Implement sdxl-turbo

README.md

@@ -137,6 +137,7 @@ The collection of pre-trained, state-of-the-art AI models.
 | [<img src="diffusion/daclip-sde/output.png" width=128px>](diffusion/daclip-sde/) | [DA-CLIP](/diffusion/daclip-sde/) | [DA-CLIP](https://github.com/Algolzw/daclip-uir) | Pytorch | 1.2.16 and later |
 | [<img src="diffusion/riffusion/output.png" width=128px>](diffusion/riffusion/) | [riffusion](/diffusion/riffusion/) | [Riffusion](https://github.com/riffusion/riffusion) | Pytorch | 1.2.16 and later |
 | [<img src="diffusion/marigold/output_colorize.png" width=128px>](diffusion/marigold/) | [marigold](/diffusion/marigold/) | [Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation](https://github.com/prs-eth/Marigold) | Pytorch | 1.2.16 and later |
+| [<img src="diffusion/sdxl-turbo/output.png" width=128px>](diffusion/sdxl-turbo/) | [sdxl-turbo](/diffusion/sdxl-turbo/) | [Hugging Face - SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo) | Pytorch | 1.2.16 and later |
 
 ## Face detection
 

diffusion/sdxl-turbo/LICENSE.TXT

@@ -0,0 +1,60 @@
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diffusion/sdxl-turbo/README.md

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+# SDXL-Turbo
+
+## Text-to-image
+
+### Input
+
+Prompt:
+```
+little cute gremlin wearing a jacket, cinematic, vivid colors, intricate masterpiece, golden ratio, highly detailed
+```
+
+### Output
+
+![Output](output.png)
+
+## Image-to-image
+
+### Input
+
+Image:
+
+<img src="cat.png" height="512px"/>
+
+(Image from https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png)
+
+Prompt:
+```
+cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k
+```
+
+### Output
+
+![Output](output_img2img.png)
+
+
+## Requirements
+This model requires additional module.
+
+```
+pip3 install transformers
+```
+
+## Usage
+Automatically downloads the onnx and prototxt files on the first run.
+It is necessary to be connected to the Internet while downloading.
+
+For the sample image, (Text-to-image)
+```bash
+$ python3 sdxl-turbo.py
+```
+
+If you want to specify the input prompt, put the prompt after the `--input` option.  
+You can use `--savepath` option to change the name of the output file to save.
+```bash
+$ python3 sdxl-turbo.py --input PROMPT --savepath SAVE_WAV_PATH
+```
+
+For Image-to-image, specify the input image file after the `--init_image` option.
+```bash
+$ python3 sdxl-turbo.py --input "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k" --init_image cat.png 
+```
+
+
+## Reference
+
+- [Hugging Face - SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo)
+
+## Framework
+
+Pytorch
+
+## Model Format
+
+ONNX opset=14
+
+## Netron
+
+[unet.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/sdxl-turbo/unet.onnx.prototxt)  
+[text_encoder.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/sdxl-turbo/text_encoder.onnx.prototxt)  
+[text_encoder_2.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/sdxl-turbo/text_encoder_2.onnx.prototxt)  
+[vae_encoder.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/sdxl-turbo/vae_encoder.onnx.prototxt)  
+[vae_decoder.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/sdxl-turbo/vae_decoder.onnx.prototxt)  

diffusion/sdxl-turbo/df/__init__.py

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+from .schedulers import EulerAncestralDiscreteScheduler
+from .pipelines import StableDiffusionXL, StableDiffusionXLImg2Img

