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 queue
import traceback
if '--dont-upcast-attention' in sys.argv:
print("disabling upcasting of attention")
os.environ['ATTN_PRECISION'] = "fp16"
import torch
import nodes
def get_input_data(inputs, class_def, outputs={}, prompt={}, extra_data={}):
valid_inputs = class_def.INPUT_TYPES()
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(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(prompt, outputs, input_unique_id, extra_data)
input_data_all = get_input_data(inputs, class_def, outputs, prompt, extra_data)
obj = class_def()
outputs[unique_id] = getattr(obj, obj.FUNCTION)(**input_data_all)
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]
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 'is_changed' not in prompt[unique_id]:
if unique_id in old_prompt and 'is_changed' in old_prompt[unique_id]:
is_changed_old = old_prompt[unique_id]['is_changed']
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']
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:
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:
print("deleted", unique_id)
d = outputs.pop(unique_id)
del d
return to_delete
class PromptExecutor:
def __init__(self):
self.outputs = {}
self.old_prompt = {}
def execute(self, prompt, extra_data={}):
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 = []
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:
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]
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(prompt, self.outputs, x, extra_data)
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])
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))
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 = []
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)]
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))
return (True, "")
def prompt_worker(q):
e = PromptExecutor()
while True:
item = q.get()
e.execute(item[-2], item[-1])
q.task_done()
from http.server import BaseHTTPRequestHandler, HTTPServer
class PromptServer(BaseHTTPRequestHandler):
def _set_headers(self, code=200, ct='text/html'):
self.send_response(code)
self.send_header('Content-type', ct)
self.end_headers()
def log_message(self, format, *args):
pass
def do_GET(self):
if self.path == "/prompt":
self._set_headers(ct='application/json')
prompt_info = {}
exec_info = {}
exec_info['queue_remaining'] = self.server.prompt_queue.unfinished_tasks
prompt_info['exec_info'] = exec_info
self.wfile.write(json.dumps(prompt_info).encode('utf-8'))
elif self.path == "/object_info":
self._set_headers(ct='application/json')
out = {}
for x in nodes.NODE_CLASS_MAPPINGS:
obj_class = nodes.NODE_CLASS_MAPPINGS[x]
info = {}
info['input'] = obj_class.INPUT_TYPES()
info['output'] = obj_class.RETURN_TYPES
info['name'] = x #TODO
info['description'] = ''
info['category'] = 'sd'
if hasattr(obj_class, 'CATEGORY'):
info['category'] = obj_class.CATEGORY
out[x] = info
self.wfile.write(json.dumps(out).encode('utf-8'))
elif self.path[1:] in os.listdir(self.server.server_dir):
if self.path[1:].endswith('.css'):
self._set_headers(ct='text/css')
elif self.path[1:].endswith('.js'):
self._set_headers(ct='text/javascript')
else:
self._set_headers()
with open(os.path.join(self.server.server_dir, self.path[1:]), "rb") as f:
self.wfile.write(f.read())
else:
self._set_headers()
with open(os.path.join(self.server.server_dir, "index.html"), "rb") as f:
self.wfile.write(f.read())
def do_HEAD(self):
self._set_headers()
def do_POST(self):
resp_code = 200
out_string = ""
if self.path == "/prompt":
print("got prompt")
self.data_string = self.rfile.read(int(self.headers['Content-Length']))
json_data = json.loads(self.data_string)
if "number" in json_data:
number = float(json_data['number'])
else:
number = self.server.number
self.server.number += 1
if "prompt" in json_data:
prompt = json_data["prompt"]
valid = validate_prompt(prompt)
extra_data = {}
if "extra_data" in json_data:
extra_data = json_data["extra_data"]
if valid[0]:
self.server.prompt_queue.put((number, id(prompt), prompt, extra_data))
else:
resp_code = 400
out_string = valid[1]
print("invalid prompt:", valid[1])
self._set_headers(code=resp_code)
self.end_headers()
self.wfile.write(out_string.encode('utf8'))
return
def run(prompt_queue, address='', port=8188):
server_address = (address, port)
httpd = HTTPServer(server_address, PromptServer)
httpd.server_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "webshit")
httpd.prompt_queue = prompt_queue
httpd.number = 0
if server_address[0] == '':
addr = '0.0.0.0'
else:
addr = server_address[0]
print("Starting server\n")
print("To see the GUI go to: http://{}:{}".format(addr, server_address[1]))
httpd.serve_forever()
if __name__ == "__main__":
q = queue.PriorityQueue()
threading.Thread(target=prompt_worker, daemon=True, args=(q,)).start()
run(q, address='127.0.0.1', port=8188)