app.py

import numpy as np
import gradio as gr
import requests
import time
import json
import base64
import os
from io import BytesIO
import PIL
from PIL.ExifTags import TAGS
import html
import re


class RenderNet:
    def __init__(self, api_key, base=None):
        self.base = base or "https://app.rendernet.ai/api/"
        self.headers = {
            "rendernetapikey": api_key
        }
    
    def generate(self, params):
        response = self._post(f"{self.base}/generate", params)
        return response.json()
    
    def get_job(self, job_id):
        response = self._get(f"{self.base}/job/{job_id}")
        return response.json()

    def wait(self, job):
        job_result = job

        while job_result['status'] not in ['succeeded', 'failed']:
            time.sleep(1)
            job_result = self.get_job(job['job'])
            print("Job Result:")
            print(job_result)
        return job_result

    def list_models(self):
        response =[
                    'dreamshaper',        
                    'ghost_mix',
                    'proto_vision',       
                    'meina_unreal',
                    'realistic_vision',   
                    'animerge',
                    'oil_painting',       
                    'absolute_reality',
                    'meinamix',           
                    'rpg',
                    'western_animation',  
                    'dynavision',
                    'Realvis_XL',         
                    'abyss_orange_mix',
                    'anything_v5',       
                    'dreamshaper_xl',
                    'epic_realism',       
                    'comicbook_style',
                    'newdawnxl',          
                    'nextphoto',
                    'nightvisionxl',      
                    'juggernaut',
                    'cyber_realistic',    
                    'real_cartoon',
                    'unstable_diffusers', 
                    'cant_believe',
                    'majicmix_fantasy',   
                    'analog_madness',
                    'blazing_drive',     
                    'mysteriousxl',
                    'night_sky',         
                    'never_ending',
                    'dark_sushi',        
                    'meina_pastel',
                    'meina_alter',        
                    'counterfeitxl_v10',
                    'plagion_v10',        
                    'replicant_v3.0'
                    ]

        return response

    def list_samplers(self):
        response = [
                    'DPM++ SDE Karras',
                    'DPM++ 2M Karras'
                    'DPM++ 2S a Karras',
                    'Euler a',
                    'DPM++ 2M SDE Karras'
                    ]
        return response

    def _post(self, url, params):
        headers = {
            **self.headers,
            "Content-Type": "application/json"
        }
        response = requests.post(url, headers=headers, data=json.dumps(params))

        if response.status_code != 200:
            raise Exception(f"Bad RenderNet Response: {response.status_code}")

        return response

    def _get(self, url):
        response = requests.get(url, headers=self.headers)

        if response.status_code != 200:
            raise Exception(f"Bad RenderNet Response: {response.status_code}")

        return response


def remove_id_and_ext(text):
    text = re.sub(r'\[.*\]$', '', text)
    extension = text[-12:].strip()
    if extension == "safetensors":
        text = text[:-13]
    elif extension == "ckpt":
        text = text[:-4]
    return text

def get_data(text):
    results = {}
    patterns = {
        'prompt': r'(.*)',
        'negative_prompt': r'Negative prompt: (.*)',
        'steps': r'Steps: (\d+),',
        'seed': r'Seed: (\d+),',
        'sampler': r'Sampler:\s*([^\s,]+(?:\s+[^\s,]+)*)', 
        'model': r'Model:\s*([^\s,]+)',
        'cfg_scale': r'CFG scale:\s*([\d\.]+)',
        'size': r'Size:\s*([0-9]+x[0-9]+)'
        }
    for key in ['prompt', 'negative_prompt', 'steps', 'seed', 'sampler', 'model', 'cfg_scale', 'size']:
        match = re.search(patterns[key], text)
        if match:
            results[key] = match.group(1)
        else:
            results[key] = None
    if results['size'] is not None:
        w, h = results['size'].split("x")
        results['w'] = w
        results['h'] = h
    else:
        results['w'] = None
        results['h'] = None
    return results

def send_to_txt2img(image):
    
