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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 html |
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import re |
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from deep_translator import GoogleTranslator |
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from langdetect import detect |
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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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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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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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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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while job_result['status'] not in ['succeeded', 'failed']: |
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time.sleep(0.25) |
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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_samplers(self): |
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response = self._get(f"{self.base}/sd/samplers") |
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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="WEBP") |
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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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prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY")) |
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model_list = prodia_client.list_models() |
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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): |
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language = detect(prompt) |
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if language == 'ru': |
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prompt = GoogleTranslator(source='ru', target='en').translate(prompt) |
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print(prompt) |
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result = prodia_client.generate({ |
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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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job = prodia_client.wait(result) |
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return job["imageUrl"] |
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def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed): |
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result = prodia_client.transform({ |
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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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job = prodia_client.wait(result) |
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return job["imageUrl"] |
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css = """ |
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footer {visibility: hidden !important;} |
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#container{ |
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margin: 0 auto; |
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max-width: 40rem; |
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} |
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#intro{ |
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max-width: 100%; |
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text-align: center; |
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margin: 0 auto; |
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} |
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div.svelte-vt1mxs { |
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display: flex; |
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position: relative; |
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flex-direction: column |
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} |
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div.svelte-vt1mxs>*,div.svelte-vt1mxs>.form > * { |
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width: var(--size-full) |
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} |
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.gap.svelte-vt1mxs { |
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gap: var(--layout-gap) |
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} |
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.hide.svelte-vt1mxs { |
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display: none |
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} |
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.compact.svelte-vt1mxs>*,.compact.svelte-vt1mxs .box { |
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border-radius: 0 |
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} |
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.compact.svelte-vt1mxs,.panel.svelte-vt1mxs { |
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border: solid var(--panel-border-width) var(--panel-border-color); |
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border-radius: var(--container-radius); |
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background: var(--panel-background-fill); |
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padding: var(--spacing-lg) |
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} |
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div#component-24 { |
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display: none; |
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} |
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div#component-8 {background: #00000024;border: 0;color: #ffffff;backdrop-filter: blur(20px);-webkit-backdrop-filter: blur(20px);border-width: 0 !important;} |
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span.md.svelte-9tftx4 { |
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display: none; |
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} |
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.empty.svelte-lk9eg8.large.unpadded_box { |
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background: none !important; |
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} |
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div#component-26 { |
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display: none; |
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} |
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div#component-7 { |
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background: none; |
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} |
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.wrap.default.full.svelte-119qaqt.hide { |
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background: none !important; |
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} |
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.styler.svelte-iyf88w { |
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background: none !important; |
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} |
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div#component-3 { |
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background: none !important; |
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border: 0; |
