Create app.py

app.py

@@ -0,0 +1,176 @@
+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": cfg_scale,
+ "width": width,
+ "height": height,
+ "seed": seed
+ })
+
+ job = prodia_client.wait(result)
+
+ return job["imageUrl"]
+
+css = """
+#generate {
+ height: 100%;
+}
+"""
+
+theme = "Base"
+
+with gr.Blocks(css=css) as demo:
+
+
+ 
+ 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.Row():
+ with gr.Column(scale=6, min_width=600):
+ prompt = gr.Textbox("puppies in a cloud, 4k", placeholder="Prompt", show_label=False, lines=3)
+ negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3)
+ with gr.Column():
+ text_button = gr.Button("Generate", variant='primary', elem_id="generate")
+ with gr.Row():
+ 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.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=50, 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()
+
+ 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()