The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
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import os
import sys
import copy
import json
import threading
import heapq
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import traceback
import asyncio
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if os.name == "nt":
import logging
logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
import server
if __name__ == "__main__":
if '--help' in sys.argv:
print("Valid Command line Arguments:")
print("\t--listen\t\t\tListen on 0.0.0.0 so the UI can be accessed from other computers.")
print("\t--port 8188\t\t\tSet the listen port.")
print("\t--dont-upcast-attention\t\tDisable upcasting of attention \n\t\t\t\t\tcan boost speed but increase the chances of black images.\n")
print("\t--use-split-cross-attention\tUse the split cross attention optimization instead of the sub-quadratic one.\n\t\t\t\t\tIgnored when xformers is used.")
print()
print("\t--highvram\t\t\tBy default models will be unloaded to CPU memory after being used.\n\t\t\t\t\tThis option keeps them in GPU memory.\n")
print("\t--normalvram\t\t\tUsed to force normal vram use if lowvram gets automatically enabled.")
print("\t--lowvram\t\t\tSplit the unet in parts to use less vram.")
print("\t--novram\t\t\tWhen lowvram isn't enough.")
print()
exit()
if '--dont-upcast-attention' in sys.argv:
print("disabling upcasting of attention")
os.environ['ATTN_PRECISION'] = "fp16"
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import torch
import nodes
def get_input_data(inputs, class_def, outputs={}, prompt={}, extra_data={}):
valid_inputs = class_def.INPUT_TYPES()
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input_data_all = {}
for x in inputs:
input_data = inputs[x]
if isinstance(input_data, list):
input_unique_id = input_data[0]
output_index = input_data[1]
obj = outputs[input_unique_id][output_index]
input_data_all[x] = obj
else:
if ("required" in valid_inputs and x in valid_inputs["required"]) or ("optional" in valid_inputs and x in valid_inputs["optional"]):
input_data_all[x] = input_data
if "hidden" in valid_inputs:
h = valid_inputs["hidden"]
for x in h:
if h[x] == "PROMPT":
input_data_all[x] = prompt
if h[x] == "EXTRA_PNGINFO":
if "extra_pnginfo" in extra_data:
input_data_all[x] = extra_data['extra_pnginfo']
return input_data_all
def recursive_execute(server, prompt, outputs, current_item, extra_data={}):
unique_id = current_item
inputs = prompt[unique_id]['inputs']
class_type = prompt[unique_id]['class_type']
class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
if unique_id in outputs:
return []
executed = []
for x in inputs:
input_data = inputs[x]
if isinstance(input_data, list):
input_unique_id = input_data[0]
output_index = input_data[1]
if input_unique_id not in outputs:
executed += recursive_execute(server, prompt, outputs, input_unique_id, extra_data)
input_data_all = get_input_data(inputs, class_def, outputs, prompt, extra_data)
if server.client_id is not None:
server.send_sync("executing", { "node": unique_id }, server.client_id)
obj = class_def()
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outputs[unique_id] = getattr(obj, obj.FUNCTION)(**input_data_all)
if "ui" in outputs[unique_id] and server.client_id is not None:
server.send_sync("executed", { "node": unique_id, "output": outputs[unique_id]["ui"] }, server.client_id)
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return executed + [unique_id]
def recursive_will_execute(prompt, outputs, current_item):
unique_id = current_item
inputs = prompt[unique_id]['inputs']
will_execute = []
if unique_id in outputs:
return []
for x in inputs:
input_data = inputs[x]
if isinstance(input_data, list):
input_unique_id = input_data[0]
output_index = input_data[1]
if input_unique_id not in outputs:
will_execute += recursive_will_execute(prompt, outputs, input_unique_id)
return will_execute + [unique_id]
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def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item):
unique_id = current_item
inputs = prompt[unique_id]['inputs']
class_type = prompt[unique_id]['class_type']
class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
is_changed_old = ''
is_changed = ''
if hasattr(class_def, 'IS_CHANGED'):
