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[pull] master from Comfy-Org:master #861

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Merged
pull merged 2 commits into from
Jun 2, 2026
Merged

[pull] master from Comfy-Org:master #861

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e9207aa
fix (MultiGPU): prevent freeze on manual abort when using MultiGPU CF…
quasar-of-mikus Jun 2, 2026
dc10c01
PiD: Add SDXL and QwenImage (#14240)
kijai Jun 2, 2026
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2 changes: 2 additions & 0 deletions comfy/multigpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,8 @@ def _worker_loop(self, device: torch.device, work_q: queue.Queue, result_q: queu
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))

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14 changes: 11 additions & 3 deletions comfy_extras/nodes_pid.py
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Expand Up @@ -21,8 +21,8 @@ def define_schema(cls) -> io.Schema:
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.",
Expand All @@ -36,9 +36,17 @@ def execute(cls, positive, latent, latent_format: str, degrade_sigma: float) ->
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},
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