    result = {tabs: gr.Tabs.update(selected="t2i")}

    try:
        text = image.info['parameters']
        data = get_data(text)
        result[prompt] = gr.update(value=data['prompt'])
        result[negative_prompt] = gr.update(value=data['negative_prompt']) if data['negative_prompt'] is not None else gr.update()
        result[steps] = gr.update(value=int(data['steps'])) if data['steps'] is not None else gr.update()
        result[seed] = gr.update(value=int(data['seed'])) if data['seed'] is not None else gr.update()
        result[cfg_scale] = gr.update(value=float(data['cfg_scale'])) if data['cfg_scale'] is not None else gr.update()
        result[width] = gr.update(value=int(data['w'])) if data['w'] is not None else gr.update()
        result[height] = gr.update(value=int(data['h'])) if data['h'] is not None else gr.update()
        result[sampler] = gr.update(value=data['sampler']) if data['sampler'] is not None else gr.update()
        if model in model_names:
            result[model] = gr.update(value=model_names[model])
        else:
            result[model] = gr.update()
        return result

    except Exception as e:
        print(e)
        result[prompt] = gr.update()
        result[negative_prompt] = gr.update()
        result[steps] = gr.update()
        result[seed] = gr.update()
        result[cfg_scale] = gr.update()
        result[width] = gr.update()
        result[height] = gr.update()
        result[sampler] = gr.update()
        result[model] = gr.update()

        return result


rendernet_client = RenderNet(api_key=os.getenv("API_KEY"))
model_list = rendernet_client.list_models()
model_names = {}

for model_name in model_list:
    name_without_ext = remove_id_and_ext(model_name)
    print(name_without_ext)
    model_names[name_without_ext] = model_name
    print(model_names)

def txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
    result = rendernet_client.generate({
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "model": model,
        "steps": steps,
        "sampler": sampler,
        "cfg_scale": cfg_scale,
        "width": width,
        "height": height,
        "seed": seed
    })
    
    print("Result Value:")
    print("")
    print(result)

    print("Job Value:")
    print("")
    job = rendernet_client.wait(result)

    return job["imageUrl"]
    
# Static Image URL
static_image_url = "https://baseavaar2.s3.amazonaws.com/banner.png"
static_image_link_url="https://rendernet.ai"

css = """
#generate {
    height: 100%;
}
"""

with gr.Blocks(css=css) as demo:

    # Add a Row for the Static Image
    with gr.Row():
        with gr.Column():
            static_image_with_link = gr.HTML(f'<a href="{static_image_link_url}" target="_blank"><img src="{static_image_url}" /></a>')
            
    with gr.Row():
        with gr.Column(scale=6):
            model = gr.Dropdown(interactive=True, value="dreamshaper", show_label=True, label="Stable Diffusion Checkpoint", choices=rendernet_client.list_models())
  
        with gr.Column(scale=1):
            gr.Markdown(elem_id="powered-by-rendernet", value="AUTOMATIC1111 Stable Diffusion Web UI.<br>Powered by [RenderNet](https://rendernet.ai).<br>For advanced features and faster generation times check out our API and Website(https://rendernet.ai/).")


    with gr.Tabs() as tabs:
        with gr.Tab("txt2img", id='t2i'):
            with gr.Row():
                with gr.Column(scale=6, min_width=600):
                    prompt = gr.Textbox("a master jedi cat in star wars with a lightsaber, wearing a jedi cloak hood in the kitchen (cat:1.3)", placeholder="Prompt", show_label=False, lines=3)
                    negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="(worst quality, low quality, normal quality:2)")
                with gr.Column():
                    text_button = gr.Button("Generate", variant='primary', elem_id="generate")
                    
            with gr.Row():
                with gr.Column(scale=3):
                    with gr.Tab("Generation"):
                        with gr.Row():
                            with gr.Column(scale=1):
                                sampler = gr.Dropdown(show_label=True, value="DPM++ SDE Karras", label="Sampling Method", choices=rendernet_client.list_samplers())
                                
                            with gr.Column(scale=1):
                                steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=30, value=25, step=1)
    
                        with gr.Row():
                            with gr.Column(scale=1):
                                width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
                                height = gr.Slider(label="Height", maximum=1024, value=512, step=8)
                            
                            with gr.Column(scale=1):
                                batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
                                batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
    
                        cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
                        seed = gr.Number(label="Seed", value=-1)
    
                    
                with gr.Column(scale=2):
                    image_output = gr.Image(value="https://app.rendernet.ai/userfiles/1699956614571.5938.png")
    
            text_button.click(txt2img, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output)            


demo.queue(max_size=80, api_open=False).launch(max_threads=256)