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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() | |