Instructions to use knoxcs/stable-diffusion-webui-knox with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use knoxcs/stable-diffusion-webui-knox with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("knoxcs/stable-diffusion-webui-knox", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,020 Bytes
0d3f4c1 | 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 | import threading
import collections
# reference: https://gist.github.com/vitaliyp/6d54dd76ca2c3cdfc1149d33007dc34a
class FIFOLock(object):
def __init__(self):
self._lock = threading.Lock()
self._inner_lock = threading.Lock()
self._pending_threads = collections.deque()
def acquire(self, blocking=True):
with self._inner_lock:
lock_acquired = self._lock.acquire(False)
if lock_acquired:
return True
elif not blocking:
return False
release_event = threading.Event()
self._pending_threads.append(release_event)
release_event.wait()
return self._lock.acquire()
def release(self):
with self._inner_lock:
if self._pending_threads:
release_event = self._pending_threads.popleft()
release_event.set()
self._lock.release()
__enter__ = acquire
def __exit__(self, t, v, tb):
self.release()
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