75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
from .lcm.lcm_scheduler import LCMScheduler
|
|
from .lcm.lcm_pipeline import LatentConsistencyModelPipeline
|
|
import folder_paths
|
|
from os import path
|
|
import time
|
|
import torch
|
|
import random
|
|
import numpy as np
|
|
from comfy.model_management import get_torch_device
|
|
|
|
MAX_SEED = np.iinfo(np.int32).max
|
|
|
|
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
|
|
if randomize_seed:
|
|
seed = random.randint(0, MAX_SEED)
|
|
return seed
|
|
|
|
class LCMSampler:
|
|
|
|
def __init__(self):
|
|
self.scheduler = LCMScheduler.from_pretrained(path.join(path.dirname(__file__), "scheduler_config.json"))
|
|
self.pipe = None
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required":
|
|
{"ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
|
|
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
|
|
"size": ("INT", {"default": 512, "min": 512, "max": 768}),
|
|
"num_images": ("INT", {"default": 1, "min": 1, "max": 64}),
|
|
"positive_prompt": ("STRING", {"multiline": True}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("LATENT",)
|
|
FUNCTION = "sample"
|
|
|
|
CATEGORY = "sampling"
|
|
|
|
def sample(self, ckpt_name, seed, steps, cfg, positive_prompt, size, num_images):
|
|
if self.pipe is None:
|
|
self.pipe = LatentConsistencyModelPipeline.from_pretrained(
|
|
pretrained_model_name_or_path=folder_paths.get_annotated_filepath(ckpt_name, "checkpoints"),
|
|
local_files_only=True,
|
|
scheduler=self.scheduler
|
|
)
|
|
self.pipe.to(get_torch_device())
|
|
|
|
torch.manual_seed(seed)
|
|
start_time = time.time()
|
|
|
|
result = self.pipe(
|
|
prompt=positive_prompt,
|
|
width=size,
|
|
height=size,
|
|
guidance_scale=cfg,
|
|
num_inference_steps=steps,
|
|
num_images_per_prompt=num_images,
|
|
lcm_origin_steps=50,
|
|
output_type="latent",
|
|
).images
|
|
|
|
print("LCM inference time: ", time.time() - start_time, "seconds")
|
|
|
|
return (result,)
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"LCMSampler": LCMSampler
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"LCMSampler" : "LCMSampler"
|
|
} |