montyanderson commited on
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Files changed (3) hide show
  1. app.py +152 -0
  2. readme.md +13 -0
  3. requirements.txt +3 -0
app.py ADDED
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+ import numpy as np
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+ import gradio as gr
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+ import requests
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+ import time
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+ import json
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+ import base64
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+ import os
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+ from PIL import Image
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+ from io import BytesIO
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+
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+ class Prodia:
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+ def __init__(self, api_key, base=None):
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+ self.base = base or "https://api.prodia.com/v1"
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+ self.headers = {
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+ "X-Prodia-Key": api_key
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+ }
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+
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+ def generate(self, params):
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+ response = self._post(f"{self.base}/job", params)
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+ return response.json()
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+
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+ def transform(self, params):
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+ response = self._post(f"{self.base}/transform", params)
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+ return response.json()
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+
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+ def controlnet(self, params):
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+ response = self._post(f"{self.base}/controlnet", params)
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+ return response.json()
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+
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+ def get_job(self, job_id):
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+ response = self._get(f"{self.base}/job/{job_id}")
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+ return response.json()
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+
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+ def wait(self, job):
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+ job_result = job
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+
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+ while job_result['status'] not in ['succeeded', 'failed']:
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+ time.sleep(0.25)
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+ job_result = self.get_job(job['job'])
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+
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+ return job_result
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+
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+ def list_models(self):
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+ response = self._get(f"{self.base}/models/list")
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+ return response.json()
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+
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+ def _post(self, url, params):
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+ headers = {
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+ **self.headers,
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+ "Content-Type": "application/json"
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+ }
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+ response = requests.post(url, headers=headers, data=json.dumps(params))
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+
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+ if response.status_code != 200:
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+ raise Exception(f"Bad Prodia Response: {response.status_code}")
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+
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+ return response
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+
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+ def _get(self, url):
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+ response = requests.get(url, headers=self.headers)
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+
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+ if response.status_code != 200:
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+ raise Exception(f"Bad Prodia Response: {response.status_code}")
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+
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+ return response
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+
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+
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+ def image_to_base64(image_path):
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+ # Open the image with PIL
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+ with Image.open(image_path) as image:
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+ # Convert the image to bytes
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+ buffered = BytesIO()
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+ image.save(buffered, format="PNG") # You can change format to PNG if needed
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+
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+ # Encode the bytes to base64
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+ img_str = base64.b64encode(buffered.getvalue())
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+
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+ return img_str.decode('utf-8') # Convert bytes to string
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+
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+
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+
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+ prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))
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+
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+ def flip_text(prompt, negative_prompt, model, steps, sampler, cfg_scale):
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+ result = prodia_client.generate({
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+ "prompt": prompt,
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+ "negative_prompt": negative_prompt,
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+ "model": model,
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+ "steps": steps,
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+ "sampler": sampler,
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+ "cfg_scale": cfg_scale
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+ })
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+
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+ job = prodia_client.wait(result)
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+
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+ return job["imageUrl"]
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+
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+ css = """
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+ #generate {
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+ height: 100%;
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+ }
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+ """
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+
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+ with gr.Blocks(css=css) as demo:
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+ with gr.Tab("txt2img"):
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+ with gr.Row():
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+ with gr.Column(scale=6, min_width=600):
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+ prompt = gr.Textbox(placeholder="Prompt", show_label=False, lines=3)
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+ negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3)
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+ with gr.Column(equal_height=True):
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+ text_button = gr.Button("Generate", variant='primary', elem_id="generate")
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+
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+ with gr.Row():
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+ with gr.Column(scale=1):
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+ with gr.Tab("Generation"):
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+ with gr.Row():
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+ with gr.Column(scale=1):
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+ model = gr.Dropdown(interactive=True,value="v1-5-pruned-emaonly.safetensors [d7049739]", show_label=False, choices=prodia_client.list_models())
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+
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+ sampler = gr.Dropdown(value="Euler a", show_label=False, choices=[
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+ "Euler",
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+ "Euler a",
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+ "LMS",
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+ "Heun",
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+ "DPM2",
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+ "DPM2 a",
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+ "DPM++ 2S a",
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+ "DPM++ 2M",
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+ "DPM++ SDE",
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+ "DPM fast",
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+ "DPM adaptive",
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+ "LMS Karras",
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+ "DPM2 Karras",
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+ "DPM2 a Karras",
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+ "DPM++ 2S a Karras",
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+ "DPM++ 2M Karras",
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+ "DPM++ SDE Karras",
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+ "DDIM",
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+ "PLMS",
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+ ])
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+
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+ with gr.Column(scale=1):
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+ steps = gr.Slider(label="Steps", miniumum=1, maximum=50, value=25)
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+ cfg_scale = gr.Slider(label="CFG Scale", miniumum=1, maximum=20, value=7)
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+
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+ with gr.Column(scale=1):
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+ image_output = gr.Image()
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+
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+ text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale], outputs=image_output)
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+
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+
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+ demo.launch()
readme.md ADDED
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+ ---
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+ title: stable-diffusion
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+ emoji: πŸ”₯
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+ colorFrom: yellow
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+ colorTo: gray
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+ sdk: gradio
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+ sdk_version: 3.11.0
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+ app_file: app.py
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+ pinned: false
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+ license: mit
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+ ---
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+
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+ Prodia's Stable Diffusion Space.
requirements.txt ADDED
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+ numpy
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+ gradio
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+ requests