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PiD: Add SDXL and QwenImage (#14240)
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Jukka Seppänen 2026-06-02 22:40:49 +03:00 committed by GitHub
parent e9207aa7cc
commit dc10c0133e
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1 changed files with 11 additions and 3 deletions
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14
comfy_extras/nodes_pid.py
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@ -21,8 +21,8 @@ class PiDConditioning(io.ComfyNode):
inputs=[ inputs=[
io.Conditioning.Input("positive"), io.Conditioning.Input("positive"),
io.Latent.Input("latent", tooltip="latent (from VAEEncode or a KSampler)."), io.Latent.Input("latent", tooltip="latent (from VAEEncode or a KSampler)."),
io.Combo.Input("latent_format", options=["flux", "sd3"], default="flux", io.Combo.Input("latent_format", options=["flux", "sd3", "sdxl", "qwenimage"], default="flux",
tooltip="Flux1 and Flux2 latents auto-detected from channel dim, sd3 has to be selected manually."), 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( io.Float.Input(
"degrade_sigma", default=0.0, min=0.0, max=1.0, step=0.01, "degrade_sigma", default=0.0, min=0.0, max=1.0, step=0.01,
tooltip="0 = clean latent. Increase to denoise corrupted latent outputs.", tooltip="0 = clean latent. Increase to denoise corrupted latent outputs.",
@ -36,9 +36,17 @@ class PiDConditioning(io.ComfyNode):
samples = latent["samples"] samples = latent["samples"]
if latent_format == "flux": if latent_format == "flux":
fmt_cls = comfy.latent_formats.Flux2 if samples.shape[1] == 128 else comfy.latent_formats.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 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) 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) sigma_t = torch.tensor([float(degrade_sigma)], dtype=torch.float32)
return io.NodeOutput(node_helpers.conditioning_set_values( return io.NodeOutput(node_helpers.conditioning_set_values(
positive, {"lq_latent": lq_latent, "degrade_sigma": sigma_t}, positive, {"lq_latent": lq_latent, "degrade_sigma": sigma_t},