| import sys |
| import torch |
| import numpy |
| import scipy.stats |
| from comfy.samplers import SchedulerHandler, SCHEDULER_HANDLERS, SCHEDULER_NAMES |
|
|
| MIN_FLOAT = -sys.float_info.max |
| MAX_FLOAT = sys.float_info.max |
|
|
| def beta_57_scheduler(model_sampling, steps, alpha=0.5, beta=0.7): |
| total_timesteps = (len(model_sampling.sigmas) - 1) |
| ts = 1 - numpy.linspace(0, 1, steps, endpoint=False) |
| ts = numpy.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps) |
|
|
| sigs = [] |
| last_t = -1 |
| for t in ts: |
| if t != last_t: |
| sigs += [float(model_sampling.sigmas[int(t)])] |
| last_t = t |
| sigs += [0.0] |
| return torch.FloatTensor(sigs) |
|
|
| scheduler_name = "beta_57" |
| if scheduler_name not in SCHEDULER_HANDLERS: |
| scheduler_handler = SchedulerHandler(handler=beta_57_scheduler, use_ms=True) |
| SCHEDULER_HANDLERS[scheduler_name] = scheduler_handler |
| if scheduler_name not in SCHEDULER_NAMES: |
| SCHEDULER_NAMES.append(scheduler_name) |
|
|
| class StaticFloat: |
| @classmethod |
| def INPUT_TYPES(s): |
| return { |
| "required": { |
| "value": ("FLOAT", {"default": 1.0, "min": MIN_FLOAT, "max": MAX_FLOAT, "step": 0.01}), |
| }, |
| } |
|
|
| RETURN_TYPES = ("FLOAT",) |
| CATEGORY = "utils/primitive" |
| FUNCTION = "set_float" |
|
|
| def set_float(self, value: float): |
| return (value,) |
|
|
| NODE_CLASS_MAPPINGS = { |
| "StaticFloat": StaticFloat, |
| } |
| NODE_DISPLAY_NAME_MAPPINGS = { |
| "StaticFloat": "Static Float Primitive", |
| } |
|
|