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} |
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input.scroll-hide.svelte-1f354aw { |
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overflow: hidden !important; |
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} |
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div#component-5 { |
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border-radius: 40px 0px 0px 40px; |
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background: black !important; |
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opacity: 0.9; |
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} |
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#component-6 { |
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border-radius: 0px 40px 40px 0px; |
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background: linear-gradient(358deg, #ff4d0080, #fff0); |
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color: #ffffffe3; |
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border: 2px #ffffffc2 dashed; |
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border-left: 0; |
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font-size: 30px; |
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letter-spacing:-1px; |
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position: relative; |
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z-index: 1; |
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backdrop-filter: blur(18px); |
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-webkit-backdrop-filter: blur(18px); |
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} |
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div#component-0 { |
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max-width: 100% !important; |
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} |
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.grid-wrap.svelte-1b19cri.fixed-height { |
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max-height: 100% !important; |
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overflow: auto; |
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} |
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footer.svelte-1ax1toq { |
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display: none !important; |
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} |
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input.scroll-hide.svelte-1f354aw { |
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font-size: 26px; |
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padding: 25px; |
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} |
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div#component-4 { |
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margin-top: 230px; |
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margin-bottom: 30px; |
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} |
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gradio-app { |
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background-color: transparent !important; |
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background: url(https://vivawaves.com/wavesweaveslogo.svg) top center no-repeat !important; |
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margin-top: 260px; |
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} |
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label.svelte-1f354aw { |
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} |
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.styler.svelte-iyf88w { |
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} |
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body { |
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background: url(https://vivawaves.com/vivatodaybg2.jpg); |
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background-size: cover; |
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} |
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img.svelte-1b19cri {} |
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.preview.svelte-1b19cri { |
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background: #0000004d !important; |
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border-radius: 20px; |
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padding: 20px; |
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} |
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button.svelte-1030q2h { |
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border-radius: 100%; |
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} |
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div.svelte-1030q2h svg { |
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} |
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svg path { |
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} |
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img.svelte-1b19cri { |
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border-radius: 10px; |
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} |
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.form.svelte-sfqy0y { |
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background: #fff0; |
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border-width: 0px; |
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opacity: 0.8; |
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} |
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.gradio-container-3-44-2,.gradio-container-3-44-2 *,.gradio-container-3-44-2 :before,.gradio-container-3-44-2 :after { |
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box-sizing: border-box; |
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border-width: 0; |
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border-style: solid; |
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} |
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div#component-13 { |
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display: none; |
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} |
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footer.svelte-mpyp5e { |
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} |
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div#intro { |
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display: none; |
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} |
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div.svelte-15lo0d8 { |
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display: flex; |
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|
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flex-wrap: wrap; |
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gap: 0; |
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width: var(--size-full); |
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|
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flex-direction: initial; |
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justify-content: center; |
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|
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align-items: baseline; |
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} |
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input.svelte-1f354aw.svelte-1f354aw, textarea.svelte-1f354aw.svelte-1f354aw { |
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|
|
|
|
|
|
|
