| """ | |
| 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=1, 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 = "<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(): | |
| 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) | |