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import numpy as np |
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import gradio as gr |
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import requests |
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import time |
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import json |
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import base64 |
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import os |
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from io import BytesIO |
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import PIL |
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from PIL.ExifTags import TAGS |
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import html |
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import re |
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from threading import Thread |
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class Prodia: |
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def __init__(self, api_key, base=None): |
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self.base = base or "https://api.prodia.com/v1" |
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self.headers = { |
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"X-Prodia-Key": api_key |
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} |
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|
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def generate(self, params): |
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response = self._post(f"{self.base}/sd/generate", params) |
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return response.json() |
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def transform(self, params): |
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response = self._post(f"{self.base}/sd/transform", params) |
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return response.json() |
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|
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def controlnet(self, params): |
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response = self._post(f"{self.base}/sd/controlnet", params) |
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return response.json() |
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|
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def upscale(self, params): |
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response = self._post(f"{self.base}/upscale", params) |
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return response.json() |
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|
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def get_job(self, job_id): |
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response = self._get(f"{self.base}/job/{job_id}") |
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return response.json() |
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def wait(self, job): |
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job_result = job |
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|
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while job_result['status'] not in ['succeeded', 'failed']: |
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time.sleep(0.5) |
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job_result = self.get_job(job['job']) |
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return job_result |
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def list_models(self): |
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response = self._get(f"{self.base}/sd/models") |
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return response.json() |
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def list_loras(self): |
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response = self._get(f"{self.base}/sd/loras") |
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return response.json() |
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def _post(self, url, params): |
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headers = { |
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**self.headers, |
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"Content-Type": "application/json" |
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} |
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response = requests.post(url, headers=headers, data=json.dumps(params)) |
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if response.status_code != 200: |
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raise Exception(f"Bad Prodia Response: {response.status_code}") |
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return response |
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def _get(self, url): |
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response = requests.get(url, headers=self.headers) |
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if response.status_code != 200: |
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raise Exception(f"Bad Prodia Response: {response.status_code}") |
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return response |
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def image_to_base64(image): |
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buffered = BytesIO() |
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image.save(buffered, format="PNG") |
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img_str = base64.b64encode(buffered.getvalue()) |
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return img_str.decode('utf-8') |
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def remove_id_and_ext(text): |
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text = re.sub(r'\[.*\]$', '', text) |
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extension = text[-12:].strip() |
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if extension == "safetensors": |
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text = text[:-13] |
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elif extension == "ckpt": |
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text = text[:-4] |
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return text |
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def get_data(text): |
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results = {} |
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patterns = { |
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'prompt': r'(.*)', |
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'negative_prompt': r'Negative prompt: (.*)', |
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'steps': r'Steps: (\d+),', |
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'seed': r'Seed: (\d+),', |
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'sampler': r'Sampler:\s*([^\s,]+(?:\s+[^\s,]+)*)', |
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'model': r'Model:\s*([^\s,]+)', |
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'cfg_scale': r'CFG scale:\s*([\d\.]+)', |
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'size': r'Size:\s*([0-9]+x[0-9]+)' |
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} |
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for key in ['prompt', 'negative_prompt', 'steps', 'seed', 'sampler', 'model', 'cfg_scale', 'size']: |
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match = re.search(patterns[key], text) |
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if match: |
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results[key] = match.group(1) |
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else: |
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results[key] = None |
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if results['size'] is not None: |
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w, h = results['size'].split("x") |
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results['w'] = w |
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results['h'] = h |
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else: |
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results['w'] = None |
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results['h'] = None |
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return results |
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def send_to_txt2img(image): |
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result = {tabs: gr.Tabs.update(selected="t2i")} |
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try: |
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text = image.info['parameters'] |
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data = get_data(text) |
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result[prompt] = gr.update(value=data['prompt']) |
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result[negative_prompt] = gr.update(value=data['negative_prompt']) if data[ |
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'negative_prompt'] is not None else gr.update() |
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result[steps] = gr.update(value=int(data['steps'])) if data['steps'] is not None else gr.update() |
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result[seed] = gr.update(value=int(data['seed'])) if data['seed'] is not None else gr.update() |
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result[cfg_scale] = gr.update(value=float(data['cfg_scale'])) if data['cfg_scale'] is not None else gr.update() |
