🚀

app.py

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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}/job", params)
+        return response.json()
+    
+    def transform(self, params):
+        response = self._post(f"{self.base}/transform", params)
+        return response.json()
+    
+    def controlnet(self, params):
+        response = self._post(f"{self.base}/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):
+    result = prodia_client.generate({
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "model": model,
+        "steps": steps,
+        "sampler": sampler,
+        "cfg_scale": cfg_scale
+    })
+
+    job = prodia_client.wait(result)
+
+    return job["imageUrl"]
+
+css = """
+#generate {
+    height: 100%;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Tab("txt2img"):
+        with gr.Row():
+            with gr.Column(scale=6, min_width=600):
+                prompt = gr.Textbox(placeholder="Prompt", show_label=False, lines=3)
+                negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3)
+            with gr.Column(equal_height=True):
+                text_button = gr.Button("Generate", variant='primary', elem_id="generate")
+                
+        with gr.Row():
+            with gr.Column(scale=1):
+                with gr.Tab("Generation"):
+                    with gr.Row():
+                        with gr.Column(scale=1):
+                            model = gr.Dropdown(interactive=True,value="v1-5-pruned-emaonly.safetensors [d7049739]", show_label=False, choices=prodia_client.list_models())
+
+                            sampler = gr.Dropdown(value="Euler a", show_label=False, 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", miniumum=1, maximum=50, value=25)
+                            cfg_scale = gr.Slider(label="CFG Scale", miniumum=1, maximum=20, value=7)
+                
+            with gr.Column(scale=1):
+                image_output = gr.Image()
+
+        text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale], outputs=image_output)
+    
+
+demo.launch()

readme.md

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+---
+title: stable-diffusion
+emoji: 🔥
+colorFrom: yellow
+colorTo: gray
+sdk: gradio
+sdk_version: 3.11.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Prodia's Stable Diffusion Space.

requirements.txt

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+numpy
+gradio
+requests