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.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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.gitignore ADDED
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+ __pycache__/
LICENSE ADDED
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+ MIT License
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+
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+ Copyright (c) 2022 Generative Toolkit 4 Scientific Discovery
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+
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+ Permission is hereby granted, free of charge, to any person obtaining a copy
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+ of this software and associated documentation files (the "Software"), to deal
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+ in the Software without restriction, including without limitation the rights
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+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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+ copies of the Software, and to permit persons to whom the Software is
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+ furnished to do so, subject to the following conditions:
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+
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+ The above copyright notice and this permission notice shall be included in all
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+ copies or substantial portions of the Software.
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+
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+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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+ SOFTWARE.
README.md ADDED
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+ ---
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+ title: GT4SD - Diffusers (image)
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+ emoji: 💡
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+ colorFrom: green
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+ colorTo: blue
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+ sdk: gradio
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+ sdk_version: 3.9.1
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+ app_file: app.py
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+ pinned: false
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+ python_version: 3.8.13
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+ pypi_version: 20.2.4
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+ duplicated_from: jannisborn/gt4sd-diffusers
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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+ import logging
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+ import pathlib
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+ import gradio as gr
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+ import pandas as pd
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+ from gt4sd.algorithms.generation.diffusion import (
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+ DiffusersGenerationAlgorithm,
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+ DDPMGenerator,
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+ DDIMGenerator,
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+ ScoreSdeGenerator,
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+ LDMTextToImageGenerator,
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+ LDMGenerator,
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+ StableDiffusionGenerator,
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+ )
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+ from gt4sd.algorithms.registry import ApplicationsRegistry
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+
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+ logger = logging.getLogger(__name__)
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+ logger.addHandler(logging.NullHandler())
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+
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+
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+ def run_inference(model_type: str, prompt: str):
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+
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+ if prompt == "":
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+ config = eval(f"{model_type}()")
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+ else:
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+ config = eval(f"{model_type}(prompt={prompt})")
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+ if config.modality != "token2image" and prompt != "":
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+ raise ValueError(
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+ f"{model_type} is an unconditional generative model, please remove prompt (not={prompt})"
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+ )
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+ model = DiffusersGenerationAlgorithm(config)
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+ image = list(model.sample(1))[0]
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+
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+ return image
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+
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+
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+ if __name__ == "__main__":
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+
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+ # Preparation (retrieve all available algorithms)
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+ all_algos = ApplicationsRegistry.list_available()
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+ algos = [
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+ x["algorithm_application"]
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+ for x in list(filter(lambda x: "Diff" in x["algorithm_name"], all_algos))
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+ ]
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+ algos = [a for a in algos if not "GeoDiff" in a]
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+
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+ # Load metadata
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+ metadata_root = pathlib.Path(__file__).parent.joinpath("model_cards")
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+
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+ examples = pd.read_csv(metadata_root.joinpath("examples.csv"), header=None).fillna(
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+ ""
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+ )
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+
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+ with open(metadata_root.joinpath("article.md"), "r") as f:
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+ article = f.read()
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+ with open(metadata_root.joinpath("description.md"), "r") as f:
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+ description = f.read()
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+
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+ demo = gr.Interface(
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+ fn=run_inference,
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+ title="Diffusion-based image generators",
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+ inputs=[
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+ gr.Dropdown(
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+ algos, label="Diffusion model", value="StableDiffusionGenerator"
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+ ),
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+ gr.Textbox(label="Text prompt", placeholder="A blue tree", lines=1),
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+ ],
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+ outputs=gr.outputs.Image(type="pil"),
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+ article=article,
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+ description=description,
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+ examples=examples.values.tolist(),
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+ )
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+ demo.launch(debug=True, show_error=True)
model_cards/article.md ADDED
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+ # Model documentation & parameters
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+
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+ **Diffusion model**: Which model version to use.
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+
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+ **Prompt**: The text prompt used, only applies to *conditional* diffusion image generators. These are `LDMTextToImageGenerator` and `StableDiffusionGenerator`. The other four models (`DDPMGenerator`, `DDPMGenerator`, `LDMGenerator` and `ScoreSdeGenerator`) are *unconditional*.
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+
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+
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+
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+ # Model card -- Image diffusion models
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+
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+ **Model Details**: Six diffusion models for image generation:
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+ - `LDMTextToImageGenerator`
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+ - `StableDiffusionGenerator`
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+ - `DDPMGenerator`
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+ - `DDPMGenerator`
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+ - `LDMGenerator`
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+ - `ScoreSdeGenerator`
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+
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+ For details, see the [Diffusers docs](https://huggingface.co/docs/diffusers/index)
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+
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+ **Developers**: Various developers of above models, wrapped by Diffusers developers into [`diffusers`](https://github.com/huggingface/diffusers)
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+
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+ **Distributors**: Diffusers code integrated into GT4SD.
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+
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+ **Model date**: 2022.
