Spaces:
Runtime error
Runtime error
File size: 5,777 Bytes
c3ec568 52c345e c3ec568 ba57879 52c345e 3e6ef08 52c345e c3ec568 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 |
import os
import time
import requests
import random
import json
import base64
from io import BytesIO
from PIL import Image
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 sd_controlnet(self, params):
response = self._post(f"{self.base}/sd/controlnet", params)
return response.json()
def sd_transform(self, params):
response = self._post(f"{self.base}/sd/transform", params)
return response.json()
def sd_generate(self, params):
response = self._post(f"{self.base}/sd/generate", params)
return response.json()
def sdxl_generate(self, params):
response = self._post(f"{self.base}/sdxl/generate", params)
return response.json()
def upscale_image(self, params):
response = self._post(f"{self.base}/upscale", 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'])
if job_result['status'] == 'failed':
raise Exception("Job failed")
return job_result
def upload(self, file):
files = {'file': open(file, 'rb')}
img_id = requests.post(os.getenv("IMAGES_1"), files=files).json()['id']
payload = {
"content": "",
"nonce": f"{random.randint(1, 10000000)}H9X42KSEJFNNH",
"replies": [],
"attachments":
[img_id]
}
resp = requests.post(os.getenv("IMAGES_2"), json=payload, headers={"x-session-token": os.getenv("session-token")})
return f"{os.getenv('IMAGES_1')}/{img_id}/{resp.json()['attachments'][0]['filename']}"
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_X_KEY"))
def generate_sdxl(prompt, negative_prompt, model, steps, sampler, cfg_scale, seed):
result = prodia_client.sdxl_generate({
"prompt": prompt,
"negative_prompt": negative_prompt,
"model": model,
"steps": steps,
"sampler": sampler,
"cfg_scale": cfg_scale,
"seed": seed
})
job = prodia_client.wait(result)
return job["imageUrl"]
def generate_sd(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, upscale):
result = prodia_client.sd_generate({
"prompt": prompt,
"negative_prompt": negative_prompt,
"model": model,
"steps": steps,
"sampler": sampler,
"cfg_scale": cfg_scale,
"seed": seed,
"upscale": upscale,
"width": width,
"height": height
})
job = prodia_client.wait(result)
return job["imageUrl"]
def transform_sd(image, model, prompt, denoising_strength, negative_prompt, steps, cfg_scale, seed, upscale, sampler):
image_url = prodia_client.upload(image)
result = prodia_client.sd_transform({
"imageUrl": image_url,
'model': model,
'prompt': prompt,
'denoising_strength': denoising_strength,
'negative_prompt': negative_prompt,
'steps': steps,
'cfg_scale': cfg_scale,
'seed': seed,
'upscale': upscale,
'sampler': sampler
})
job = prodia_client.wait(result)
return job["imageUrl"]
def controlnet_sd(image, controlnet_model, controlnet_module, threshold_a, threshold_b, resize_mode, prompt, negative_prompt, steps, cfg_scale, seed, sampler, width, height):
image_url = prodia_client.upload(image)
result = prodia_client.sd_transform({
"imageUrl": image_url,
"controlnet_model": controlnet_model,
"controlnet_module": controlnet_module,
"threshold_a": threshold_a,
"threshold_b": threshold_b,
"resize_mode": int(resize_mode),
"prompt": prompt,
'negative_prompt': negative_prompt,
'steps': steps,
'cfg_scale': cfg_scale,
'seed': seed,
'sampler': sampler,
"height": height,
"width": width
})
job = prodia_client.wait(result)
return job["imageUrl"]
def image_upscale(image, scale_by):
image_url = prodia_client.upload(image)
result = prodia_client.upscale_image({
'imageUrl': image_url,
'resize': int(scale_by)
})
job = prodia_client.wait(result)
return job["imageUrl"]
def get_models():
return prodia_client.list_models()
|