if unique_id in old_prompt and 'is_changed' in old_prompt[unique_id]:
is_changed_old = old_prompt[unique_id]['is_changed']
if 'is_changed' not in prompt[unique_id]:
input_data_all = get_input_data(inputs, class_def)
is_changed = class_def.IS_CHANGED(**input_data_all)
prompt[unique_id]['is_changed'] = is_changed
else:
is_changed = prompt[unique_id]['is_changed']
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if unique_id not in outputs:
return True
to_delete = False
if is_changed != is_changed_old:
to_delete = True
elif unique_id not in old_prompt:
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to_delete = True
elif inputs == old_prompt[unique_id]['inputs']:
for x in inputs:
input_data = inputs[x]
if isinstance(input_data, list):
input_unique_id = input_data[0]
output_index = input_data[1]
if input_unique_id in outputs:
to_delete = recursive_output_delete_if_changed(prompt, old_prompt, outputs, input_unique_id)
else:
to_delete = True
if to_delete:
break
else:
to_delete = True
if to_delete:
d = outputs.pop(unique_id)
del d
return to_delete
class PromptExecutor:
def __init__(self, server):
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self.outputs = {}
self.old_prompt = {}
self.server = server
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def execute(self, prompt, extra_data={}):
if "client_id" in extra_data:
self.server.client_id = extra_data["client_id"]
else:
self.server.client_id = None
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with torch.no_grad():
for x in prompt:
recursive_output_delete_if_changed(prompt, self.old_prompt, self.outputs, x)
current_outputs = set(self.outputs.keys())
executed = []
try:
to_execute = []
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for x in prompt:
class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']]
if hasattr(class_, 'OUTPUT_NODE'):
to_execute += [(0, x)]
while len(to_execute) > 0:
#always execute the output that depends on the least amount of unexecuted nodes first
to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1])), a[-1]), to_execute)))
x = to_execute.pop(0)[-1]
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class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']]
if hasattr(class_, 'OUTPUT_NODE'):
if class_.OUTPUT_NODE == True:
valid = False
try:
m = validate_inputs(prompt, x)
valid = m[0]
except:
valid = False
if valid:
executed += recursive_execute(self.server, prompt, self.outputs, x, extra_data)
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except Exception as e:
print(traceback.format_exc())
to_delete = []
for o in self.outputs:
if o not in current_outputs:
to_delete += [o]
if o in self.old_prompt:
d = self.old_prompt.pop(o)
del d
for o in to_delete:
d = self.outputs.pop(o)
del d
else:
executed = set(executed)
for x in executed:
self.old_prompt[x] = copy.deepcopy(prompt[x])
finally:
if self.server.client_id is not None:
self.server.send_sync("executing", { "node": None }, self.server.client_id)
torch.cuda.empty_cache()
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def validate_inputs(prompt, item):
unique_id = item
inputs = prompt[unique_id]['inputs']
class_type = prompt[unique_id]['class_type']
obj_class = nodes.NODE_CLASS_MAPPINGS[class_type]
class_inputs = obj_class.INPUT_TYPES()
required_inputs = class_inputs['required']
for x in required_inputs:
if x not in inputs:
return (False, "Required input is missing. {}, {}".format(class_type, x))
val = inputs[x]
info = required_inputs[x]
type_input = info[0]
if isinstance(val, list):
if len(val) != 2:
return (False, "Bad Input. {}, {}".format(class_type, x))
o_id = val[0]
o_class_type = prompt[o_id]['class_type']
r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES
if r[val[1]] != type_input:
return (False, "Return type mismatch. {}, {}".format(class_type, x))
r = validate_inputs(prompt, o_id)
if r[0] == False:
return r
else:
if type_input == "INT":
val = int(val)
inputs[x] = val
if type_input == "FLOAT":
val = float(val)
inputs[x] = val
if type_input == "STRING":
val = str(val)
inputs[x] = val
if len(info) > 1:
if "min" in info[1] and val < info[1]["min"]:
return (False, "Value smaller than min. {}, {}".format(class_type, x))
if "max" in info[1] and val > info[1]["max"]:
return (False, "Value bigger than max. {}, {}".format(class_type, x))