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position: relative; |
|
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|
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outline: none !important; |
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|
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box-shadow: var(--input-shadow); |
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|
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background: var(--input-background-fill); |
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|
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padding: var(--input-padding); |
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|
|
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width: 100%; |
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|
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color: var(--body-text-color); |
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|
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font-weight: var(--input-text-weight); |
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|
|
|
font-size: large; |
|
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|
|
|
line-height: initial; |
|
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|
|
|
border: none; |
|
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|
|
|
text-size-adjust: auto; |
|
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|
|
|
font-size: 23px !important; |
|
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|
|
|
} |
|
|
|
|
|
div#component-24 { |
|
|
|
|
|
display: none; |
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|
|
|
} |
|
|
|
|
|
div#component-8 {background: #00000024;border: 0;color: #ffffff;backdrop-filter: blur(20px);-webkit-backdrop-filter: blur(20px);border-width: 0 !important;} |
|
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|
|
|
span.md.svelte-9tftx4 { |
|
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|
|
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display: none; |
|
|
|
|
|
} |
|
|
|
|
|
.empty.svelte-lk9eg8.large.unpadded_box { |
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|
|
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background: none !important; |
|
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|
|
|
} |
|
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div#component-26 { |
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|
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display: none; |
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|
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} |
|
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|
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|
div#component-7 { |
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|
|
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background: none; |
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|
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} |
|
|
|
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.wrap.default.full.svelte-119qaqt.hide { |
|
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|
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background: none !important; |
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|
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} |
|
|
|
|
|
.styler.svelte-iyf88w { |
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|
|
|
|
background: none !important; |
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|
|
|
|
} |
|
|
|
|
|
div#component-3 { |
|
|
|
|
|
background: none !important; |
|
|
|
|
|
border: 0; |
|
|
|
|
|
} |
|
|
|
|
|
div#component-9 { |
|
|
border: 0 !important; |
|
|
} |
|
|
|
|
|
|
|
|
input.scroll-hide.svelte-1f354aw { |
|
|
|
|
|
overflow: hidden !important; |
|
|
|
|
|
} |
|
|
|
|
|
div#component-5 { |
|
|
|
|
|
border-radius: 40px; |
|
|
|
|
|
background: transparent !important; |
|
|
|
|
|
opacity: 1; |
|
|
|
|
|
} |
|
|
|
|
|
#component-6 { |
|
|
border-radius: 40px; |
|
|
background: #d7661500; |
|
|
border: none; |
|
|
border-left: 0; |
|
|
font-size: 30px; |
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letter-spacing: -1px; |
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position: relative; |
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z-index: 1; |
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backdrop-filter: none; |
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-webkit-backdrop-filter: none; |
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display: block; |
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|
} |
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div#component-0 { |
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max-width: 100% !important; |
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|
} |
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|
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|
.grid-wrap.svelte-1b19cri.fixed-height { |
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|
max-height: 100% !important; |
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|
overflow: auto; |
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|
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|
} |
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|
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|
footer.svelte-1ax1toq { |
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|
display: none !important; |
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|
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|
} |
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|
input.scroll-hide.svelte-1f354aw { |
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|
font-size: 26px; |
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|
padding: 25px; |
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|
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|
} |
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|
div#component-4 { |
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|
margin-top: 230px; |
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|
margin-bottom: 30px; |
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|
} |
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|
gradio-app { |
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|
background-color: transparent !important; |
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|
background: url(https://vivawaves.com/wavesweaveslogo.svg) top center no-repeat !important; |
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|
margin-top: 77px; |
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|
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|
|
} |
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|
|
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|
label.svelte-1f354aw { |
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|
} |
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|
.styler.svelte-iyf88w { |
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|
} |
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|
body { |
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|
background: url(https://vivawaves.com/vivatodaybg2.jpg); |
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|
background-size: cover; |
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|
} |
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|
