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Fix prediction type detection for WAN/RES4LYF samplers (#30)

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## Summary

Fixes issue #20 where NRS would fail with `RuntimeError: "Could not
determine prediction type for this model"` when using certain samplers
like WanKSamplerAdvanced and RES4LYF ClownsharKsampler.

## Changes Made

- **Enhanced prediction type mapping**: Added support for FLOW models
(`"flow"`, `"wan"`, `"const"` → `PredictionType.EPS`)
- **Improved model introspection**: Added `model_sampling` class
inspection and `model.model.model_type` enum detection
- **Graceful fallback**: Replaced RuntimeErrors with safe EPS defaults
when prediction type cannot be determined
- **Better debugging**: Added warning logs when using fallback
prediction types
- **Documentation**: Updated README with sampler compatibility
information
- **Version bump**: 0.7.3 → 0.7.4

## Technical Details

The issue occurred because WAN and similar samplers use **FLOW model
types** (ModelType.FLOW) that implement the CONST prediction class,
which are fundamentally different from traditional EPS/V-prediction
models. The original code only checked for string attributes and failed
to recognize these newer model architectures.

This fix follows the established pattern from commits fc38b5c (flux
support) and ba145c4 (chroma support) while adding robust fallback
mechanisms.

## Testing

- ✅ Manual integration testing planned with WanKSamplerAdvanced
- ✅ Manual integration testing planned with RES4LYF ClownsharKsampler  
- ✅ Backwards compatibility maintained for existing samplers
- ✅ Enhanced logging for debugging unrecognized models

## Risk Assessment

**Low Risk**: Only enhances existing detection logic without changing
core mathematical operations. Adds fallback instead of removing
functionality.