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+
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+ **Model version**: Diffusion models, checkpoints provided and distributed by [`diffusers`](https://github.com/huggingface/diffusers).
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+
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+ **Model type**: Various, see [`diffusers`](https://github.com/huggingface/diffusers) docs.
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+
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+ **Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**:
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+ N.A.
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+
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+ **Paper or other resource for more information**:
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+ N.A.
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+
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+ **License**: MIT
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+
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+ **Where to send questions or comments about the model**: Open an issue on [`diffusers`](https://github.com/huggingface/diffusers) repo.
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+
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+ **Intended Use. Use cases that were envisioned during development**: Computer vision researchers experimenting with image generative models.
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+
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+ **Primary intended uses/users**: Computer vision researchers
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+
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+ **Out-of-scope use cases**: Production-level inference, producing molecules with harmful properties.
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+
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+ **Metrics**: N.A.
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+
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+ **Datasets**: N.A.
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+
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+ **Ethical Considerations**: Unclear, please consult with original authors in case of questions.
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+
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+ **Caveats and Recommendations**: Unclear, please consult with original authors in case of questions.
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+
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+ Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)
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+
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+ ## Citation
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+ ```bib
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+ @misc{von-platen-etal-2022-diffusers,
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+ author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},
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+ title = {Diffusers: State-of-the-art diffusion models},
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+ year = {2022},
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+ publisher = {GitHub},
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+ journal = {GitHub repository},
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+ howpublished = {\url{https://github.com/huggingface/diffusers}}
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+ }
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+ ```
model_cards/description.md ADDED
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+ <img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="120" >
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+
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+ This UI provides access to various diffusion-based image generators implemented in the [`diffusers`](https://github.com/huggingface/diffusers) library, wrapped and re-distributed by GT4SD.
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+
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+ For **examples** and **documentation** of the model parameters, please see below.
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+ Moreover, we provide a **model card** ([Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)) at the bottom of this page.
model_cards/examples.csv ADDED
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+ LDMGenerator,
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+ LDMTextToImageGenerator,Generative models on the moon
requirements.txt ADDED
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+ -f https://download.pytorch.org/whl/cpu/torch_stable.html
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+ -f https://data.pyg.org/whl/torch-1.12.1+cpu.html
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+ # pip==20.2.4
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+ torch==1.12.1
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+ torch-scatter
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+ torch-spline-conv
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+ torch-sparse
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+ torch-geometric
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+ torchvision==0.13.1
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+ torchaudio==0.12.1
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+ gt4sd>=1.0.5
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+ molgx>=0.22.0a1
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+ molecule_generation
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+ nglview
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+ PyTDC==0.3.7
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+ gradio==3.12.0
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+ markdown-it-py>=2.1.0
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+ mols2grid>=0.2.0
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+ numpy==1.23.5
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+ pandas>=1.0.0
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+ terminator @ git+https://github.com/IBM/regression-transformer@gt4sd
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+ guacamol_baselines @ git+https://github.com/GT4SD/[email protected]
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+ moses @ git+https://github.com/GT4SD/[email protected]
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+ paccmann_chemistry @ git+https://github.com/PaccMann/[email protected]
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+ paccmann_generator @ git+https://github.com/PaccMann/[email protected]
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+ paccmann_gp @ git+https://github.com/PaccMann/[email protected]
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+ paccmann_omics @ git+https://github.com/PaccMann/[email protected]
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+ paccmann_predictor @ git+https://github.com/PaccMann/paccmann_predictor@sarscov2
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+ reinvent_models @ git+https://github.com/GT4SD/[email protected]
utils.py ADDED
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+ import logging
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+ from collections import defaultdict
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+ from typing import List
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+
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+ import mols2grid
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+ import pandas as pd
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+
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+ logger = logging.getLogger(__name__)
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+ logger.addHandler(logging.NullHandler())
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+
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+
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+ def draw_grid_generate(
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+ samples: List[str],
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+ seeds: List[str] = [],
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+ n_cols: int = 3,
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+ size=(140, 200),
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+ ) -> str:
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+ """
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+ Uses mols2grid to draw a HTML grid for the generated molecules
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+
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+ Args:
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+ samples: The generated samples.
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+ n_cols: Number of columns in grid. Defaults to 5.
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+ size: Size of molecule in grid. Defaults to (140, 200).
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+
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+ Returns:
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+ HTML to display
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+ """
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+
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+ result = defaultdict(list)
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+ result.update(
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+ {
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+ "SMILES": seeds + samples,
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+ "Name": [f"Seed_{i}" for i in range(len(seeds))]
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+ + [f"Generated_{i}" for i in range(len(samples))],
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+ },
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+ )
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+
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+ result_df = pd.DataFrame(result)
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+ obj = mols2grid.display(
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+ result_df,
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+ tooltip=list(result.keys()),
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+ height=1100,
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+ n_cols=n_cols,
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+ name="Results",
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+ size=size,
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+ )
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+ return obj.data