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import gradio as gr |
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from urllib.parse import urlparse |
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import requests |
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import time |
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import os |
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from utils.gradio_helpers import parse_outputs, process_outputs |
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inputs = [] |
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inputs.append(gr.Image( |
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label="Image", type="filepath" |
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)) |
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inputs.append(gr.Textbox( |
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label="Prompt", info='''Prompt''' |
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)) |
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inputs.append(gr.Textbox( |
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label="Negative Prompt", info='''Negative Prompt''' |
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)) |
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inputs.append(gr.Number( |
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label="Scale Factor", info='''Scale factor''', value=2 |
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)) |
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inputs.append(gr.Slider( |
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label="Dynamic", info='''HDR, try from 3 - 9''', value=6, |
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minimum=1, maximum=50 |
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)) |
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inputs.append(gr.Number( |
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label="Creativity", info='''Creativity, try from 0.3 - 0.9''', value=0.35 |
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)) |
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inputs.append(gr.Number( |
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label="Resemblance", info='''Resemblance, try from 0.3 - 1.6''', value=0.6 |
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)) |
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inputs.append(gr.Dropdown( |
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choices=[16, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208, 224, 240, 256], label="tiling_width", info='''Fractality, set lower tile width for a high Fractality''', value="112" |
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)) |
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inputs.append(gr.Dropdown( |
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choices=[16, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208, 224, 240, 256], label="tiling_height", info='''Fractality, set lower tile height for a high Fractality''', value="144" |
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)) |
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inputs.append(gr.Dropdown( |
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choices=['epicrealism_naturalSinRC1VAE.safetensors [84d76a0328]', 'juggernaut_reborn.safetensors [338b85bc4f]', 'flat2DAnimerge_v45Sharp.safetensors'], label="sd_model", info='''Stable Diffusion model checkpoint''', value="juggernaut_reborn.safetensors [338b85bc4f]" |
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)) |
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inputs.append(gr.Dropdown( |
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choices=['DPM++ 2M Karras', 'DPM++ SDE Karras', 'DPM++ 2M SDE Exponential', 'DPM++ 2M SDE Karras', 'Euler a', 'Euler', 'LMS', 'Heun', 'DPM2', 'DPM2 a', 'DPM++ 2S a', 'DPM++ 2M', 'DPM++ SDE', 'DPM++ 2M SDE', 'DPM++ 2M SDE Heun', 'DPM++ 2M SDE Heun Karras', 'DPM++ 2M SDE Heun Exponential', 'DPM++ 3M SDE', 'DPM++ 3M SDE Karras', 'DPM++ 3M SDE Exponential', 'DPM fast', 'DPM adaptive', 'LMS Karras', 'DPM2 Karras', 'DPM2 a Karras', 'DPM++ 2S a Karras', 'Restart', 'DDIM', 'PLMS', 'UniPC'], label="scheduler", info='''scheduler''', value="DPM++ 3M SDE Karras" |
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)) |
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inputs.append(gr.Slider( |
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label="Num Inference Steps", info='''Number of denoising steps''', value=18, |
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minimum=1, maximum=100, step=1, |
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)) |
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inputs.append(gr.Number( |
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label="Seed", info='''Random seed. Leave blank to randomize the seed''', value=1337 |
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)) |
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inputs.append(gr.Checkbox( |
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label="Downscaling", info='''Downscale the image before upscaling. Can improve quality and speed for images with high resolution but lower quality''', value=False |
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)) |
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inputs.append(gr.Number( |
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label="Downscaling Resolution", info='''Downscaling resolution''', value=768 |
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)) |
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inputs.append(gr.Textbox( |
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label="Lora Links", info='''Link to a lora file you want to use in your upscaling. Multiple links possible, seperated by comma''' |
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)) |
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inputs.append(gr.Textbox( |
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label="Custom Sd Model", info='''Link to a custom safetensors checkpoint file you want to use in your upscaling. Will overwrite sd_model checkpoint.''' |
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)) |
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names = ['image', 'prompt', 'negative_prompt', 'scale_factor', 'dynamic', 'creativity', 'resemblance', 'tiling_width', 'tiling_height', 'sd_model', 'scheduler', 'num_inference_steps', 'seed', 'downscaling', 'downscaling_resolution', 'lora_links', 'custom_sd_model'] |
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outputs = [] |
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outputs.append(gr.Image()) |
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expected_outputs = len(outputs) |
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def predict(request: gr.Request, *args, progress=gr.Progress(track_tqdm=True)): |
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headers = {'Content-Type': 'application/json'} |
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payload = {"input": {}} |
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base_url = "http://0.0.0.0:7860" |
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for i, key in enumerate(names): |
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value = args[i] |
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if value and (os.path.exists(str(value))): |
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value = f"{base_url}/file=" + value |
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if value is not None and value != "": |
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payload["input"][key] = value |
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response = requests.post("http://0.0.0.0:5000/predictions", headers=headers, json=payload) |
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if response.status_code == 201: |
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follow_up_url = response.json()["urls"]["get"] |
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response = requests.get(follow_up_url, headers=headers) |
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while response.json()["status"] != "succeeded": |
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if response.json()["status"] == "failed": |
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raise gr.Error("The submission failed!") |
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response = requests.get(follow_up_url, headers=headers) |
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time.sleep(1) |
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if response.status_code == 200: |
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json_response = response.json() |
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if(outputs[0].get_config()["name"] == "json"): |
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return json_response["output"] |
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predict_outputs = parse_outputs(json_response["output"]) |
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processed_outputs = process_outputs(predict_outputs) |
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difference_outputs = expected_outputs - len(processed_outputs) |
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if difference_outputs > 0: |
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extra_outputs = [gr.update(visible=False)] * difference_outputs |
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processed_outputs.extend(extra_outputs) |
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elif difference_outputs < 0: |
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processed_outputs = processed_outputs[:difference_outputs] |
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return tuple(processed_outputs) if len(processed_outputs) > 1 else processed_outputs[0] |
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else: |
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if(response.status_code == 409): |
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raise gr.Error(f"Sorry, the Cog image is still processing. Try again in a bit.") |
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raise gr.Error(f"The submission failed! Error: {response.status_code}") |
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title = "Demo for clarity-upscaler cog image by philz1337x" |
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model_description = "High resolution image Upscaler and Enhancer. Use at ClarityAI.cc. A free Magnific alternative. Twitter/X: @philz1337x" |
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app = gr.Interface( |
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fn=predict, |
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inputs=inputs, |
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outputs=outputs, |
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title=title, |
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description=model_description, |
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allow_flagging="never", |
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) |
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app.launch(share=True) |
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