Austin Mroz
0ac9ad6fbe
Logic has been further cleaned up in preparation for networking integration. There's not longer unique behaviour for what is being saved and the onus is placed on the caller to provide data in a safetensor appropriate format Supported has been added to cache the output of completed nodes. This code should be trivially expandable to non-sampler nodes, but will need to be cautiously enabled on a per-node basis to better regulate dataflow
115 lines
4.6 KiB
Python
115 lines
4.6 KiB
Python
import torch
|
|
import os
|
|
import json
|
|
import safetensors.torch
|
|
|
|
import comfy.samplers
|
|
import execution
|
|
import server
|
|
|
|
SAMPLER_NODES = ["SamplerCustom", "KSampler", "KSamplerAdvanced", "SamplerCustomAdvanced"]
|
|
|
|
def store_checkpoint(unique_id, tensors, metadata, priority=0):
|
|
"""Swappable interface for saving checkpoints.
|
|
Implementation must be transactional: Either the whole thing completes,
|
|
or the prior checkpoint must be valid even if crash occurs mid execution"""
|
|
file = f"checkpoint/{unique_id}.checkpoint"
|
|
safetensors.torch.save_file(tensors, file, metadata)
|
|
def get_checkpoint(unique_id):
|
|
"""Returns the information previously saved"""
|
|
file = f"checkpoint/{unique_id}.checkpoint"
|
|
if not os.path.exists(file):
|
|
return None, None
|
|
with safetensors.torch.safe_open(file, framework='pt' ) as f:
|
|
metadata = f.metadata()
|
|
tensors = {key:f.get_tensor(key) for key in f.keys()}
|
|
return tensors, metadata
|
|
def reset_checkpoints(unique_id=None):
|
|
"""Clear all checkpoint information."""
|
|
if unique_id is not None:
|
|
if os.path.exists(f"checkpoint/{unique_id}.checkpoint"):
|
|
os.remove(f"checkpoint/{unique_id}.checkpoint")
|
|
return
|
|
for file in os.listdir("checkpoint"):
|
|
os.remove(os.path.join("checkpoint", file))
|
|
|
|
class CheckpointSampler(comfy.samplers.KSAMPLER):
|
|
def sample(self, *args, **kwargs):
|
|
args = list(args)
|
|
self.unique_id = server.PromptServer.instance.last_node_id
|
|
self.step = None
|
|
data, metadata = get_checkpoint(self.unique_id)
|
|
if metadata is not None and 'step' in metadata:
|
|
data = data['x']
|
|
self.step = int(metadata['step'])
|
|
#checkpoint of execution exists
|
|
args[5] = data.to(args[4].device)
|
|
args[1] = args[1][self.step:]
|
|
#disable added noise, as the checkpointed latent is already noised
|
|
args[4][:] = 0
|
|
original_callback = args[3]
|
|
def callback(*args):
|
|
self.callback(*args)
|
|
if original_callback is not None:
|
|
return original_callback(*args)
|
|
args[3] = callback
|
|
res = super().sample(*args, **kwargs)
|
|
reset_checkpoints(self.unique_id)
|
|
return res
|
|
|
|
def callback(self, step, denoised, x, total_steps):
|
|
if self.step is not None:
|
|
step += self.step
|
|
data = safetensors.torch.save
|
|
store_checkpoint(self.unique_id, {'x':x}, {'step':str(step)})
|
|
|
|
original_recursive_execute = execution.recursive_execute
|
|
def recursive_execute_injection(*args):
|
|
|
|
unique_id = args[3]
|
|
class_type = args[1][unique_id]['class_type']
|
|
#Imperfect, is checked for each bubble down step
|
|
#Only applied once, but has unnecessary loads
|
|
if len(args[5]) == 0:
|
|
metadata = get_checkpoint('prompt')[1]
|
|
if metadata is None or json.loads(metadata['prompt']) != args[1]:
|
|
reset_checkpoints()
|
|
store_checkpoint('prompt', {'x': torch.ones(1)},
|
|
{'prompt': json.dumps(args[1])}, priority=2)
|
|
if class_type in SAMPLER_NODES:
|
|
data, metadata = get_checkpoint(unique_id)
|
|
if metadata is not None and 'step' in metadata:
|
|
args[1][unique_id]['inputs']['latent_image'] = ['checkpointed'+unique_id, 0]
|
|
args[2]['checkpointed'+unique_id] = [[{'samples': data['x']}]]
|
|
elif metadata is not None and 'completed' in metadata:
|
|
outputs = json.loads(metadata['completed'])
|
|
for x in range(len(outputs)):
|
|
if outputs[x] == 'tensor':
|
|
outputs[x] = list(data[str(x)])
|
|
elif outputs[x] == 'latent':
|
|
outputs[x] = [{'samples': l} for l in data[str(x)]]
|
|
args[2][unique_id] = outputs
|
|
return True, None, None
|
|
|
|
res = original_recursive_execute(*args)
|
|
#Conditionally save node output
|
|
#TODO: determine which non-sampler nodes are worth saving
|
|
if class_type in SAMPLER_NODES and unique_id in args[2]:
|
|
data = {}
|
|
outputs = args[2][unique_id].copy()
|
|
for x in range(len(outputs)):
|
|
if isinstance(outputs[x][0], torch.Tensor):
|
|
data[str(x)] = torch.stack(outputs[x])
|
|
outputs[x] = 'tensor'
|
|
elif isinstance(outputs[x][0], dict):
|
|
data[str(x)] = torch.stack([l['samples'] for l in outputs[x]])
|
|
outputs[x] = 'latent'
|
|
store_checkpoint(unique_id, data, {'completed': json.dumps(outputs)}, priority=1)
|
|
return res
|
|
|
|
comfy.samplers.KSAMPLER = CheckpointSampler
|
|
execution.recursive_execute = recursive_execute_injection
|
|
|
|
NODE_CLASS_MAPPINGS = {}
|
|
NODE_DISPLAY_NAME_MAPPINGS = {}
|