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result[width] = gr.update(value=int(data['w'])) if data['w'] is not None else gr.update() |
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result[height] = gr.update(value=int(data['h'])) if data['h'] is not None else gr.update() |
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result[sampler] = gr.update(value=data['sampler']) if data['sampler'] is not None else gr.update() |
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if data['model'] in model_names: |
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result[model] = gr.update(value=model_names[data['model']]) |
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else: |
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result[model] = gr.update() |
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return result |
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except Exception as e: |
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print(e) |
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result[prompt] = gr.update() |
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result[negative_prompt] = gr.update() |
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result[steps] = gr.update() |
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result[seed] = gr.update() |
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result[cfg_scale] = gr.update() |
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result[width] = gr.update() |
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result[height] = gr.update() |
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result[sampler] = gr.update() |
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result[model] = gr.update() |
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return result |
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def place_lora(current_prompt, lora_name): |
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pattern = r"<lora:" + lora_name + r":.*?>" |
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|
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if re.search(pattern, current_prompt): |
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yield re.sub(pattern, "", current_prompt) |
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else: |
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yield current_prompt + " <lora:" + lora_name + ":1> " |
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prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY")) |
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model_list = prodia_client.list_models() |
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lora_list = prodia_client.list_loras() |
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model_names = {} |
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for model_name in model_list: |
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name_without_ext = remove_id_and_ext(model_name) |
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model_names[name_without_ext] = model_name |
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def txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, batch_count, gallery): |
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yield { |
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text_button: gr.update(visible=False), |
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stop_btn: gr.update(visible=True), |
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} |
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data = { |
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"prompt": prompt, |
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"negative_prompt": negative_prompt, |
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"model": model, |
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"steps": steps, |
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"sampler": sampler, |
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"cfg_scale": cfg_scale, |
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"width": width, |
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"height": height, |
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"seed": seed |
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} |
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total_images = [] |
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threads = [] |
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|
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def generate_one_image(): |
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result = prodia_client.generate(data) |
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job = prodia_client.wait(result) |
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total_images.append(job['imageUrl']) |
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for x in range(batch_count): |
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t = Thread(target=generate_one_image) |
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threads.append(t) |
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t.start() |
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for t in threads: |
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t.join() |
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new_images_list = [img['name'] for img in gallery] |
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for image in total_images: |
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new_images_list.insert(0, image) |
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if batch_count > 1: |
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results = gr.update(value=total_images, preview=False) |
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else: |
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results = gr.update(value=total_images, preview=True) |
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yield { |
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text_button: gr.update(visible=True), |
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stop_btn: gr.update(visible=False), |
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image_output: results, |
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gallery_obj: gr.update(value=new_images_list), |
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} |
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def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, |
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batch_count, gallery): |
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if input_image is None: |
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return |
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yield { |
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i2i_text_button: gr.update(visible=False), |
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i2i_stop_btn: gr.update(visible=True), |
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} |
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data = { |
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"imageData": image_to_base64(input_image), |
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"denoising_strength": denoising, |
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"prompt": prompt, |
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"negative_prompt": negative_prompt, |
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"model": model, |
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"steps": steps, |
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"sampler": sampler, |
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"cfg_scale": cfg_scale, |
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"width": width, |
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"height": height, |
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"seed": seed |
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} |
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total_images = [] |
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threads = [] |
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|
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def generate_one_image(): |
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result = prodia_client.transform(data) |
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job = prodia_client.wait(result) |
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total_images.append(job['imageUrl']) |
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for x in range(batch_count): |
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t = Thread(target=generate_one_image) |
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threads.append(t) |
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t.start() |
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for t in threads: |
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t.join() |
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new_images_list = [img['name'] for img in gallery] |
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|
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for image in total_images: |
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new_images_list.insert(0, image) |
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if batch_count > 1: |
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results = gr.update(value=total_images, preview=False) |
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else: |
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results = gr.update(value=total_images, preview=True) |
