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
from threading import Thread

class Prodia:
    def __init__(self, api_key, base=None):
        self.base = base or "https://api.prodia.com/v1"
        self.headers = {
            "X-Prodia-Key": api_key
        }

    def generate(self, params):
        response = self._post(f"{self.base}/sd/generate", params)
        return response.json()

    def transform(self, params):
        response = self._post(f"{self.base}/sd/transform", params)
        return response.json()

    def controlnet(self, params):
        response = self._post(f"{self.base}/sd/controlnet", params)
        return response.json()

    def upscale(self, params):
        response = self._post(f"{self.base}/upscale", 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(0.5)
            job_result = self.get_job(job['job'])

        return job_result

    def list_models(self):
        response = self._get(f"{self.base}/sd/models")
        return response.json()

    def list_loras(self):
        response = self._get(f"{self.base}/sd/loras")
        return response.json()

    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 Prodia 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 Prodia Response: {response.status_code}")

        return response


def image_to_base64(image):
    # Convert the image to bytes
    buffered = BytesIO()
    image.save(buffered, format="PNG")  # You can change format to PNG if needed

    # Encode the bytes to base64
    img_str = base64.b64encode(buffered.getvalue())

    return img_str.decode('utf-8')  # Convert bytes to string


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 data['model'] in model_names:
            result[model] = gr.update(value=model_names[data['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

def place_lora(current_prompt, lora_name):
    pattern = r"<lora:" + lora_name + r":.*?>"

    if re.search(pattern, current_prompt):
        yield re.sub(pattern, "", current_prompt)
    else:
        yield current_prompt + " <lora:" + lora_name + ":1> "

prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))
model_list = prodia_client.list_models()
lora_list = prodia_client.list_loras()
model_names = {}

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


def txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, batch_count, gallery):
    yield {
        text_button: gr.update(visible=False),
        stop_btn: gr.update(visible=True),
    }
    data = {
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "model": model,
        "steps": steps,
        "sampler": sampler,
        "cfg_scale": cfg_scale,
        "width": width,
        "height": height,
        "seed": seed
    }

    total_images = []
    threads = []

    def generate_one_image():
        result = prodia_client.generate(data)
        job = prodia_client.wait(result)
        total_images.append(job['imageUrl'])

    for x in range(batch_count):
        t = Thread(target=generate_one_image)
        threads.append(t)
        t.start()

    for t in threads:
        t.join()

    new_images_list = [img['name'] for img in gallery]

    for image in total_images:
        new_images_list.insert(0, image)

    if batch_count > 1:
        results = gr.update(value=total_images, preview=False)
    else:
        results = gr.update(value=total_images, preview=True)

    yield {
        text_button: gr.update(visible=True),
        stop_btn: gr.update(visible=False),
        image_output: results,
        gallery_obj: gr.update(value=new_images_list),
    }


def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed,
            batch_count, gallery):
    if input_image is None:
        return
    yield {
        i2i_text_button: gr.update(visible=False),
        i2i_stop_btn: gr.update(visible=True),
    }
    data = {
        "imageData": image_to_base64(input_image),
        "denoising_strength": denoising,
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "model": model,
        "steps": steps,
        "sampler": sampler,
        "cfg_scale": cfg_scale,
        "width": width,
        "height": height,
        "seed": seed
    }

    total_images = []
    threads = []

    def generate_one_image():
        result = prodia_client.transform(data)
        job = prodia_client.wait(result)
        total_images.append(job['imageUrl'])

    for x in range(batch_count):
        t = Thread(target=generate_one_image)
        threads.append(t)
        t.start()

    for t in threads:
        t.join()

    new_images_list = [img['name'] for img in gallery]

    for image in total_images:
        new_images_list.insert(0, image)

    if batch_count > 1:
        results = gr.update(value=total_images, preview=False)
    else:
        results = gr.update(value=total_images, preview=True)

    yield {
        i2i_text_button: gr.update(visible=True),
        i2i_stop_btn: gr.update(visible=False),
        i2i_image_output: results,
        gallery_obj: gr.update(value=new_images_list),
    }

def upscale_fn(image, scale):
    if image is None:
        return
    yield {
        upscale_btn: gr.update(visible=False),
        upscale_stop: gr.update(visible=True),
    }
    job = prodia_client.upscale({
        'imageData': image_to_base64(image),
        'resize': scale
    })

    result = prodia_client.wait(job)
    yield {
        upscale_output: result['imageUrl'],
        upscale_btn: gr.update(visible=True),
        upscale_stop: gr.update(visible=False)
    }

def stop_upscale():
    return {
        upscale_btn: gr.update(visible=True),
        upscale_stop: gr.update(visible=False)
    }

