61 lines
1.5 KiB
Python
61 lines
1.5 KiB
Python
import time
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import json
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import torch
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import requests
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import numpy as np
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from PIL import Image
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POLLING = 0.5
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def wait_for_job(remote_url, job_id):
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fail = 0
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while fail <= 3:
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r = requests.get(f"{remote_url}/history", timeout=4)
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try:
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r.raise_for_status()
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except Exception as e:
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print("NetDist caught error while fetching output image:\n", e)
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fail += 1
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continue
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data = r.json()
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if not data:
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time.sleep(POLLING)
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continue
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for i,d in data.items():
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if d["prompt"][3].get("job_id") == job_id:
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# this needs to be less jank
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if len(d["outputs"].keys()) > 0:
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return d["outputs"][list(d["outputs"].keys())[-1]].get("images")
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else:
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return []
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# todo: check if it's actually in the queue to avoid waiting forever
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time.sleep(POLLING)
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raise OSError("Failed to fetch image from remote client!")
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def fetch_from_remote(remote_url, job_id):
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def img_to_torch(img):
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image = img.convert("RGB")
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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return image
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if not remote_url or not job_id:
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return None
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images = []
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for i in wait_for_job(remote_url, job_id):
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img_url = f"{remote_url}/view?filename={i['filename']}&subfolder={i['subfolder']}&type={i['type']}"
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ir = requests.get(img_url, stream=True, timeout=16)
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ir.raise_for_status()
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img = Image.open(ir.raw)
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images.append(img_to_torch(img))
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if len(images) == 0:
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return None
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out = images[0]
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for i in images[1:]:
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out = torch.cat((out,i))
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return out
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