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| import gradio as gr | |
| import spaces | |
| from gradio_imageslider import ImageSlider | |
| from image_gen_aux import UpscaleWithModel | |
| from image_gen_aux.utils import load_image | |
| # This uses https://github.com/asomoza/image_gen_aux/blob/main/src/image_gen_aux/upscalers/README.md | |
| # Also this space has been duplicated from their official huggingface space, https://huggingface.co/spaces/OzzyGT/basic_upscaler | |
| # They did great work, and I was happy to see them to also use my models :) I thought Id duplicate it and extend it. | |
| # It basically made me get a pro account so I can make a Zero GPU space. And I will also upload more of my models as a model card now to use here. | |
| # Start out with my own models. If others like kim, sirosky, and other model trainers would like their models added here, then thats great. | |
| # I simply want them to message me first so I know that everythings okay with having their model as a selection here since they are the author of that model. If they want their model on here or not basically. | |
| # I load models from huggingface model cards though, so the model should be hosted on huggingface. | |
| MODELS = { | |
| "4xNomos2_hq_drct-l": "Phips/4xNomos2_hq_drct-l", | |
| "4xNomosWebPhoto_RealPLKSR": "Phips/4xNomosWebPhoto_RealPLKSR", | |
| "4xRealWebPhoto_v4_dat2": "Phips/4xRealWebPhoto_v4_dat2", | |
| "4xRealWebPhoto_v3_atd": "Phips/4xRealWebPhoto_v3_atd", | |
| "4xNomos8k_atd_jpg": "Phips/4xNomos8k_atd_jpg", | |
| "4xNomosUni_rgt_multijpg": "Phips/4xNomosUni_rgt_multijpg", | |
| "4xLSDIRDAT": "Phips/4xLSDIRDAT", | |
| "4xSSDIRDAT": "Phips/4xSSDIRDAT", | |
| "4xNomos8kHAT-L_otf": "Phips/4xNomos8kHAT-L_otf", | |
| "4xNomosUniDAT_otf": "Phips/4xNomosUniDAT_otf", | |
| "4xNomosUniDAT_bokeh_jpg": "Phips/4xNomosUniDAT_bokeh_jpg", | |
| "4xNomos8kSCHAT-L": "Phips/4xNomos8kSCHAT-L", | |
| "4xFFHQDAT": "Phips/4xFFHQDAT", | |
| "4xFaceUpDAT": "Phips/4xFaceUpDAT", | |
| "4xTextures_GTAV_rgt-s_dither": "Phips/4xTextures_GTAV_rgt-s_dither", | |
| "4xTextureDAT2_otf": "Phips/4xTextureDAT2_otf", | |
| "4xLexicaDAT2_otf": "Phips/4xLexicaDAT2_otf", | |
| "2xHFA2k_LUDVAE_compact": "Phips/2xHFA2k_LUDVAE_compact", | |
| "2xHFA2kAVCCompact": "Phips/2xHFA2kAVCCompact", | |
| "2xHFA2kCompact": "Phips/2xHFA2kCompact", | |
| "2xEvangelion_dat2": "Phips/2xEvangelion_dat2", | |
| "1xDeJPG_realplksr_otf": "Phips/1xDeJPG_realplksr_otf", | |
| "1xDeH264_realplksr": "Phips/1xDeH264_realplksr", | |
| "1xDeNoise_realplksr_otf": "Phips/1xDeNoise_realplksr_otf", | |
| "1xExposureCorrection_compact": "Phips/1xExposureCorrection_compact", | |
| "1xUnderExposureCorrection_compact": "Phips/1xUnderExposureCorrection_compact", | |
| "1xOverExposureCorrection_compact": "Phips/1xOverExposureCorrection_compact", | |
| } | |
| def upscale_image(image, model_selection): | |
| original = load_image(image) | |
| upscaler = UpscaleWithModel.from_pretrained(MODELS[model_selection]).to("cuda") | |
| image = upscaler(original, tiling=True, tile_width=1024, tile_height=1024) | |
| return original, image | |
| def clear_result(): | |
| return gr.update(value=None) | |
| title = """<h1 align="center">Image Upscaler</h1> | |
| <div align="center">Use this Space to upscale your images, makes use of the | |
| <a href="https://github.com/asomoza/image_gen_aux">Image Generation Auxiliary Tools</a> library. <br> This space makes use of my [self trained models](https://github.com/Phhofm/models), but can be extended to more models from other authors if they message me.</div> | |
| """ | |
| with gr.Blocks() as demo: | |
| gr.HTML(title) | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(type="pil", label="Input Image") | |
| model_selection = gr.Dropdown( | |
| choices=list(MODELS.keys()), | |
| value="4xNomos2_hq_drct-l", | |
| label="Model", | |
| ) | |
| run_button = gr.Button("Upscale") | |
| with gr.Column(): | |
| result = ImageSlider( | |
| interactive=False, | |
| label="Generated Image", | |
| ) | |
| run_button.click( | |
| fn=clear_result, | |
| inputs=None, | |
| outputs=result, | |
| ).then( | |
| fn=upscale_image, | |
| inputs=[input_image, model_selection], | |
| outputs=result, | |
| ) | |
| demo.launch(share=False) | |