Voronoi3

README.md

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+# Genetic Stable Diffusion.
+
+This fork of Stable Diffusion uses genetic stuff and a graphical user interface.
+It also works in many languages (tested: French and German, should be ok for many more).
+It generates many images.
+It should work directly on Mac M1.
+It should be easy to adap to a machine with GPU.
+Without GPU it will be more complicated.
+Ping us at the Nevergrad user group if you need help, I'll do my best.
+
+[**Nevergrad Users**](https://www.facebook.com/groups/nevergradusers/)<br/>
+[**Doc**](https://docs.google.com/document/d/12Bz095QNuo_ojxSlGENXKL5Law75IUx5_Nm5L5guKgo/edit?usp=sharing)<br/>
+
+
+
+## Get a HuggingFace token! This is a fork of HuggingFace's stablediffusion.
+
+Just click here and copy-paste your token:
+[**Hugging face tokens**](https://huggingface.co/login?next=%2Fsettings%2Ftokens)<br/>
+
+## Install StableDiffusion as usual, plus a few more stuff.  Basically:
+
+You need homebrew.
+On a Mac, you need to do special stuff for the MPS: we recommend 
+[**This page**](https://towardsdatascience.com/gpu-acceleration-comes-to-pytorch-on-m1-macs-195c399efcc1)<br/>
+
+You need to open a terminal. Then:
+```
+mkdir stablediffusion
+cd stablediffusion
+git clone git@github.com:teytaud/genetic-stable-diffusion.git .
+brew install wget
+conda env create -f environment.yaml
+conda activate ldm    # you can change that name in the environment.yaml file...
+conda install pytorch torchvision -c pytorch
+pip install transformers diffusers invisible-watermark
+pip install -e .
+pip install pygame
+pip install joblib
+pip install pyttsx3
+pip install einops
+pip install webbrowser
+pip install pyfiglet
+pip install nevergrad
+pip install langdetect
+pip install deep-translator
+pip install git+https://github.com/sberbank-ai/Real-ESRGAN.git
+wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P weights
+wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth
+```
+
+## Then run << python geneticsd.py >>.
+You should be asked for a prompt (just <<enter>> if you like the proposed hardcoded prompt), and then a window should be opened.
+
+## Send feedback to [**Nevergrad Users**](https://www.facebook.com/groups/nevergradusers/)<br/>
+
 # Stable Diffusion
 *Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*
 

archimulti_minisd.sh

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+echo deprecated.

diffusers/__init__.py

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+from .utils import (
+    is_inflect_available,
+    is_onnx_available,
+    is_scipy_available,
+    is_transformers_available,
+    is_unidecode_available,
+)
+
+
+__version__ = "0.3.0"
+
+from .configuration_utils import ConfigMixin
+from .modeling_utils import ModelMixin
+from .models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel
+from .onnx_utils import OnnxRuntimeModel
+from .optimization import (
+    get_constant_schedule,
+    get_constant_schedule_with_warmup,
+    get_cosine_schedule_with_warmup,
+    get_cosine_with_hard_restarts_schedule_with_warmup,
+    get_linear_schedule_with_warmup,
+    get_polynomial_decay_schedule_with_warmup,
+    get_scheduler,
+)
+from .pipeline_utils import DiffusionPipeline
+from .pipelines import DDIMPipeline, DDPMPipeline, KarrasVePipeline, LDMPipeline, PNDMPipeline, ScoreSdeVePipeline
+from .schedulers import (
+    DDIMScheduler,
+    DDPMScheduler,
+    KarrasVeScheduler,
+    PNDMScheduler,
+    SchedulerMixin,
+    ScoreSdeVeScheduler,
+)
+from .utils import logging
+
+
+if is_scipy_available():
+    from .schedulers import LMSDiscreteScheduler
+else:
+    from .utils.dummy_scipy_objects import *  # noqa F403
+
+from .training_utils import EMAModel
+
+
+if is_transformers_available():
+    from .pipelines import (
+        LDMTextToImagePipeline,
+        StableDiffusionImg2ImgPipeline,
+        StableDiffusionInpaintPipeline,
+        StableDiffusionPipeline,
+    )
+else:
+    from .utils.dummy_transformers_objects import *  # noqa F403
+
+
+if is_transformers_available() and is_onnx_available():
+    from .pipelines import StableDiffusionOnnxPipeline
+else:
+    from .utils.dummy_transformers_and_onnx_objects import *  # noqa F403

diffusers/commands/__init__.py

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+# Copyright 2022 The HuggingFace Team. 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.
+
+from abc import ABC, abstractmethod
+from argparse import ArgumentParser
+
+
+class BaseDiffusersCLICommand(ABC):
+    @staticmethod
+    @abstractmethod
+    def register_subcommand(parser: ArgumentParser):
+        raise NotImplementedError()
+
+    @abstractmethod
+    def run(self):
+        raise NotImplementedError()

diffusers/commands/diffusers_cli.py

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+#!/usr/bin/env python
+# Copyright 2022 The HuggingFace Team. 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.
+
+from argparse import ArgumentParser
+
+from .env import EnvironmentCommand
+
+
+def main():
+    parser = ArgumentParser("Diffusers CLI tool", usage="diffusers-cli <command> [<args>]")
+    commands_parser = parser.add_subparsers(help="diffusers-cli command helpers")
+
+    # Register commands
+    EnvironmentCommand.register_subcommand(commands_parser)
+
+    # Let's go
+    args = parser.parse_args()
+
+    if not hasattr(args, "func"):
+        parser.print_help()
+        exit(1)
+
+    # Run
+    service = args.func(args)
+    service.run()
+
+
+if __name__ == "__main__":
+    main()

diffusers/commands/env.py

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+# Copyright 2022 The HuggingFace Team. 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 platform
+from argparse import ArgumentParser
+
+import huggingface_hub
+
+from .. import __version__ as version
+from ..utils import is_torch_available, is_transformers_available
+from . import BaseDiffusersCLICommand
+
+
+def info_command_factory(_):
+    return EnvironmentCommand()
+
+
+class EnvironmentCommand(BaseDiffusersCLICommand):
+    @staticmethod
+    def register_subcommand(parser: ArgumentParser):
+        download_parser = parser.add_parser("env")
+        download_parser.set_defaults(func=info_command_factory)
+
+    def run(self):
+        hub_version = huggingface_hub.__version__
+
+        pt_version = "not installed"
+        pt_cuda_available = "NA"
+        if is_torch_available():
+            import torch
+
+            pt_version = torch.__version__
+            pt_cuda_available = torch.cuda.is_available()
+
+        transformers_version = "not installed"
+        if is_transformers_available:
+            import transformers
+
+            transformers_version = transformers.__version__
+
+        info = {
+            "`diffusers` version": version,
+            "Platform": platform.platform(),
+            "Python version": platform.python_version(),
+            "PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",
+            "Huggingface_hub version": hub_version,
+            "Transformers version": transformers_version,
+            "Using GPU in script?": "<fill in>",
+            "Using distributed or parallel set-up in script?": "<fill in>",
+        }
+
+        print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")
+        print(self.format_dict(info))
+
+        return info
+
+    @staticmethod
+    def format_dict(d):
+        return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n"