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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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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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print(params) |
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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="PNG") |
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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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def send_to_txt2img(image): |
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result = {tabs: gr.update(selected="t2i")} |
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try: |
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text = image.info['parameters'] |
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data = get_data(text) |
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result[prompt] = gr.update(value=data['prompt']) |
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result[negative_prompt] = gr.update(value=data['negative_prompt']) if data['negative_prompt'] is not None else gr.update() |
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result[steps] = gr.update(value=int(data['steps'])) if data['steps'] is not None else gr.update() |
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result[seed] = gr.update(value=int(data['seed'])) if data['seed'] is not None else gr.update() |
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result[cfg_scale] = gr.update(value=float(data['cfg_scale'])) if data['cfg_scale'] is not None else gr.update() |
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result[width] = gr.update(value=int(data['w'])) if data['w'] is not None else gr.update() |
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result[height] = gr.update(value=int(data['h'])) if data['h'] is not None else gr.update() |
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result[sampler] = gr.update(value=data['sampler']) if data['sampler'] is not None else gr.update() |
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if model in model_names: |
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result[model] = gr.update(value=model_names[model]) |
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else: |
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result[model] = gr.update() |
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return result |
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except Exception as e: |
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print(e) |
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return result |
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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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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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if job['status'] != "succeeded": |
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raise gr.Error("job failed") |
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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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if input_image is None: |
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raise gr.Error("Please add an image to run img2img") |
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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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if job['status'] != "succeeded": |
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raise gr.Error("job failed") |
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return job["imageUrl"] |
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css = """ |
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#generate { |
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height: 100%; |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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with gr.Row(): |
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with gr.Column(scale=6): |
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model = gr.Dropdown(interactive=True,value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True, label="Stable Diffusion Checkpoint", choices=prodia_client.list_models()) |
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with gr.Column(scale=1): |
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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 generation times check out our [API Docs](https://docs.prodia.com/reference/getting-started-guide).") |
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with gr.Tabs() as tabs: |
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with gr.Tab("txt2img", id='t2i'): |
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with gr.Row(): |
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with gr.Column(scale=6, min_width=600): |
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prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3) |
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negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly") |
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with gr.Column(): |
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text_button = gr.Button("Generate", variant='primary', elem_id="generate") |
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with gr.Row(): |
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with gr.Column(scale=3): |
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with gr.Tab("Generation"): |
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with gr.Row(): |
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with gr.Column(scale=1): |
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sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers()) |
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with gr.Column(scale=1): |
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steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1) |
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with gr.Row(): |
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with gr.Column(scale=1): |
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width = gr.Slider(label="Width", maximum=1024, minimum=128, value=512, step=8) |
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height = gr.Slider(label="Height", maximum=1024, minimum=128, value=512, step=8) |
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with gr.Column(scale=1): |
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batch_size = gr.Slider(label="Batch Size", maximum=1, value=1) |
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batch_count = gr.Slider(label="Batch Count", maximum=1, value=1) |
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cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1) |
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seed = gr.Number(label="Seed", value=-1) |
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with gr.Column(scale=2): |
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image_output = gr.Image(value="https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png") |
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text_button.click(txt2img, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, |
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seed], outputs=image_output, concurrency_limit=64) |
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with gr.Tab("img2img", id='i2i'): |
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with gr.Row(): |
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with gr.Column(scale=6, min_width=600): |
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i2i_prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3) |
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i2i_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly") |
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with gr.Column(): |
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i2i_text_button = gr.Button("Generate", variant='primary', elem_id="generate") |
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with gr.Row(): |
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with gr.Column(scale=3): |
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with gr.Tab("Generation"): |
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i2i_image_input = gr.Image(type="pil") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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i2i_sampler = gr.Dropdown(value="Euler a", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers()) |
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with gr.Column(scale=1): |
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i2i_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1) |
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with gr.Row(): |
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with gr.Column(scale=1): |
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i2i_width = gr.Slider(label="Width", maximum=1024, value=512, step=8) |
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i2i_height = gr.Slider(label="Height", maximum=1024, value=512, step=8) |
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with gr.Column(scale=1): |
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i2i_batch_size = gr.Slider(label="Batch Size", maximum=1, value=1) |
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i2i_batch_count = gr.Slider(label="Batch Count", maximum=1, value=1) |
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i2i_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1) |
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i2i_denoising = gr.Slider(label="Denoising Strength", minimum=0, maximum=1, value=0.7, step=0.1) |
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i2i_seed = gr.Number(label="Seed", value=-1) |
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with gr.Column(scale=2): |
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i2i_image_output = gr.Image(value="https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png") |
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i2i_text_button.click(img2img, inputs=[i2i_image_input, i2i_denoising, i2i_prompt, i2i_negative_prompt, |
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model, i2i_steps, i2i_sampler, i2i_cfg_scale, i2i_width, i2i_height, |
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i2i_seed], outputs=i2i_image_output, concurrency_limit=64) |
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with gr.Tab("PNG Info"): |
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def plaintext_to_html(text, classname=None): |
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content = "<br>\n".join(html.escape(x) for x in text.split('\n')) |
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return f"<p class='{classname}'>{content}</p>" if classname else f"<p>{content}</p>" |
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def get_exif_data(image): |
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items = image.info |
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info = '' |
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for key, text in items.items(): |
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info += f""" |
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<div> |
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<p><b>{plaintext_to_html(str(key))}</b></p> |
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<p>{plaintext_to_html(str(text))}</p> |
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</div> |
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""".strip()+"\n" |
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if len(info) == 0: |
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message = "Nothing found in the image." |
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info = f"<div><p>{message}<p></div>" |
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return info |
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with gr.Row(): |
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with gr.Column(): |
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image_input = gr.Image(type="pil") |
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with gr.Column(): |
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exif_output = gr.HTML(label="EXIF Data") |
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send_to_txt2img_btn = gr.Button("Send to txt2img") |
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image_input.upload(get_exif_data, inputs=[image_input], outputs=exif_output) |
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send_to_txt2img_btn.click(send_to_txt2img, inputs=[image_input], outputs=[tabs, prompt, negative_prompt, |
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steps, seed, model, sampler, |
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width, height, cfg_scale], |
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concurrency_limit=32) |
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demo.queue(max_size=20, api_open=False).launch(max_threads=40, show_api=False) |
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