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yield { |
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i2i_text_button: gr.update(visible=True), |
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i2i_stop_btn: gr.update(visible=False), |
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i2i_image_output: results, |
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gallery_obj: gr.update(value=new_images_list), |
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} |
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def upscale_fn(image, scale): |
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if image is None: |
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return |
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yield { |
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upscale_btn: gr.update(visible=False), |
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upscale_stop: gr.update(visible=True), |
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} |
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job = prodia_client.upscale({ |
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'imageData': image_to_base64(image), |
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'resize': scale |
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}) |
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result = prodia_client.wait(job) |
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yield { |
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upscale_output: result['imageUrl'], |
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upscale_btn: gr.update(visible=True), |
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upscale_stop: gr.update(visible=False) |
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} |
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def stop_upscale(): |
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return { |
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upscale_btn: gr.update(visible=True), |
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upscale_stop: gr.update(visible=False) |
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} |
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def stop_t2i(): |
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return { |
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text_button: gr.update(visible=True), |
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stop_btn: gr.update(visible=False) |
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} |
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def stop_i2i(): |
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return { |
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i2i_text_button: gr.update(visible=True), |
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i2i_stop_btn: gr.update(visible=False) |
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} |
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samplers = [ |
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"Euler", |
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"Euler a", |
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"LMS", |
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"Heun", |
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"DPM2", |
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"DPM2 a", |
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"DPM++ 2S a", |
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"DPM++ 2M", |
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"DPM++ SDE", |
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"DPM fast", |
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"DPM adaptive", |
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"LMS Karras", |
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"DPM2 Karras", |
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"DPM2 a Karras", |
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"DPM++ 2S a Karras", |
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"DPM++ 2M Karras", |
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"DPM++ SDE Karras", |
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"DDIM", |
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"PLMS", |
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] |
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css = """ |
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:root, .dark{ |
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--checkbox-label-gap: 0.25em 0.1em; |
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--section-header-text-size: 12pt; |
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--block-background-fill: transparent; |
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} |
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.block.padded:not(.gradio-accordion) { |
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padding: 0 !important; |
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} |
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div.gradio-container{ |
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max-width: unset !important; |
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} |
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.compact{ |
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background: transparent !important; |
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padding: 0 !important; |
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} |
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div.form{ |
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border-width: 0; |
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box-shadow: none; |
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background: transparent; |
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overflow: visible; |
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gap: 0.5em; |
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} |
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.block.gradio-dropdown, |
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.block.gradio-slider, |
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.block.gradio-checkbox, |
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.block.gradio-textbox, |
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.block.gradio-radio, |
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.block.gradio-checkboxgroup, |
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.block.gradio-number, |
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.block.gradio-colorpicker { |
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border-width: 0 !important; |
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box-shadow: none !important; |
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} |
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.gradio-dropdown label span:not(.has-info), |
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.gradio-textbox label span:not(.has-info), |
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.gradio-number label span:not(.has-info) |
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{ |
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margin-bottom: 0; |
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} |
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.gradio-dropdown ul.options{ |
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z-index: 3000; |
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min-width: fit-content; |
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max-width: inherit; |
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white-space: nowrap; |
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} |
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.gradio-dropdown ul.options li.item { |
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padding: 0.05em 0; |
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} |
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.gradio-dropdown ul.options li.item.selected { |
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background-color: var(--neutral-100); |
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} |
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.dark .gradio-dropdown ul.options li.item.selected { |
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background-color: var(--neutral-900); |
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} |
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.gradio-dropdown div.wrap.wrap.wrap.wrap{ |
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box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05); |
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} |
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.gradio-dropdown:not(.multiselect) .wrap-inner.wrap-inner.wrap-inner{ |
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flex-wrap: unset; |
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} |
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.gradio-dropdown .single-select{ |
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white-space: nowrap; |
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overflow: hidden; |
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} |
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.gradio-dropdown .token-remove.remove-all.remove-all{ |
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display: none; |
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} |
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.gradio-dropdown.multiselect .token-remove.remove-all.remove-all{ |
