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CPU Upgrade
| 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 | |
| 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}/models/list") | |
| 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_path): | |
| # Open the image with PIL | |
| with Image.open(image_path) as 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 | |
| prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY")) | |
| def flip_text(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed): | |
| 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": seed | |
| }) | |
| job = prodia_client.wait(result) | |
| return job["imageUrl"] | |
| def get_exif_data(image): | |
| print(image) | |
| exif = image.getexif() | |
| print(exif) | |
| return image.info | |
| css = """ | |
| #generate { | |
| height: 100%; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Row(): | |
| with gr.Column(scale=6): | |
| model = gr.Dropdown(interactive=True,value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True, label="Stable Diffusion Checkpoint", choices=prodia_client.list_models()) | |
| with gr.Column(scale=1): | |
| gr.Markdown(elem_id="powered-by-prodia", value="AUTOMATIC1111 Stable Diffusion Web UI.<br>Powered by [Prodia](https://prodia.com).") | |
| with gr.Tab("txt2img"): | |
| with gr.Row(): | |
| with gr.Column(scale=6, min_width=600): | |
| prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3) | |
| negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly") | |
| with gr.Column(): | |
| text_button = gr.Button("Generate", variant='primary', elem_id="generate") | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| with gr.Tab("Generation"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| sampler = gr.Dropdown(value="Euler a", show_label=True, label="Sampling Method", choices=[ | |
| "Euler", | |
| "Euler a", | |
| "LMS", | |
| "Heun", | |
| "DPM2", | |
| "DPM2 a", | |
| "DPM++ 2S a", | |
| "DPM++ 2M", | |
| "DPM++ SDE", | |
| "DPM fast", | |
| "DPM adaptive", | |
| "LMS Karras", | |
| "DPM2 Karras", | |
| "DPM2 a Karras", | |
| "DPM++ 2S a Karras", | |
| "DPM++ 2M Karras", | |
| "DPM++ SDE Karras", | |
| "DDIM", | |
| "PLMS", | |
| ]) | |
| with gr.Column(scale=1): | |
| steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=30, value=25, step=1) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| width = gr.Slider(label="Width", maximum=1024, value=512, step=8) | |
| height = gr.Slider(label="Height", maximum=1024, value=512, step=8) | |
| with gr.Column(scale=1): | |
| batch_size = gr.Slider(label="Batch Size", maximum=1, value=1) | |
| batch_count = gr.Slider(label="Batch Count", maximum=1, value=1) | |
| cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1) | |
| seed = gr.Number(label="Seed", value=-1) | |
| with gr.Column(scale=2): | |
| image_output = gr.Image(value="https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png") | |
| text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output) | |
| with gr.Tab("PNG Info"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_input = gr.Image(type="pil") | |
| with gr.Column(): | |
| exif_button = gr.Button("Get input data") | |
| exif_output = gr.Textbox(label="EXIF Data") | |
| exif_button.click(get_exif_data, inputs=[image_input], outputs=exif_output) | |
| demo.queue(concurrency_count=24) | |
| demo.launch() | |