Spaces:
Runtime error
Runtime error
File size: 4,526 Bytes
0f3978b 5683ad2 0f3978b 5bb086c 6dbc9d1 0f3978b 7ccc156 0f3978b 7ccc156 0f3978b 367fa57 0f3978b 7ccc156 367fa57 0f3978b 7ccc156 367fa57 0f3978b 7ccc156 367fa57 7ccc156 367fa57 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
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}/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):
with Image.open(image_path) as image:
buffered = BytesIO()
image.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue())
return img_str.decode('utf-8')
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%;
}
"""
# Define the Gradio interface
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
gr.Markdown("# Disney Pixar AI Poster Generator")
gr.Markdown("Enter your prompt and adjust the settings to generate a Disney Pixar style AI poster.")
with gr.Row():
with gr.Column(scale=2):
prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3)
negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly")
text_button = gr.Button("Generate", variant='primary', style={"min-width": "100%"})
with gr.Column(scale=4):
image_output = gr.Image(value="https://cdn-uploads.huggingface.co/production/uploads/noauth/XWJyh9DhMGXrzyRJk7SfP.png", label="Generated Image", style={"width": "100%", "height": "auto"})
with gr.Row():
with gr.Column(scale=2):
model = gr.Dropdown(interactive=True, value="sd_xl_base_1.0.safetensors [be9edd61]", show_label=True, label="Stable Diffusion Checkpoint", style={"min-width": "100%"})
sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method", style={"min-width": "100%"})
steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, 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=2):
width = gr.Slider(label="Width", minimum=512, maximum=1536, value=1024, step=8)
height = gr.Slider(label="Height", minimum=512, maximum=1536, value=1024, step=8)
batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
gr.Markdown("*Resolution Maximum: 1MP (1048576 px)*")
# Launch the Gradio interface
demo.launch()
|