if isinstance(type_input, list):
if val not in type_input:
return (False, "Value not in list. {}, {}: {} not in {}".format(class_type, x, val, type_input))
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return (True, "")
def validate_prompt(prompt):
outputs = set()
for x in prompt:
class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']]
if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE == True:
outputs.add(x)
if len(outputs) == 0:
return (False, "Prompt has no outputs")
good_outputs = set()
errors = []
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for o in outputs:
valid = False
reason = ""
try:
m = validate_inputs(prompt, o)
valid = m[0]
reason = m[1]
except:
valid = False
reason = "Parsing error"
if valid == True:
good_outputs.add(x)
else:
print("Failed to validate prompt for output {} {}".format(o, reason))
print("output will be ignored")
errors += [(o, reason)]
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if len(good_outputs) == 0:
errors_list = "\n".join(map(lambda a: "{}".format(a[1]), errors))
return (False, "Prompt has no properly connected outputs\n {}".format(errors_list))
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return (True, "")
def prompt_worker(q, server):
e = PromptExecutor(server)
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while True:
item, item_id = q.get()
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e.execute(item[-2], item[-1])
q.task_done(item_id, e.outputs)
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class PromptQueue:
def __init__(self, server):
self.server = server
self.mutex = threading.RLock()
self.not_empty = threading.Condition(self.mutex)
self.task_counter = 0
self.queue = []
self.currently_running = {}
self.history = {}
server.prompt_queue = self
def put(self, item):
with self.mutex:
heapq.heappush(self.queue, item)
self.server.queue_updated()
self.not_empty.notify()
def get(self):
with self.not_empty:
while len(self.queue) == 0:
self.not_empty.wait()
item = heapq.heappop(self.queue)
i = self.task_counter
self.currently_running[i] = copy.deepcopy(item)
self.task_counter += 1
self.server.queue_updated()
return (item, i)
def task_done(self, item_id, outputs):
with self.mutex:
prompt = self.currently_running.pop(item_id)
self.history[prompt[1]] = { "prompt": prompt, "outputs": {} }
for o in outputs:
if "ui" in outputs[o]:
self.history[prompt[1]]["outputs"][o] = outputs[o]["ui"]
self.server.queue_updated()
def get_current_queue(self):
with self.mutex:
out = []
for x in self.currently_running.values():
out += [x]
return (out, copy.deepcopy(self.queue))
def get_tasks_remaining(self):
with self.mutex:
return len(self.queue) + len(self.currently_running)
def wipe_queue(self):
with self.mutex:
self.queue = []
self.server.queue_updated()
def delete_queue_item(self, function):
with self.mutex:
for x in range(len(self.queue)):
if function(self.queue[x]):
if len(self.queue) == 1:
self.wipe_queue()
else:
self.queue.pop(x)
heapq.heapify(self.queue)
self.server.queue_updated()
return True
return False
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async def run(server, address='', port=8188):
await asyncio.gather(server.start(address, port), server.publish_loop())
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def hijack_progress(server):
from tqdm.auto import tqdm
orig_func = getattr(tqdm, "update")
def wrapped_func(*args, **kwargs):
pbar = args[0]
v = orig_func(*args, **kwargs)
server.send_sync("progress", { "value": pbar.n, "max": pbar.total}, server.client_id)
return v
setattr(tqdm, "update", wrapped_func)
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if __name__ == "__main__":
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
server = server.PromptServer(loop)
q = PromptQueue(server)
hijack_progress(server)
threading.Thread(target=prompt_worker, daemon=True, args=(q,server,)).start()
if '--listen' in sys.argv:
address = '0.0.0.0'
else:
address = '127.0.0.1'
port = 8188
try:
p_index = sys.argv.index('--port')
port = int(sys.argv[p_index + 1])
except:
pass
if os.name == "nt":
try:
loop.run_until_complete(run(server, address=address, port=port))
except KeyboardInterrupt:
pass
else:
loop.run_until_complete(run(server, address=address, port=port))
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