img.svelte-1b19cri {} |
|
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|
.preview.svelte-1b19cri { |
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|
background: #0000004d !important; |
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|
border-radius: 20px; |
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|
padding: 20px; |
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|
|
|
overflow: hidden; |
|
|
|
|
|
} |
|
|
|
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|
button.svelte-1030q2h { |
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|
border-radius: 100%; |
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|
} |
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|
div.svelte-1030q2h svg { |
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|
} |
|
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|
svg path { |
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|
} |
|
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|
img.svelte-1b19cri { |
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|
|
border-radius: 10px; |
|
|
|
|
|
} |
|
|
|
|
|
.form.svelte-sfqy0y { |
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|
|
background: #fff0; |
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|
|
border-width: 0px; |
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|
opacity: 0.8; |
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|
|
} |
|
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|
.gradio-container-3-44-2,.gradio-container-3-44-2 *,.gradio-container-3-44-2 :before,.gradio-container-3-44-2 :after { |
|
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|
|
box-sizing: border-box; |
|
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|
|
border-width: 0; |
|
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|
|
border-style: solid; |
|
|
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|
|
} |
|
|
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|
|
div#component-13 { |
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|
|
|
|
display: none; |
|
|
|
|
|
} |
|
|
|
|
|
footer.svelte-mpyp5e { |
|
|
|
|
|
display: none !important; |
|
|
|
|
|
} |
|
|
|
|
|
div#intro { |
|
|
|
|
|
display: none; |
|
|
|
|
|
} |
|
|
|
|
|
div.svelte-15lo0d8 { |
|
|
|
|
|
display: flex; |
|
|
|
|
|
flex-wrap: wrap; |
|
|
|
|
|
gap: 0 !important; |
|
|
|
|
|
width: var(--size-full); |
|
|
|
|
|
flex-direction: initial; |
|
|
|
|
|
justify-content: center; |
|
|
|
|
|
align-items: baseline; |
|
|
|
|
|
} |
|
|
|
|
|
input.svelte-1f354aw.svelte-1f354aw, textarea.svelte-1f354aw.svelte-1f354aw { |
|
|
|
|
|
display: block; |
|
|
|
|
|
position: relative; |
|
|
|
|
|
outline: none !important; |
|
|
|
|
|
box-shadow: var(--input-shadow); |
|
|
|
|
|
background: var(--input-background-fill); |
|
|
|
|
|
padding: var(--input-padding); |
|
|
|
|
|
width: 100%; |
|
|
|
|
|
color: var(--body-text-color); |
|
|
|
|
|
font-weight: var(--input-text-weight); |
|
|
|
|
|
font-size: large; |
|
|
|
|
|
line-height: initial; |
|
|
|
|
|
border: none; |
|
|
|
|
|
text-size-adjust: auto; |
|
|
|
|
|
font-size: 23px !important; |
|
|
|
|
|
border-radius: 30px; |
|
|
|
|
|
background: white !important; |
|
|
|
|
|
text-align: center; |
|
|
|
|
|
} |
|
|
|
|
|
div#component-8 { |
|
|
|
|
|
margin-bottom: 70px; |
|
|
margin-top: 210px; |
|
|
|
|
|
} |
|
|
div#component-15 {display: none;} |
|
|
|
|
|
div#component-18 { |
|
|
display: none; |
|
|
} |
|
|
|
|
|
div#component-1 { |
|
|
display: none; |
|
|
} |
|
|
|
|
|
button.selected.svelte-kqij2n { |
|
|
display: none; |
|
|
} |
|
|
|
|
|
button.svelte-kqij2n { |
|
|
display: none; |
|
|
} |
|
|
|
|
|
.tab-nav.scroll-hide.svelte-kqij2n { |
|
|
display: none; |
|
|
} |
|
|
|
|
|
.svelte-vt1mxs.gap { |
|
|
border-radius: 20px; |
|
|
} |
|
|
|
|
|
div#component-6 {padding: 26px;} |
|
|
|
|
|
button#generate { |
|
|
background: #eb7623; |
|
|
border-radius: 40px; |
|
|
padding: 16px; |
|
|
color: #FFF; |
|
|
FONT-SIZE: large; |
|
|
border: 2px solid #ff7600; |
|
|
border-top: 0px solid; |
|
|
box-shadow: 0px 12px 10px -10px #ff7600; |
|
|
} |
|
|
""" |
|
|
|
|
|
with gr.Blocks(css=css) as demo: |
|
|
with gr.Row(): |
|
|
with gr.Accordion(label="Models", open=False): |
|
|
model = gr.Radio(interactive=True, value="epicrealism_naturalSinRC1VAE.safetensors [90a4c676]", show_label=True, choices=prodia_client.list_models()) |
|
|
|
|
|
with gr.Tabs() as tabs: |
|
|
|
|
|
with gr.Tab("txt2img", id='t2i'): |
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Column(scale=3): |
|
|
with gr.Tab("Основные настройки"): |
|
|
with gr.Column(scale=6, min_width=600): |
|
|
prompt = gr.Textbox("", placeholder="Take a deep breath and take your time describing your weave... Be as vague or specific as you want. 💜✨ ", show_label=False, lines=3) |
|
|
negative_prompt = gr.Textbox(placeholder="Here you can describe anything that you would like NOT to see. ", show_label=False, lines=1, value="") |
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Column(scale=1): |
|
|
steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=50, value=40, step=1) |
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Column(scale=1): |
|
|
width = gr.Slider(label="Ширина", minimum=15, maximum=1024, value=1024, step=8) |
|
|
height = gr.Slider(label="Длина", minimum=15, maximum=1024, value=1024, step=8) |
|
|
|
|
|
with gr.Tab("Расширенные настройки"): |
|
|
with gr.Row(): |
|
|
with gr.Column(scale=1): |
|
|
sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers()) |
|
|
|
|
|
cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1) |
|
|
seed = gr.Slider(label="Seed", minimum=-1, maximum=10000000, value=-1) |
|
|
text_button = gr.Button("Weave", variant='primary', elem_id="generate") |
|
|
image_output = gr.Image() |
|
|
|
|
|
text_button.click(txt2img, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output) |
|
|
|
|
|
with gr.Tab("img2img", id='i2i'): |
|
|
with gr.Row(): |
|
|
with gr.Column(scale=3): |
|
|
with gr.Tab("Основные настройки"): |
|
|
i2i_image_input = gr.Image(type="pil") |
|
|
with gr.Column(scale=6, min_width=600): |
|
|
i2i_prompt = gr.Textbox("", placeholder="Prompt", show_label=False, lines=3) |
|
|
i2i_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry") |
|
|
|
|
|
with gr.Row(): |
|
|
|
|
|
with gr.Column(scale=1): |
|
|
i2i_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=50, value=30, step=1) |
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Column(scale=1): |
|
|
i2i_width = gr.Slider(label="Ширина", minimum=15, maximum=1024, value=512, step=8) |
|
|
i2i_height = gr.Slider(label="Высота", minimum=15, maximum=1024, value=512, step=8) |
|
|
|
|
|
|
|
|
with gr.Tab("Расширенные настройки"): |
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Column(scale=1): |
|
|
i2i_sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers()) |
|
|
|
|
|
|
|
|
i2i_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1) |
|
|
i2i_denoising = gr.Slider(label="Схожесть с оригиналом", minimum=0, maximum=1, value=0.7, step=0.1) |
|
|
i2i_seed = gr.Slider(label="Seed", minimum=-1, maximum=10000000, value=-1) |
|
|
|
|
|
|
|
|
i2i_text_button = gr.Button("Генерация", variant='primary', elem_id="generate") |
|
|
i2i_image_output = gr.Image() |
|
|
|
|
|
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], outputs=i2i_image_output) |
|
|
demo.queue(concurrency_count=64, max_size=80, api_open=False).launch(max_threads=256) |
|
|
|
