mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-03-01 23:37:33 +08:00
92 lines
3.1 KiB
Python
92 lines
3.1 KiB
Python
import hashlib
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import torch
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import logging
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from comfy.cli_args import args
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from PIL import ImageFile, UnidentifiedImageError
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def conditioning_set_values(conditioning, values={}, append=False):
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c = []
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for t in conditioning:
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n = [t[0], t[1].copy()]
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for k in values:
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val = values[k]
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if append:
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old_val = n[1].get(k, None)
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if old_val is not None:
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val = old_val + val
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n[1][k] = val
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c.append(n)
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return c
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def conditioning_set_values_with_timestep_range(conditioning, values={}, start_percent=0.0, end_percent=1.0):
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"""
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Apply values to conditioning only during [start_percent, end_percent], keeping the
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original conditioning active outside that range. Respects existing per-entry ranges.
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"""
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if start_percent > end_percent:
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logging.warning(f"start_percent ({start_percent}) must be <= end_percent ({end_percent})")
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return conditioning
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EPS = 1e-5 # the sampler gates entries with strict > / <, shift boundaries slightly to ensure only one conditioning is active per timestep
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c = []
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for t in conditioning:
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cond_start = t[1].get("start_percent", 0.0)
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cond_end = t[1].get("end_percent", 1.0)
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intersect_start = max(start_percent, cond_start)
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intersect_end = min(end_percent, cond_end)
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if intersect_start >= intersect_end: # no overlap: emit unchanged
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c.append(t)
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continue
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if intersect_start > cond_start: # part before the requested range
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c.extend(conditioning_set_values([t], {"start_percent": cond_start, "end_percent": intersect_start - EPS}))
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c.extend(conditioning_set_values([t], {**values, "start_percent": intersect_start, "end_percent": intersect_end}))
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if intersect_end < cond_end: # part after the requested range
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c.extend(conditioning_set_values([t], {"start_percent": intersect_end + EPS, "end_percent": cond_end}))
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return c
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def pillow(fn, arg):
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prev_value = None
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try:
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x = fn(arg)
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except (OSError, UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445, also fixes ComfyUI issue #3416
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prev_value = ImageFile.LOAD_TRUNCATED_IMAGES
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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x = fn(arg)
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finally:
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if prev_value is not None:
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ImageFile.LOAD_TRUNCATED_IMAGES = prev_value
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return x
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def hasher():
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hashfuncs = {
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"md5": hashlib.md5,
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"sha1": hashlib.sha1,
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"sha256": hashlib.sha256,
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"sha512": hashlib.sha512
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}
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return hashfuncs[args.default_hashing_function]
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def string_to_torch_dtype(string):
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if string == "fp32":
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return torch.float32
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if string == "fp16":
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return torch.float16
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if string == "bf16":
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return torch.bfloat16
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def image_alpha_fix(destination, source):
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if destination.shape[-1] < source.shape[-1]:
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source = source[...,:destination.shape[-1]]
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elif destination.shape[-1] > source.shape[-1]:
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destination = torch.nn.functional.pad(destination, (0, 1))
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destination[..., -1] = 1.0
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return destination, source
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