blepping
a7855cde42
Allow passing advanced noise sampler parameters Fix masks not being respected in sharpen operation
68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
from __future__ import annotations
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import math
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from comfy import model_management
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def ensure_model(model, *, mode: str | None = None):
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if not mode or mode == "disable":
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# Ensure model mode disabled.
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return
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mp = model.inner_model.model_patcher
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lm = model_management.LoadedModel(mp)
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found_lm = None
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for list_lm in model_management.current_loaded_models:
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if lm == list_lm:
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found_lm = list_lm
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break
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if getattr(found_lm, "currently_used", False):
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# Model already exists and appears to be loaded.
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return
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if found_lm is not None:
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lm = found_lm
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mp = lm.model
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if mode.startswith("normal"):
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if mode == "normal_unload":
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model_management.unload_all_models()
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model_management.load_models_gpu((mp,))
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return
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model_management.unload_all_models()
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if mode == "lowvram":
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# Logic from comfy.model_management.load_models_gpu
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min_inference_memory = model_management.minimum_inference_memory()
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minimum_memory_required = max(
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min_inference_memory,
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model_management.extra_reserved_memory(),
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)
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loaded_memory = lm.model_loaded_memory()
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current_free_mem = model_management.get_free_memory(lm.device) + loaded_memory
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lowvram_model_memory = max(
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64 * 1024 * 1024,
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(current_free_mem - minimum_memory_required),
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min(
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current_free_mem * model_management.MIN_WEIGHT_MEMORY_RATIO,
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current_free_mem - model_management.minimum_inference_memory(),
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),
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)
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lowvram_model_memory = max(0.1, lowvram_model_memory - loaded_memory)
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elif mode == "novram":
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lowvram_model_memory = 0.1
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else:
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raise ValueError("Bad ensure_model_mode")
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lm.model_load(lowvram_model_memory)
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model_management.current_loaded_models.insert(0, lm)
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def fallback(val, default, *, exclude=None, default_is_fun=False):
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return val if val is not exclude else (default() if default_is_fun else default)
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def scale_dim(n, factor=1.0, *, increment=64) -> int:
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return math.ceil((n * factor) / increment) * increment
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def sigma_to_float(sigma):
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return sigma.detach().cpu().max().item()
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