def stop_t2i():
    return {
        text_button: gr.update(visible=True),
        stop_btn: gr.update(visible=False)
    }

def stop_i2i():
    return {
        i2i_text_button: gr.update(visible=True),
        i2i_stop_btn: gr.update(visible=False)
    }



samplers = [
    "Euler",
    "Euler a",
    "LMS",
    "Heun",
    "DPM2",
    "DPM2 a",
    "DPM++ 2S a",
    "DPM++ 2M",
    "DPM++ SDE",
    "DPM fast",
    "DPM adaptive",
    "LMS Karras",
    "DPM2 Karras",
    "DPM2 a Karras",
    "DPM++ 2S a Karras",
    "DPM++ 2M Karras",
    "DPM++ SDE Karras",
    "DDIM",
    "PLMS",
]

css = """
:root, .dark{
    --checkbox-label-gap: 0.25em 0.1em;
    --section-header-text-size: 12pt;
    --block-background-fill: transparent;
}
.block.padded:not(.gradio-accordion) {
    padding: 0 !important;
}
div.gradio-container{
    max-width: unset !important;
}
.compact{
    background: transparent !important;
    padding: 0 !important;
}
div.form{
    border-width: 0;
    box-shadow: none;
    background: transparent;
    overflow: visible;
    gap: 0.5em;
}
.block.gradio-dropdown,
.block.gradio-slider,
.block.gradio-checkbox,
.block.gradio-textbox,
.block.gradio-radio,
.block.gradio-checkboxgroup,
.block.gradio-number,
.block.gradio-colorpicker {
    border-width: 0 !important;
    box-shadow: none !important;
}
.gradio-dropdown label span:not(.has-info),
.gradio-textbox label span:not(.has-info),
.gradio-number label span:not(.has-info)
{
    margin-bottom: 0;
}
.gradio-dropdown ul.options{
    z-index: 3000;
    min-width: fit-content;
    max-width: inherit;
    white-space: nowrap;
}
.gradio-dropdown ul.options li.item {
    padding: 0.05em 0;
}
.gradio-dropdown ul.options li.item.selected {
    background-color: var(--neutral-100);
}
.dark .gradio-dropdown ul.options li.item.selected {
    background-color: var(--neutral-900);
}
.gradio-dropdown div.wrap.wrap.wrap.wrap{
    box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
}
.gradio-dropdown:not(.multiselect) .wrap-inner.wrap-inner.wrap-inner{
    flex-wrap: unset;
}
.gradio-dropdown .single-select{
    white-space: nowrap;
    overflow: hidden;
}
.gradio-dropdown .token-remove.remove-all.remove-all{
    display: none;
}
.gradio-dropdown.multiselect .token-remove.remove-all.remove-all{
    display: flex;
}
.gradio-slider input[type="number"]{
    width: 6em;
}
.block.gradio-checkbox {
    margin: 0.75em 1.5em 0 0;
}
.gradio-html div.wrap{
    height: 100%;
}
div.gradio-html.min{
    min-height: 0;
}
#model_dd {
    width: 16%;
}
"""

with gr.Blocks(css=css) as demo:
    model = gr.Dropdown(interactive=True, value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True,
                                label="Stable Diffusion Checkpoint", choices=prodia_client.list_models(), elem_id="model_dd")

    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("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k",
                                        placeholder="Prompt", show_label=False, lines=3)
                    negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3,
                                                 value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly")
                with gr.Row():
                    text_button = gr.Button("Generate", variant='primary', elem_id="generate")
                    stop_btn = gr.Button("Cancel", variant="stop", elem_id="generate", visible=False)

            with gr.Row():
                with gr.Column():
                    with gr.Tab("Generation"):
                        with gr.Row():
                            with gr.Column(scale=1):
                                sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method",
                                                      choices=samplers)

                            with gr.Column(scale=1):
                                steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=100, value=25, step=1)

                        with gr.Row():
                            with gr.Column(scale=8):
                                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", minimum=1, maximum=4, value=1, step=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.Tab("Lora"):
                        with gr.Row():
                            for lora in lora_list:
                                lora_btn = gr.Button(lora, size="sm")
                                lora_btn.click(place_lora, inputs=[prompt, lora_btn], outputs=prompt)

                with gr.Column():
                    image_output = gr.Gallery(columns=3,
                        value=["https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png"])

        with gr.Tab("img2img", id='i2i'):
            with gr.Row():
                with gr.Column(scale=6, min_width=600):
                    i2i_prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k",
                                            placeholder="Prompt", show_label=False, lines=3)
                    i2i_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3,
                                                     value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly")
                with gr.Row():
                    i2i_text_button = gr.Button("Generate", variant='primary', elem_id="generate")
                    i2i_stop_btn = gr.Button("Cancel", variant="stop", elem_id="generate", visible=False)

            with gr.Row():
                with gr.Column(scale=1):
                    with gr.Tab("Generation"):
                        i2i_image_input = gr.Image(type="pil")