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display: flex; |
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} |
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.gradio-slider input[type="number"]{ |
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width: 6em; |
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} |
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.block.gradio-checkbox { |
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margin: 0.75em 1.5em 0 0; |
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} |
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.gradio-html div.wrap{ |
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height: 100%; |
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} |
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div.gradio-html.min{ |
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min-height: 0; |
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} |
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#model_dd { |
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width: 16%; |
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} |
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""" |
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|
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with gr.Blocks(css=css) as demo: |
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model = gr.Dropdown(interactive=True, value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True, |
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label="Stable Diffusion Checkpoint", choices=prodia_client.list_models(), elem_id="model_dd") |
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|
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with gr.Tabs() as tabs: |
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with gr.Tab("txt2img", id='t2i'): |
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with gr.Row(): |
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with gr.Column(scale=6, min_width=600): |
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prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", |
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placeholder="Prompt", show_label=False, lines=3) |
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negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, |
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value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly") |
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with gr.Row(): |
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text_button = gr.Button("Generate", variant='primary', elem_id="generate") |
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stop_btn = gr.Button("Cancel", variant="stop", elem_id="generate", visible=False) |
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|
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with gr.Row(): |
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with gr.Column(): |
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with gr.Tab("Generation"): |
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with gr.Row(): |
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with gr.Column(scale=1): |
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sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method", |
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choices=samplers) |
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|
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with gr.Column(scale=1): |
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steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=100, value=25, step=1) |
|
|
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with gr.Row(): |
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with gr.Column(scale=8): |
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width = gr.Slider(label="Width", maximum=1024, value=512, step=8) |
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height = gr.Slider(label="Height", maximum=1024, value=512, step=8) |
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|
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with gr.Column(scale=1): |
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batch_size = gr.Slider(label="Batch Size", maximum=1, value=1) |
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batch_count = gr.Slider(label="Batch Count", minimum=1, maximum=4, value=1, step=1) |
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|
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cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1) |
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seed = gr.Number(label="Seed", value=-1) |
|
|
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with gr.Tab("Lora"): |
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with gr.Row(): |
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for lora in lora_list: |
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lora_btn = gr.Button(lora, size="sm") |
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lora_btn.click(place_lora, inputs=[prompt, lora_btn], outputs=prompt) |
|
|
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with gr.Column(): |
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image_output = gr.Gallery(columns=3, |
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value=["https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png"]) |
|
|
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with gr.Tab("img2img", id='i2i'): |
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with gr.Row(): |
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with gr.Column(scale=6, min_width=600): |
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i2i_prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", |
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placeholder="Prompt", show_label=False, lines=3) |
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i2i_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, |
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value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly") |
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with gr.Row(): |
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i2i_text_button = gr.Button("Generate", variant='primary', elem_id="generate") |
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i2i_stop_btn = gr.Button("Cancel", variant="stop", elem_id="generate", visible=False) |
|
|
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with gr.Row(): |
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with gr.Column(scale=1): |
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with gr.Tab("Generation"): |
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i2i_image_input = gr.Image(type="pil") |
|
|
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with gr.Row(): |
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with gr.Column(scale=1): |
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i2i_sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, |
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label="Sampling Method", choices=samplers) |
|
|
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with gr.Column(scale=1): |
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i2i_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=100, value=25, step=1) |
|
|
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with gr.Row(): |
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with gr.Column(scale=6): |
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i2i_width = gr.Slider(label="Width", maximum=1024, value=512, step=8) |
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i2i_height = gr.Slider(label="Height", maximum=1024, value=512, step=8) |
|
|
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with gr.Column(scale=1): |
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i2i_batch_size = gr.Slider(label="Batch Size", maximum=1, value=1) |
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i2i_batch_count = gr.Slider(label="Batch Count", minimum=1, maximum=4, value=1, step=1) |
|
|
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i2i_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1) |
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i2i_denoising = gr.Slider(label="Denoising Strength", minimum=0, maximum=1, value=0.7, step=0.1) |
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i2i_seed = gr.Number(label="Seed", value=-1) |
|
|
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with gr.Tab("Lora"): |
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with gr.Row(): |
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for lora in lora_list: |
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lora_btn = gr.Button(lora, size="sm") |
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lora_btn.click(place_lora, inputs=[i2i_prompt, lora_btn], outputs=i2i_prompt) |
|
|
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with gr.Column(scale=1): |
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i2i_image_output = gr.Gallery(columns=3, |
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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(): |
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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") |
|
|
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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')) |
|
|
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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) |
|
|