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# This space used model: stabilityai/stable-diffusion-xl-base-1.0
# and model: stabilityai/stable-diffusion-xl-refiner-1.0
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
batch_size=1
batch_count=1
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}/sdxl/generate", 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}/sdxl/models")
return response.json()
def list_samplers(self):
response = self._get(f"{self.base}/sdxl/samplers")
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("API_KEY"))
def flip_text(prompt, negative_prompt, steps, cfg_scale, width, height, seed):
result = prodia_client.generate({
"prompt": prompt,
"negative_prompt": negative_prompt,
"model": "sd_xl_base_1.0.safetensors [be9edd61]",
"steps": steps,
"sampler": "DPM++ 2M Karras",
"cfg_scale": cfg_scale,
"width": width,
"height": height,
"seed": seed
})
job = prodia_client.wait(result)
return job["imageUrl"]
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
gr.HTML(value=""""<h1><center>Fast SDXL on <a href="https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0" target="_blank">stabilityai/stable-diffusion-xl-base-1.0</a>""")
with gr.Row():
with gr.Column(scale=6, min_width=600):
prompt = gr.Textbox(label="Prompt", placeholder="a cute cat, 8k", show_label=True, lines=1)
text_button = gr.Button("Generate", variant='primary', elem_id="generate")
with gr.Row():
with gr.Column(scale=1):
image_output = gr.Image()
with gr.Row():
with gr.Accordion("Additionals inputs", open=False):
with gr.Column(scale=1):
negative_prompt = gr.Textbox(label="Negative Prompt", value="text, blurry", placeholder="What you don't want to see in the image", show_label=True, lines=1)
with gr.Column(scale=1):
steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=30, value=25, step=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=1):
width = gr.Slider(label="↔️ Width", minimum=1024, maximum=1024, value=1024, step=8)
height = gr.Slider(label="↕️ Height", minimum=1024, maximum=1024, value=1024, step=8)
text_button.click(flip_text, inputs=[prompt, negative_prompt, steps, cfg_scale, width, height, seed], outputs=image_output)
demo.queue(concurrency_count=16, max_size=20, api_open=False).launch(max_threads=64)
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