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87 changes: 69 additions & 18 deletions modules/node_wrapper.py
Original file line number Diff line number Diff line change
@@ -1,5 +1,4 @@
"""

Custom nodes for SDXL in ComfyUI

MIT License
Expand All @@ -23,7 +22,6 @@
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

"""

import nodes
Expand Down Expand Up @@ -76,11 +74,14 @@ def sdxl_sampler(base_model, base_positive, base_negative, latent_image, noise_s
sampler_name, scheduler, refiner_model=None, refiner_positive=None, refiner_negative=None,
base_ratio=0.8, denoise=1.0, cfg_method=None, dynamic_base_cfg=0.0, dynamic_refiner_cfg=0.0,
refiner_detail_boost=0.0):
"""
SDXL sampler wrapper that optionally uses a refiner model for later steps.
Returns the generated latent tensor.
"""
if base_model is None:
return None

has_refiner_model = refiner_model is not None

base_steps = int(steps * (base_ratio + 0.0001)) if has_refiner_model else steps
refiner_steps = max(0, steps - base_steps)

Expand All @@ -91,25 +92,75 @@ def sdxl_sampler(base_model, base_positive, base_negative, latent_image, noise_s
return latent_image

if refiner_steps == 0 or not has_refiner_model:
result = sdxl_ksampler(base_model, None, noise_seed, base_steps, 0, cfg, sampler_name,
scheduler, base_positive, base_negative, None, None,
latent_image, denoise=denoise, disable_noise=False, start_step=0, last_step=steps,
force_full_denoise=True, dynamic_base_cfg=dynamic_base_cfg, cfg_method=cfg_method)
result = sdxl_ksampler(
base_model, None, noise_seed, base_steps, 0, cfg, sampler_name, scheduler,
base_positive, base_negative, None, None, latent_image, denoise=denoise,
disable_noise=False, start_step=0, last_step=steps, force_full_denoise=True,
dynamic_base_cfg=dynamic_base_cfg, cfg_method=cfg_method
)
else:
result = sdxl_ksampler(base_model, refiner_model, noise_seed, base_steps, refiner_steps, cfg, sampler_name,
scheduler, base_positive, base_negative, refiner_positive, refiner_negative,
latent_image, denoise=denoise, disable_noise=False,
start_step=0, last_step=steps, force_full_denoise=True,
dynamic_base_cfg=dynamic_base_cfg, dynamic_refiner_cfg=dynamic_refiner_cfg,
cfg_method=cfg_method, refiner_detail_boost=refiner_detail_boost)

result = sdxl_ksampler(
base_model, refiner_model, noise_seed, base_steps, refiner_steps, cfg, sampler_name, scheduler,
base_positive, base_negative, refiner_positive, refiner_negative, latent_image, denoise=denoise,
disable_noise=False, start_step=0, last_step=steps, force_full_denoise=True,
dynamic_base_cfg=dynamic_base_cfg, dynamic_refiner_cfg=dynamic_refiner_cfg,
cfg_method=cfg_method, refiner_detail_boost=refiner_detail_boost
)

# Important for legacy Searge code: unwrap first element
return result[0]

@staticmethod
def common_sampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent,
denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
result = nodes.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent, denoise=denoise, disable_noise=disable_noise, start_step=start_step,
last_step=last_step, force_full_denoise=force_full_denoise)

"""
Common sampler wrapper; returns the generated latent tensor.
"""
result = nodes.common_ksampler(
model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent,
denoise=denoise, disable_noise=disable_noise, start_step=start_step,
last_step=last_step, force_full_denoise=force_full_denoise
)
# Important for legacy Searge code: unwrap first element
return result[0]


# --- Begin Blend compatibility shim (ComfyUI ≥ mid-2025)
# Searge's code calls: NodeWrapper.image_blend.blend_images(a, b, weight, mode)
# Newer Comfy builds may expose different method names. This shim restores that call.

try:
_ib = NodeWrapper.image_blend
except Exception:
_ib = None

if _ib is not None and not hasattr(_ib, "blend_images"):
def _searge_blend_images(img_a, img_b, weight, mode="normal"):
"""
Compatibility wrapper so legacy calls image_blend.blend_images(...) still work.
Tries new APIs first; falls back to a manual blend for 'normal' mode.
"""
# Prefer a native method if present
if hasattr(_ib, "blend") and callable(_ib.blend):
return _ib.blend(img_a, img_b, weight, mode)
if hasattr(_ib, "execute") and callable(_ib.execute):
return _ib.execute(img_a, img_b, weight, mode)
if callable(_ib):
try:
return _ib(img_a, img_b, weight, mode)
except TypeError:
return _ib(img_a, img_b, weight)

# Fallback: simple linear blend for "normal" mode
if mode not in (None, "normal"):
raise AttributeError("Blend mode not supported by compatibility shim")
try:
# torch ops keep device/dtype consistent with upstream tensors
import torch
w = float(weight) if isinstance(weight, (int, float)) else weight
return img_a * (1.0 - w) + img_b * w
except Exception as e:
raise AttributeError(f"Blend compatibility shim failed: {e}")

setattr(_ib, "blend_images", _searge_blend_images)
# --- End Blend compatibility shim
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