                        with gr.Row():
                            with gr.Column(scale=1):
                                i2i_sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True,
                                                          label="Sampling Method", choices=samplers)

                            with gr.Column(scale=1):
                                i2i_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=100, value=25, step=1)

                        with gr.Row():
                            with gr.Column(scale=6):
                                i2i_width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
                                i2i_height = gr.Slider(label="Height", maximum=1024, value=512, step=8)

                            with gr.Column(scale=1):
                                i2i_batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
                                i2i_batch_count = gr.Slider(label="Batch Count", minimum=1, maximum=4, value=1, step=1)

                        i2i_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
                        i2i_denoising = gr.Slider(label="Denoising Strength", minimum=0, maximum=1, value=0.7, step=0.1)
                        i2i_seed = gr.Number(label="Seed", value=-1)

                    with gr.Tab("Lora"):
                        with gr.Row():
                            for lora in lora_list:
                                lora_btn = gr.Button(lora, size="sm")
                                lora_btn.click(place_lora, inputs=[i2i_prompt, lora_btn], outputs=i2i_prompt)

                with gr.Column(scale=1):
                    i2i_image_output = gr.Gallery(columns=3,
                        value=["https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png"])

        with gr.Tab("Extras"):
            with gr.Row():
                with gr.Tab("Single Image"):
                    with gr.Column():
                        upscale_image_input = gr.Image(type="pil")
                        upscale_btn = gr.Button("Generate", variant="primary")
                        upscale_stop = gr.Button("Stop", variant="stop", visible=False)
                        with gr.Tab("Scale by"):
                            scale_by = gr.Radio([2, 4], value=2, label="Resize")

                upscale_output = gr.Image()

        with gr.Tab("PNG Info"):
            def plaintext_to_html(text, classname=None):
                content = "<br>\n".join(html.escape(x) for x in text.split('\n'))

                return f"<p class='{classname}'>{content}</p>" if classname else f"<p>{content}</p>"


            def get_exif_data(image):
                items = image.info

                info = ''
                for key, text in items.items():
                    info += f"""
                    <div>
                    <p><b>{plaintext_to_html(str(key))}</b></p>
                    <p>{plaintext_to_html(str(text))}</p>
                    </div>
                    """.strip() + "\n"

                if len(info) == 0:
                    message = "Nothing found in the image."
                    info = f"<div><p>{message}<p></div>"

                return info


            with gr.Row():
                with gr.Column():
                    image_input = gr.Image(type="pil")

                with gr.Column():
                    exif_output = gr.HTML(label="EXIF Data")
                    send_to_txt2img_btn = gr.Button("Send to txt2img")

        with gr.Tab("Gallery"):
            gallery_obj = gr.Gallery(height=1000, columns=6)

        t2i_event = text_button.click(txt2img,
                                      inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height,
                                              seed, batch_count, gallery_obj], outputs=[image_output, gallery_obj, text_button, stop_btn])
        stop_btn.click(fn=stop_t2i, outputs=[text_button, stop_btn], cancels=[t2i_event])

        image_input.upload(get_exif_data, inputs=[image_input], outputs=exif_output)
        send_to_txt2img_btn.click(send_to_txt2img, inputs=[image_input],
                                  outputs=[tabs, prompt, negative_prompt, steps, seed, model, sampler, width, height,
                                           cfg_scale])

        i2i_event = i2i_text_button.click(img2img,
                                          inputs=[i2i_image_input, i2i_denoising, i2i_prompt, i2i_negative_prompt,
                                                  model, i2i_steps, i2i_sampler, i2i_cfg_scale, i2i_width, i2i_height,
                                                  i2i_seed, i2i_batch_count, gallery_obj],
                                          outputs=[i2i_image_output, gallery_obj, i2i_text_button, i2i_stop_btn])
        i2i_stop_btn.click(fn=stop_i2i, outputs=[i2i_text_button, i2i_stop_btn], cancels=[i2i_event])

        upscale_event = upscale_btn.click(fn=upscale_fn, inputs=[upscale_image_input, scale_by], outputs=[upscale_output, upscale_btn, upscale_stop])
        upscale_stop.click(fn=stop_upscale, outputs=[upscale_btn, upscale_stop], cancels=[upscale_event])

demo.queue().launch(max_threads=256)