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 batch_count = 1 batch_size = 1 i2i_batch_count = 1 i2i_batch_size = 1 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 list_samplers(self): response = self._get(f"{self.base}/sd/samplers") 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 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 prodia_client = Prodia(api_key=os.getenv("super_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 txt2img(prompt, negative_prompt, model, width, height): result = prodia_client.generate({ "prompt": prompt, "negative_prompt": negative_prompt, "model": model, "steps": 30, "sampler": "DPM++ SDE", "cfg_scale": 7, "width": width, "height": height, "seed": -1 }) job = prodia_client.wait(result) return job["imageUrl"] def img2img(input_image, prompt, negative_prompt, model, width, height): result = prodia_client.transform({ "imageData": image_to_base64(input_image), "denoising_strength": 0.7, "prompt": prompt, "negative_prompt": negative_prompt, "model": i2i_model, "steps": 30, "sampler": "DPM++ SDE", "cfg_scale": 7, "width": width, "height": height, "seed": -1 }) job = prodia_client.wait(result) return job["imageUrl"] css = """ #generate { height: 100%; } """ with gr.Blocks(css=css, theme="Base") as demo: gr.HTML(value="

🥏 DreamDrop

") with gr.Tabs() as tabs: with gr.Tab("Text to Image", id='t2i'): with gr.Row(): with gr.Column(scale=6, min_width=600): prompt = gr.Textbox(label="Prompt", placeholder="a cute cat, 8k", lines=2) negative_prompt = gr.Textbox(label="Negative Prompt", value="text, blurry, fuzziness", lines=1) with gr.Column(): text_button = gr.Button("Generate", variant='primary', elem_id="generate") with gr.Row(): with gr.Column(scale=2): image_output = gr.Image(label="Result Image") with gr.Row(): with gr.Accordion("⚙️ Settings", open=False): with gr.Column(scale=1): model = gr.Dropdown(interactive=True, value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True, label="Model", choices=prodia_client.list_models()) width = gr.Slider(label="↔️ Width", maximum=1024, value=768, step=8) height = gr.Slider(label="↕️ Height", maximum=1024, value=768, step=8) text_button.click(txt2img, inputs=[prompt, negative_prompt, model, width, height], outputs=image_output) with gr.Tab("Image to Image", id='i2i'): with gr.Row(): with gr.Column(scale=6, min_width=600): i2i_prompt = gr.Textbox(label="Prompt", placeholder="a cute cat, 8k", lines=3) i2i_negative_prompt = gr.Textbox(label="Negative Prompt", lines=2, value="text, blurry, fuzziness") with gr.Column(): i2i_text_button = gr.Button("Generate", variant='primary', elem_id="generate") with gr.Row(): with gr.Column(scale=3): i2i_image_input = gr.Image(label="Input Image", type="pil") with gr.Column(scale=2): i2i_image_output = gr.Image(label="Result Image") with gr.Row(): with gr.Accordion("⚙️ Settings", open=False): with gr.Column(scale=1): i2i_model = gr.Dropdown(interactive=True, value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True, label="Model", choices=prodia_client.list_models()) with gr.Column(scale=1): i2i_width = gr.Slider(label="↔️ Width", maximum=1024, value=768, step=8) i2i_height = gr.Slider(label="↕️ Height", maximum=1024, value=768, step=8) i2i_text_button.click(img2img, inputs=[i2i_image_input, i2i_prompt, i2i_negative_prompt, model, i2i_width, i2i_height], outputs=i2i_image_output) demo.queue(concurrency_count=64, max_size=30, api_open=False).launch(max_threads=256, show_api=False)