
| truncation=True, | ||
| **tokenizer_kwargs[self._model.base_model_prefix]) | ||
| if self._model.base_model_prefix == "dallebart": | ||
| tokenizerd_inputs["condition_scale"] = self._condition_scale | ||
| return tokenizerd_inputs | ||
|
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| # Seperates data into some batches. | ||
| one_batch = [] | ||
| for example in data: | ||
| one_batch.append(example) | ||
| if len(one_batch) == batch_size: | ||
| yield _parse_batch(one_batch) | ||
| one_batch = [] | ||
| if one_batch: | ||
| yield _parse_batch(one_batch) | ||
|
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| def _preprocess(self, inputs): | ||
| """ | ||
| Transform the raw text to the model inputs, two steps involved: | ||
| 1) Transform the raw text to token ids. | ||
| 2) Generate the other model inputs from the raw text and token ids. | ||
| """ | ||
| inputs = self._check_input_text(inputs) | ||
| batches = self._batchify(inputs, self._batch_size) | ||
| outputs = {'batches': batches, 'text': inputs} | ||
| return outputs | ||
|
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| def _run_model(self, inputs): | ||
| """ | ||
| Run the task model from the outputs of the `_preprocess` function. | ||
| """ | ||
| all_images = [] | ||
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| for batch_inputs in inputs["batches"]: | ||
| images = self._model.generate( | ||
| **batch_inputs, | ||
| temperature=self._temperature, | ||
| top_k=self._top_k, | ||
| top_p=self._top_p, | ||
| num_return_sequences=self._num_return_images, | ||
| use_faster=self._use_faster, | ||
| use_fp16_decoding=self._use_fp16_decoding).cpu().numpy() | ||
| if self._model.base_model_prefix == "dallebart": | ||
| images = (images.clip(0, 1) * 255).astype("uint8") | ||
| elif self._model.base_model_prefix == "gpt": | ||
| images = ((images + 1.0) * 127.5).clip(0, 255).astype("uint8") | ||
|
There was a problem hiding this comment. 这些后处理如果是和模型绑定的话后面也可以看看将这些方法放在transformers模型代码中提供出来，这里进行统一调用，方便更多模型扩展 There was a problem hiding this comment. done, 现已添加到model的generate内部。 |
||
| for image in images: | ||
| all_images.append(image) | ||
| inputs['images'] = all_images | ||
| return inputs | ||
|
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| def _postprocess(self, inputs): | ||
| """ | ||
| The model output is images, this function will convert the model output to PIL Image. | ||
| """ | ||
| batch_out = [] | ||
| generated_images = inputs['images'] | ||
| for generated_image in generated_images: | ||
| generated_image = generated_image.transpose([1, 0, 2, 3]).reshape( | ||
| generated_image.shape[-3], | ||
| self._num_return_images * generated_image.shape[-2], | ||
| generated_image.shape[-1]) | ||
| batch_out.append(Image.fromarray(generated_image)) | ||
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| return batch_out | ||
|
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| def _construct_input_spec(self): | ||
| """ | ||
| Construct the input spec for the convert dygraph model to static model. | ||
| """ | ||
| self._input_spec = [ | ||
| paddle.static.InputSpec(shape=[None, None], | ||
| dtype="int64", | ||
| name='input_ids'), | ||
| ] | ||
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这里也尽量隐去 artist 吧，这个模型名确实不见得有认可度，先用 pai这些模型权重来体现吧
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use_faster：是否使用faster_generation，默认为False，目前支持faster_generation的模型有["pai-painter-painting-base-zh", "pai-painter-scenery-base-zh", "pai-painter-commercial-base-zh"]。