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Merge branch 'Comfy-Org:master' into master

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azazeal04 2026-06-03 11:01:47 +02:00 committed by GitHub
commit 83f1dfed27
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4 changed files with 27 additions and 23 deletions
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32
comfy/ldm/flux/math.py
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@ -4,7 +4,7 @@ from torch import Tensor
from comfy.ldm.modules.attention import optimized_attention
import comfy.model_management
import logging
import comfy.quant_ops
def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor:
@ -44,21 +44,15 @@ def _apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)
try:
import comfy.quant_ops
q_apply_rope = comfy.quant_ops.ck.apply_rope
q_apply_rope1 = comfy.quant_ops.ck.apply_rope1
def apply_rope(xq, xk, freqs_cis):
if comfy.model_management.in_training:
return _apply_rope(xq, xk, freqs_cis)
else:
return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)
def apply_rope1(x, freqs_cis):
if comfy.model_management.in_training:
return _apply_rope1(x, freqs_cis)
else:
return q_apply_rope1(x, freqs_cis)
except:
logging.warning("No comfy kitchen, using old apply_rope functions.")
apply_rope = _apply_rope
apply_rope1 = _apply_rope1
def apply_rope(xq, xk, freqs_cis):
if comfy.model_management.in_training:
return _apply_rope(xq, xk, freqs_cis)
else:
return comfy.quant_ops.ck.apply_rope(xq, xk, freqs_cis)
def apply_rope1(x, freqs_cis):
if comfy.model_management.in_training:
return _apply_rope1(x, freqs_cis)
else:
return comfy.quant_ops.ck.apply_rope1(x, freqs_cis)

2
comfy/multigpu.py
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@ -54,6 +54,8 @@ class MultiGPUThreadPool:
try:
result = fn(*args, **kwargs)
result_q.put((result, None))
except comfy.model_management.InterruptProcessingException as e:
result_q.put((None, e))
except Exception as e:
result_q.put((None, e))

14
comfy_extras/nodes_pid.py
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@ -21,8 +21,8 @@ class PiDConditioning(io.ComfyNode):
inputs=[
io.Conditioning.Input("positive"),
io.Latent.Input("latent", tooltip="latent (from VAEEncode or a KSampler)."),
io.Combo.Input("latent_format", options=["flux", "sd3"], default="flux",
tooltip="Flux1 and Flux2 latents auto-detected from channel dim, sd3 has to be selected manually."),
io.Combo.Input("latent_format", options=["flux", "sd3", "sdxl", "qwenimage"], default="flux",
tooltip="Flux1 (16-ch) and Flux2 (128-ch) latents are auto-detected from channel dim under 'flux'. For SD3 (16-ch), SDXL (4-ch), or QwenImage (16-ch), select manually."),
io.Float.Input(
"degrade_sigma", default=0.0, min=0.0, max=1.0, step=0.01,
tooltip="0 = clean latent. Increase to denoise corrupted latent outputs.",
@ -36,9 +36,17 @@ class PiDConditioning(io.ComfyNode):
samples = latent["samples"]
if latent_format == "flux":
fmt_cls = comfy.latent_formats.Flux2 if samples.shape[1] == 128 else comfy.latent_formats.Flux
else:
elif latent_format == "sd3":
fmt_cls = comfy.latent_formats.SD3
elif latent_format == "sdxl":
fmt_cls = comfy.latent_formats.SDXL
elif latent_format == "qwenimage":
fmt_cls = comfy.latent_formats.Wan21
else:
raise ValueError(f"Unknown latent_format: {latent_format}")
lq_latent = fmt_cls().process_in(samples)
if lq_latent.ndim == 5:
lq_latent = lq_latent[:, :, 0]
sigma_t = torch.tensor([float(degrade_sigma)], dtype=torch.float32)
return io.NodeOutput(node_helpers.conditioning_set_values(
positive, {"lq_latent": lq_latent, "degrade_sigma": sigma_t},

2
requirements.txt
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@ -23,7 +23,7 @@ SQLAlchemy>=2.0.0
filelock
av>=16.0.0
comfy-kitchen==0.2.10
comfy-aimdo==0.4.7
comfy-aimdo==0.4.8
requests
simpleeval>=1.0.0
blake3