""" This is NEW release of DreamDrop V2.0! Features added: 1. Can generate up to 10 images at a time 2. Image Upscaler (x8) appeared 3. Integrated MagicPrompt (for Stable Diffusion and for Dall•E) 4. Added generation parameters menu (Steps, Samplers and CFG Sсale) Enjoy! """ 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 MagicPrompt import MagicPromptSD from Upscaler import upscale_image 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.environ.get("API_X_KEY")) # You can get the API key on https://docs.prodia.com/reference/getting-started-guide 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, sampler, steps, cfg_scale, width, height, num_images): generated_images = [] for _ in range(num_images): result = prodia_client.generate({ "prompt": prompt, "negative_prompt": negative_prompt, "model": model, "steps": steps, "sampler": sampler, "cfg_scale": cfg_scale, "width": width, "height": height, "seed": -1 }) job = prodia_client.wait(result) generated_images.append(job["imageUrl"]) return generated_images def img2img(input_image, denoising, prompt, negative_prompt, model, sampler, steps, cfg_scale, i2i_width, i2i_height): result = prodia_client.transform({ "imageData": image_to_base64(input_image), "denoising_strength": denoising, "prompt": prompt, "negative_prompt": negative_prompt, "model": i2i_model.value, "steps": steps, "sampler": sampler, "cfg_scale": cfg_scale, "width": i2i_width, "height": i2i_height, "seed": -1 }) job = prodia_client.wait(result) return job["imageUrl"] with gr.Blocks(css="style.css", theme="zenafey/prodia-web") as demo: gr.Markdown(""" # 🥏 DreamDrop ```V2.0``` """) 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) text_button = gr.Button("Generate", variant='primary') with gr.Row(): with gr.Column(scale=5): images_output = gr.Gallery(label="Result Image(s)", num_rows=1, num_cols=5, scale=1, allow_preview=True, preview=True) 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()) with gr.Column(scale=1): sampler = gr.Dropdown(label="Sampler", choices=prodia_client.list_samplers(), value="DPM++ SDE", interactive=True) steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=25, interactive=True) cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, interactive=True) width = gr.Slider(label="↔️ Width", maximum=1024, value=768, step=8) height = gr.Slider(label="↕️ Height", maximum=1024, value=768, step=8) num_images = gr.Slider(minimum=1, maximum=10, value=2, step=1, label="Image Count", interactive=True) text_button.click(txt2img, inputs=[prompt, negative_prompt, model, sampler, steps, cfg_scale, width, height, num_images], outputs=images_output) with gr.Tab("Image-to-Image", id='i2i'): with gr.Row(): with gr.Column(scale=6): with gr.Column(scale=1): i2i_image_input = gr.Image(label="Input Image", type="pil", interactive=True) with gr.Column(scale=6, min_width=600): i2i_prompt = gr.Textbox(label="Prompt", placeholder="a cute cat, 8k", lines=2) i2i_negative_prompt = gr.Textbox(label="Negative Prompt", lines=1, value="text, blurry, fuzziness") with gr.Column(): i2i_text_button = gr.Button("Generate", variant='primary', elem_id="generate") with gr.Column(scale=1): i2i_image_output = gr.Image(label="Result Image(s)") 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_denoising = gr.Slider(label="Denoising Strength", minimum=0, maximum=1, value=0.7, step=0.1) sampler = gr.Dropdown(label="Sampler", choices=prodia_client.list_samplers(), value="DPM++ SDE", interactive=True) steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=25, interactive=True) cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, interactive=True) 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_denoising, i2i_prompt, i2i_negative_prompt, model, sampler, steps, cfg_scale, i2i_width, i2i_height], outputs=i2i_image_output) with gr.Tab("Upscaler"): gr.Markdown(""" # Upscaler ```x8``` """) radio_input = gr.Radio(label="Upscale Levels", choices=[2, 4, 6, 8], value=2) gr.Interface(fn=upscale_image, inputs = [gr.Image(label="Input Image", interactive=True), radio_input], outputs = gr.Image(label="Upscaled Image")) with gr.Tab("PNG-Info"): def plaintext_to_html(text, classname=None): content = "
\n".join(html.escape(x) for x in text.split('\n')) return f"

{content}

" if classname else f"

{content}

" def get_exif_data(image): items = image.info info = '' for key, text in items.items(): info += f"""

{plaintext_to_html(str(key))}

{plaintext_to_html(str(text))}

""".strip()+"\n" if len(info) == 0: message = "Nothing found in the image." info = f"

{message}

" return info with gr.Row(): gr.Markdown(""" # PNG-Info """) with gr.Column(): image_input = gr.Image(type="pil", label="Input Image", interactive=True) with gr.Column(): exif_output = gr.HTML(label="EXIF Data") image_input.upload(get_exif_data, inputs=[image_input], outputs=exif_output) with gr.Tab("MagicPrompt"): gr.Markdown(""" # MagicPrompt """) gr.Interface(fn=MagicPromptSD, inputs=[gr.Radio(label="Prompt Model", choices=["Gustavosta/MagicPrompt-Stable-Diffusion", "Gustavosta/MagicPrompt-Dalle"], value="Gustavosta/MagicPrompt-Stable-Diffusion"), gr.Textbox(label="Enter your idea")], outputs=gr.Textbox(label="Output Prompt", interactive=False), allow_flagging='never') demo.launch(show_api=False)