craftgamesnetwork/high-resolution-controlnet-tile

1import gradio as gr2from urllib.parse import urlparse3import requests4import time5import os6 7from utils.gradio_helpers import parse_outputs, process_outputs8 9inputs = []10inputs.append(gr.Textbox(11    label="Prompt", info='''Prompt for the model'''12))13 14inputs.append(gr.Image(15    label="Image", type="filepath"16))17 18inputs.append(gr.Dropdown(19    choices=[2048, 2560], label="resolution", info='''Image resolution''', value="2048"20))21 22inputs.append(gr.Number(23    label="Resemblance", info='''Conditioning scale for controlnet''', value=0.524))25 26inputs.append(gr.Number(27    label="Creativity", info='''Denoising strength. 1 means total destruction of the original image''', value=0.528))29 30inputs.append(gr.Number(31    label="Hdr", info='''HDR improvement over the original image''', value=032))33 34inputs.append(gr.Dropdown(35    choices=['DDIM', 'DPMSolverMultistep', 'K_EULER_ANCESTRAL', 'K_EULER'], label="scheduler", info='''Choose a scheduler.''', value="DDIM"36))37 38inputs.append(gr.Number(39    label="Steps", info='''Steps''', value=2040))41 42inputs.append(gr.Slider(43    label="Guidance Scale", info='''Scale for classifier-free guidance''', value=7,44    minimum=0.1, maximum=3045))46 47inputs.append(gr.Number(48    label="Seed", info='''Seed''', value=None49))50 51inputs.append(gr.Textbox(52    label="Negative Prompt", info='''Negative prompt'''53))54 55inputs.append(gr.Checkbox(56    label="Guess Mode", info='''In this mode, the ControlNet encoder will try best to recognize the content of the input image even if you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.''', value=False57))58 59names = ['prompt', 'image', 'resolution', 'resemblance', 'creativity', 'hdr', 'scheduler', 'steps', 'guidance_scale', 'seed', 'negative_prompt', 'guess_mode']60 61outputs = []62outputs.append(gr.Image())63 64expected_outputs = len(outputs)65def predict(request: gr.Request, *args, progress=gr.Progress(track_tqdm=True)):66    headers = {'Content-Type': 'application/json'}67 68    payload = {"input": {}}69    70    71    base_url = "http://0.0.0.0:7860"72    for i, key in enumerate(names):73        value = args[i]74        if value and (os.path.exists(str(value))):75            value = f"{base_url}/file=" + value76        if value is not None and value != "":77            payload["input"][key] = value78 79    response = requests.post("http://0.0.0.0:5000/predictions", headers=headers, json=payload)80 81    82    if response.status_code == 201:83        follow_up_url = response.json()["urls"]["get"]84        response = requests.get(follow_up_url, headers=headers)85        while response.json()["status"] != "succeeded":86            if response.json()["status"] == "failed":87                raise gr.Error("The submission failed!")88            response = requests.get(follow_up_url, headers=headers)89            time.sleep(1)90    if response.status_code == 200:91        json_response = response.json()92        #If the output component is JSON return the entire output response 93        if(outputs[0].get_config()["name"] == "json"):94            return json_response["output"]95        predict_outputs = parse_outputs(json_response["output"])96        processed_outputs = process_outputs(predict_outputs)97        difference_outputs = expected_outputs - len(processed_outputs)98        # If less outputs than expected, hide the extra ones99        if difference_outputs > 0:100            extra_outputs = [gr.update(visible=False)] * difference_outputs101            processed_outputs.extend(extra_outputs)102        # If more outputs than expected, cap the outputs to the expected number103        elif difference_outputs < 0:104            processed_outputs = processed_outputs[:difference_outputs]105        106        return tuple(processed_outputs) if len(processed_outputs) > 1 else processed_outputs[0]107    else:108        if(response.status_code == 409):109            raise gr.Error(f"Sorry, the Cog image is still processing. Try again in a bit.")110        raise gr.Error(f"The submission failed! Error: {response.status_code}")111 112title = "Demo for high-resolution-controlnet-tile cog image by batouresearch"113model_description = "Fermat.app open-source implementation of an efficient ControlNet 1.1 tile for high-quality upscales. Increase the creativity to encourage hallucination."114 115app = gr.Interface(116    fn=predict,117    inputs=inputs,118    outputs=outputs,119    title=title,120    description=model_description,121    allow_flagging="never",122)123app.launch(share=True)