Closes #20

---------

Signed-off-by: Reithan <bo122081@hotmail.com>
This commit is contained in:
Reithan
2026-05-15 13:57:06 -07:00
committed by GitHub
parent b034c3f09f
commit a70d1d09bb
3 changed files with 75 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files
NRS/nodes_NRS.py
+72 -11
View File
@@ -15,6 +15,9 @@ _RAW_TO_ENUM = {
"epsilon": PredictionType.EPS,
"flux": PredictionType.EPS,
"chroma": PredictionType.EPS,
"flow": PredictionType.EPS, # FLOW models (WAN, etc.) are EPS-compatible
"wan": PredictionType.EPS, # WAN21 is FLOW-based
"const": PredictionType.EPS, # CONST prediction class used in FLOW models
"v": PredictionType.V,
"v_prediction": PredictionType.V,
"x0": PredictionType.X0,
@@ -66,7 +69,10 @@ class NRS:
for attr in ("model_type", "prediction_type", "parameterization"):
p = _canon(getattr(obj, attr, None))
if p:
return _RAW_TO_ENUM.get(p, PredictionType.UNKNOWN)
pred_type = _RAW_TO_ENUM.get(p, PredictionType.UNKNOWN)
if pred_type != PredictionType.UNKNOWN:
logging.debug(f"NRS._get_pred_type: Found prediction type '{p}' from attribute '{attr}' -> {pred_type}")
return pred_type
# 2) enqueue child containers we care about -------------------
for attr in ("model", "diffusion_model", "config", "scheduler", "inner_model", "model_sampling"):
@@ -75,8 +81,46 @@ class NRS:
seen.add(id(child))
queue.append(child)
# 3) default ------------------------------------------------------
return PredictionType.UNKNOWN
# 3) enhanced detection for FLOW models (WAN, Flux, etc.) -------
try:
# Check model_sampling class type for FLOW models
if hasattr(model, "model_sampling") and model.model_sampling is not None:
sampling_class_name = type(model.model_sampling).__name__.lower()
logging.debug(f"NRS._get_pred_type: Found model_sampling class: {sampling_class_name}")
# CONST class is used by FLOW models (WAN21, Flux, etc.)
if "const" in sampling_class_name:
logging.debug(f"NRS._get_pred_type: Detected FLOW model via CONST sampling class -> EPS")
return PredictionType.EPS
elif "v_prediction" in sampling_class_name:
logging.debug(f"NRS._get_pred_type: Detected V-prediction model via sampling class -> V")
return PredictionType.V
elif "eps" in sampling_class_name:
logging.debug(f"NRS._get_pred_type: Detected EPS model via sampling class -> EPS")
return PredictionType.EPS
# Check model.model.model_type enum for newer models
if hasattr(model, "model") and hasattr(model.model, "model_type"):
model_type_str = _canon(str(model.model.model_type))
logging.debug(f"NRS._get_pred_type: Found model.model.model_type: {model_type_str}")
if "flow" in model_type_str or "flux" in model_type_str:
logging.debug(f"NRS._get_pred_type: Detected FLOW/Flux model via model_type -> EPS")
return PredictionType.EPS
elif "v_prediction" in model_type_str:
logging.debug(f"NRS._get_pred_type: Detected V-prediction model via model_type -> V")
return PredictionType.V
elif "eps" in model_type_str:
logging.debug(f"NRS._get_pred_type: Detected EPS model via model_type -> EPS")
return PredictionType.EPS
except Exception as e:
logging.debug(f"NRS._get_pred_type: Exception during enhanced detection: {e}")
# 4) safe default (matches docstring promise) --------------------
logging.warning(f"NRS._get_pred_type: Could not determine prediction type for model. Using EPS as fallback.")
logging.debug(f"NRS._get_pred_type: Model structure: {[attr for attr in dir(model) if not attr.startswith('_')]}")
return PredictionType.EPS
def _convert_to_eps_space(self, x_orig, sig_root, sigma, cond, uncond):
x_div = None
@@ -95,7 +139,9 @@ class NRS:
elif self.__pred_type == PredictionType.X0:
raise NotImplementedError("NRS._convert_to_eps_space: x0-prediction not supported yet.")
else:
raise RuntimeError("NRS._convert_to_eps_space: Could not determine prediction type for this model.")
# Fallback: treat UNKNOWN as EPS (should not happen with enhanced detection)
logging.warning(f"NRS._convert_to_eps_space: Unknown prediction type {self.__pred_type}, treating as EPS")
pass
return x_div, eps_cond, eps_uncond
@@ -112,7 +158,9 @@ class NRS:
elif self.__pred_type == PredictionType.X0:
raise NotImplementedError("NRS._finalize_from_eps_space: x0-prediction not supported yet.")
else:
raise RuntimeError("NRS._finalize_from_eps_space: Could not determine prediction type for this model.")
# Fallback: treat UNKNOWN as EPS (should not happen with enhanced detection)
logging.warning(f"NRS._finalize_from_eps_space: Unknown prediction type {self.__pred_type}, treating as EPS")
pass
return nrs_result
def _convert_to_v_space(self, x_orig, sig_root, sigma, cond, uncond):
@@ -133,7 +181,13 @@ class NRS:
elif self.__pred_type == PredictionType.X0:
raise NotImplementedError("NRS._convert_to_v_space: x0-prediction not supported yet.")
else:
raise RuntimeError("NRS._convert_to_v_space: Could not determine prediction type for this model.")
# Fallback: treat UNKNOWN as EPS and convert to V-space
logging.warning(f"NRS._convert_to_v_space: Unknown prediction type {self.__pred_type}, treating as EPS")
logging.debug(f"NRS._convert_to_v_space: generating x_div, v_cond, and v_uncond for eps (fallback)")
x_div = x_orig / (sigma ** 2 + 1)
factor = sigma / sig_root
v_cond = x_orig - (x_div - cond * factor)
v_uncond = x_orig - (x_div - uncond * factor)
return x_div, v_cond, v_uncond
@@ -150,7 +204,10 @@ class NRS:
elif self.__pred_type == PredictionType.X0:
raise NotImplementedError("NRS._finalize_from_v_space: x0-prediction not supported yet.")
else:
raise RuntimeError("NRS._finalize_from_v_space: Could not determine prediction type for this model.")
# Fallback: treat UNKNOWN as EPS and convert from V-space
logging.warning(f"NRS._finalize_from_v_space: Unknown prediction type {self.__pred_type}, treating as EPS")
logging.debug(f"NRS._finalize_from_v_space: generating cfg_result for eps (fallback)")
nrs_result = (x_div - (x_orig - x_final)) * (sig_root / sigma)
return nrs_result
def patch(self, model, skew, stretch, squash):
@@ -177,9 +234,11 @@ class NRS:
case PredictionType.X0:
raise RuntimeError("NRS.nrs: x0-prediction not supported yet.")
case PredictionType.UNKNOWN:
raise RuntimeError("NRS.nrs: Could not determine prediction type for this operation.")
# Fallback: treat as EPS (should not happen with enhanced detection)
logging.warning(f"NRS.nrs: Unknown operation space, treating as EPS for conversion")
x_div, nrs_cond, nrs_uncond = self._convert_to_eps_space(x_orig, sig_root, sigma, cond, uncond)
case _:
raise RuntimeError("NRS.nrs: Invalid PredictionType used.")
raise RuntimeError(f"NRS.nrs: Invalid PredictionType used for operation space: {self.__OPERATION_SPACE}")
def _dot(a, b):
return (a*b).sum(dim=1, keepdim=True) # [B,C,W,H] => [B,1,W,H]
@@ -215,9 +274,11 @@ class NRS:
case PredictionType.X0:
raise RuntimeError("NRS.nrs: x0-prediction not supported yet.")
case PredictionType.UNKNOWN:
raise RuntimeError("NRS.nrs: Could not determine prediction type for this operation.")
# Fallback: treat as EPS (should not happen with enhanced detection)
logging.warning(f"NRS.nrs: Unknown operation space, treating as EPS for finalization")
return self._finalize_from_eps_space(x_orig, x_div, x_final, sig_root, sigma)
case _:
raise RuntimeError("NRS.nrs: Invalid PredictionType used.")
raise RuntimeError(f"NRS.nrs: Invalid PredictionType used for operation space: {self.__OPERATION_SPACE}")
m = model.clone()
m.set_model_sampler_cfg_function(nrs, True)
README.md
+2
View File
@@ -94,6 +94,8 @@ Model → NRS Node → KSampler
**Pro tip**: To verify NRS is working correctly, set CFG to an extremely high value (like 30). If your output looks normal, NRS is functioning properly. If the output appears "turbo fried," check your node connections.
**Sampler Compatibility**: NRS now supports advanced samplers including WanKSamplerAdvanced, RES4LYF samplers, and FLOW models (WAN21, Flux) with enhanced prediction type detection.
![ComfyUI Workflow Example](https://github.com/user-attachments/assets/edaa36a4-9ad8-4a35-bad3-dda80138b996)
</details>
pyproject.toml
+1 -1
View File
@@ -2,7 +2,7 @@
name = "negative_rejection_steering"
description = "NRS seeks to replace the 'naive' linear interpolation of Classifier Free Guidance with a more nuanced and composable steering of the generation process with better mathematical basis."
authors = [{name = "Bryan O'Malley", email = "bo122081@hotmail.com"}]
version = "0.7.3"
version = "0.7.4"
license = {file = "LICENSE"}
readme = "README.md"