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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': scale_by
})
job = prodia_client.wait(result)
return job["imageUrl"]
def get_models():
return prodia_client.list_models()
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