diffusion/sdxl-turbo/df/configuration_utils.py

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+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+# Copyright (c) 2022, NVIDIA CORPORATION.  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.
+
+""" ConfigMixin base class and utilities."""
+
+import functools
+import inspect
+
+from collections import OrderedDict
+from typing import Any, Dict
+
+
+class FrozenDict(OrderedDict):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        for key, value in self.items():
+            setattr(self, key, value)
+
+        self.__frozen = True
+
+
+class ConfigMixin:
+    def register_to_config(self, **kwargs):
+        if not hasattr(self, "_internal_dict"):
+            internal_dict = kwargs
+        else:
+            internal_dict = {**self._internal_dict, **kwargs}
+
+        self._internal_dict = FrozenDict(internal_dict)
+
+    @classmethod
+    def from_config(cls, config_dict, **kwargs):
+        init_dict, _, hidden_dict = cls.extract_init_dict(config_dict, **kwargs)
+
+        # Return model and optionally state and/or unused_kwargs
+        model = cls(**init_dict)
+
+        # make sure to also save config parameters that might be used for compatible classes
+        model.register_to_config(**hidden_dict)
+
+        return model
+
+    @staticmethod
+    def _get_init_keys(cls):
+        return set(dict(inspect.signature(cls.__init__).parameters).keys())
+
+    @classmethod
+    def extract_init_dict(cls, config_dict, **kwargs):
+        # 0. Copy origin config dict
+        original_dict = dict(config_dict.items())
+
+        # 1. Retrieve expected config attributes from __init__ signature
+        expected_keys = cls._get_init_keys(cls)
+        expected_keys.remove("self")
+
+        # remove private attributes
+        config_dict = {k: v for k, v in config_dict.items() if not k.startswith("_")}
+
+        # 3. Create keyword arguments that will be passed to __init__ from expected keyword arguments
+        init_dict = {}
+        for key in expected_keys:
+            # if config param is passed to kwarg and is present in config dict
+            # it should overwrite existing config dict key
+            if key in kwargs and key in config_dict:
+                config_dict[key] = kwargs.pop(key)
+
+            if key in kwargs:
+                # overwrite key
+                init_dict[key] = kwargs.pop(key)
+            elif key in config_dict:
+                # use value from config dict
+                init_dict[key] = config_dict.pop(key)
+
+        # 6. Define unused keyword arguments
+        unused_kwargs = {**config_dict, **kwargs}
+
+        # 7. Define "hidden" config parameters that were saved for compatible classes
+        hidden_config_dict = {k: v for k, v in original_dict.items() if k not in init_dict}
+
+        return init_dict, unused_kwargs, hidden_config_dict
+
+    @property
+    def config(self) -> Dict[str, Any]:
+        """
+        Returns the config of the class as a frozen dictionary
+
+        Returns:
+            `Dict[str, Any]`: Config of the class.
+        """
+        return self._internal_dict
+
+
+def register_to_config(init):
+    r"""
+    Decorator to apply on the init of classes inheriting from [`ConfigMixin`] so that all the arguments are
+    automatically sent to `self.register_for_config`. To ignore a specific argument accepted by the init but that
+    shouldn't be registered in the config, use the `ignore_for_config` class variable
+
+    Warning: Once decorated, all private arguments (beginning with an underscore) are trashed and not sent to the init!
+    """
+
+    @functools.wraps(init)
+    def inner_init(self, *args, **kwargs):
+        # Ignore private kwargs in the init.
+        init_kwargs = {k: v for k, v in kwargs.items() if not k.startswith("_")}
+        config_init_kwargs = {k: v for k, v in kwargs.items() if k.startswith("_")}
+
+        # Get positional arguments aligned with kwargs
+        new_kwargs = {}
+        signature = inspect.signature(init)
+        parameters = {
+            name: p.default for i, (name, p) in enumerate(signature.parameters.items()) if i > 0
+        }
+        for arg, name in zip(args, parameters.keys()):
+            new_kwargs[name] = arg
+
+        # Then add all kwargs
+        new_kwargs.update(
+            {
+                k: init_kwargs.get(k, default)
+                for k, default in parameters.items()
+                if k not in new_kwargs
+            }
+        )
+
+        new_kwargs = {**config_init_kwargs, **new_kwargs}
+        getattr(self, "register_to_config")(**new_kwargs)
+        init(self, *args, **init_kwargs)
+
+    return inner_init

diffusion/sdxl-turbo/df/pipelines/__init__.py

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+from .stable_diffusion_xl import StableDiffusionXL
+from .stable_diffusion_xl_img2img import StableDiffusionXLImg2Img