Open Soliver84 opened 9 months ago
notes.py: `from .lcm.lcm_scheduler import LCMScheduler from .lcm.lcm_pipeline import LatentConsistencyModelPipeline 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": { "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), "steps": ("INT", {"default": 4, "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 = ("IMAGE",) FUNCTION = "sample" CATEGORY = "sampling" def sample(self, seed, steps, cfg, positive_prompt, size, num_images): if self.pipe is None: self.pipe = LatentConsistencyModelPipeline.from_pretrained( pretrained_model_name_or_path="C:\Matrix\Data\Models\LCM_Dreamshaper_v7", local_files_only=True, scheduler=self.scheduler`
notes.py: `from .lcm.lcm_scheduler import LCMScheduler from .lcm.lcm_pipeline import LatentConsistencyModelPipeline 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: