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import numpy as np
import gradio as gr
import requests
import time
import json
import base64
import os
from PIL import Image
from io import BytesIO
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": 7,
"width": width,
"height": height,
"seed": -1
})
job = prodia_client.wait(result)
return job["imageUrl"]
css = """
#generate {
height: 100%;
}
"""
theme = "Base"
with gr.Blocks(css=css) as demo:
with gr.Row():
gr.Markdown("""
<h1><center>Stable Diffusion</center></h1>
""")
with gr.Row():
with gr.Column(scale=6, min_width=600):
prompt = gr.Textbox(label="Prompt", placeholder="beautiful cat, 8k", show_label=True, lines=2)
negative_prompt = gr.Textbox(label="Negative Prompt", value="text, blurry, fuzziness", placeholder="What you don't want to see in the image", show_label=True, lines=3)
with gr.Column():
text_button = gr.Button("Generate", variant='primary', elem_id="generate")
with gr.Row():
with gr.Column(scale=2):
image_output = gr.Image()
with gr.Accordion("Advanced options", open=False):
with gr.Column(scale=6):
model = gr.Dropdown(interactive=True,value="v1-5-pruned-emaonly.safetensors [d7049739]", show_label=True, label="Model", choices=prodia_client.list_models())
with gr.Column(scale=1):
sampler = gr.Dropdown(value="DPM++ SDE", show_label=True, label="Sampler", 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="Steps", minimum=1, maximum=50, value=30, 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)
text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output)
demo.queue(concurrency_count=10)
demo.launch()
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