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| import numpy as np | |
| import gradio as gr | |
| import requests | |
| import time | |
| import json | |
| import base64 | |
| import os | |
| from io import BytesIO | |
| import math | |
| import PIL | |
| from PIL import Image | |
| 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 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(0.25) | |
| 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 _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_path): | |
| # Open the image with PIL | |
| with Image.open(image_path) as 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 create_grid(image_urls): | |
| # Download first image to get size | |
| response = requests.get(image_urls[0]) | |
| img_data = response.content | |
| img = Image.open(BytesIO(img_data)) | |
| w, h = img.size | |
| # Calculate rows and cols | |
| num_images = len(image_urls) | |
| num_cols = min(num_images, 3) | |
| num_rows = math.ceil(num_images / num_cols) | |
| # Create new rgba image | |
| grid_w = num_cols * w | |
| grid_h = num_rows * h | |
| grid = Image.new('RGBA', (grid_w, grid_h), (0, 0, 0, 0)) | |
| # Download images and paste into grid | |
| for index, img_url in enumerate(image_urls): | |
| response = requests.get(img_url) | |
| img_data = response.content | |
| img = Image.open(BytesIO(img_data)) | |
| row = index // num_cols | |
| col = index % num_cols | |
| grid.paste(img, (col * w, row * h)) | |
| # Save image | |
| return grid | |
| 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 | |
| prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY")) | |
| model_list = prodia_client.list_models() | |
| 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 flip_text(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, batch_size, batch_count, gallery): | |
| 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 = [] | |
| count_threads = [] | |
| def generate_one_grid(): | |
| grid_images = [] | |
| size_threads = [] | |
| def generate_one_image(): | |
| result = prodia_client.generate(data) | |
| job = prodia_client.wait(result) | |
| grid_images.append(job['imageUrl']) | |
| for y in range(batch_size): | |
| t = Thread(target=generate_one_image) | |
| size_threads.append(t) | |
| t.start() | |
| for t in size_threads: | |
| t.join() | |
| total_images.append(create_grid(grid_images)) | |
| for x in range(batch_count): | |
| t = Thread(target=generate_one_grid) | |
| count_threads.append(t) | |
| t.start() | |
| for t in count_threads: | |
| t.join() | |
| new_images_list = [img['name'] for img in gallery] | |
| for image in total_images: | |
| new_images_list.insert(0, image) | |
| return {image_output: total_images, gallery_obj: new_images_list} | |
| css = """ | |
| #generate { | |
| height: 100%; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Row(): | |
| with gr.Column(scale=6): | |
| model = gr.Dropdown(interactive=True, value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True, | |
| label="Stable Diffusion Checkpoint", choices=prodia_client.list_models()) | |
| with gr.Column(scale=1): | |
| gr.Markdown(elem_id="powered-by-prodia", | |
| value="AUTOMATIC1111 Stable Diffusion Web UI.<br>Powered by [Prodia](https://prodia.com).<br> For more features and faster gen times check out our [API Docs](https://docs.prodia.com/reference/getting-started-guide)") | |
| 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.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(value="Euler a", show_label=True, label="Sampling Method", | |
| choices=[ | |
| "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", | |
| ]) | |
| 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", minimum=1, maximum=9, value=1, step=1) | |
| batch_count = gr.Slider(label="Batch Count", minimum=1, maximum=100, 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.Column(scale=2): | |
| image_output = gr.Gallery(value=["https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png"], preview=True) | |
| 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=5) | |
| text_button.click(flip_text, | |
| inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, batch_size, batch_count, | |
| gallery_obj], outputs=[image_output, gallery_obj]) | |
| 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]) | |
| demo.queue(concurrency_count=32) | |
| demo.launch() |