Add MagicLatentAdapter node (experimental support FLUX/Qwen models) (#10)

Introduces MagicLatentAdapter (experimental support FLUX/Qwen models) for generating or adapting latents to match model expectations. Adds mask alignment utilities to CADE nodes for robust mask blending, improves Gaussian blur to support 5D tensors, and updates preset defaults for improved results. Documentation and workflow updated to reflect new node and features.

README.md

@@ -164,14 +164,19 @@ MagicNodes/
 │  │  ├─ mg_controlfusion_easy.py
 │  │  └─ mg_supersimple_easy.py
 │  │  └─ preset_loader.py
-│  └─ hard/
-│     ├─ mg_cade25.py
-│     ├─ mg_controlfusion.py
-│     ├─ mg_tde2.py
-│     ├─ mg_upscale_module.py
-│     ├─ mg_ids.py
-│     └─ mg_zesmart_sampler_v1_1.py
-│ 
+│  ├─ hard/
+│  │  ├─ mg_cade25.py
+│  │  ├─ mg_controlfusion.py
+│  │  ├─ mg_tde2.py
+│  │  ├─ mg_upscale_module.py
+│  │  ├─ mg_ids.py
+│  │  └─ mg_zesmart_sampler_v1_1.py
+│  │
+│  ├─ mg_combinode.py
+│  ├─ mg_latent_adapter.py
+│  ├─ mg_sagpu_attention.py
+│  └─  mg_seed_latent.py
+│  
 ├─ pressets/
 │  ├─ mg_cade25.cfg
 │  └─ mg_controlfusion.cfg
@@ -240,7 +245,14 @@ Depth models (Depth Anything v2)
   - Keep dimensions multiples of 8; recommended starting sizes around ~672x944 (other aspect ratios work). With SuperSimple’s default scale, step 4 lands near ~3688x5192.
   - `mix_image=True` encodes the provided image via VAE and adds noise: a soft way to keep global structure while allowing refinement downstream.
   - For run-to-run comparability, hold your sampler seed fixed (in SuperSimple/CADE). SeedLatent itself does not expose a seed; variation is primarily controlled by the sampler seed.
-  - Batch friendly: `batch_size>1` produces independent latents of the chosen size.
+- Batch friendly: `batch_size>1` produces independent latents of the chosen size.
+
+### Magic Latent Adapter (mg_latent_adapter.py) !experimental!
+- Purpose: small adapter node that generates or adapts a `LATENT` to match the target model’s latent format (channels and dimensions), including 5D layouts (`NCDHW`) when required. Two modes: `generate` (make a fresh latent aligned to VAE stride) and `adapt` (reshape/channel‑match an existing latent).
+- How it works: relies on Comfy’s `fix_empty_latent_channels` and reads the model’s `latent_format` to adjust channel count; aligns spatial size to VAE stride; handles 4D (`NCHW`) and 5D (`NCDHW`).
+- Experimental: added to ease early, experimental support for FLUX/Qwen‑like models by reducing shape/dimension friction. Still evolving; treat as opt‑in.
+- Usage: place before CADE/your sampler. In `generate` mode you can also enable image mixing via VAE; in `adapt` mode feed any upstream `LATENT` and the current `MODEL`. A simple family switch (`auto / SD / SDXL / FLUX`) controls stride fallback when VAE isn’t provided.
+- Notes: quality with FLUX/Qwen models also depends on using the proper text encoders/conditioning nodes for those families; this adapter only solves latent shapes, not conditioning mismatches.
 
 ## Dependencies (Why These Packages)
 - transformers — used by CADE for CLIPSeg (CIDAS/clipseg-rd64-refined) to build text‑driven masks (e.g., face/hands). If missing, CLIPSeg is disabled gracefully.

__init__.py

@@ -25,6 +25,7 @@ from .mod.hard.mg_cade25 import ComfyAdaptiveDetailEnhancer25
 from .mod.hard.mg_ids import IntelligentDetailStabilizer
 from .mod.mg_seed_latent import MagicSeedLatent
 from .mod.mg_sagpu_attention import PatchSageAttention
+from .mod.mg_latent_adapter import MagicLatentAdapter
 from .mod.hard.mg_controlfusion import MG_ControlFusion
 from .mod.hard.mg_zesmart_sampler_v1_1 import MG_ZeSmartSampler
 from .mod.easy.mg_cade25_easy import CADEEasyUI as ComfyAdaptiveDetailEnhancer25_Easy
@@ -59,6 +60,7 @@ NODE_CLASS_MAPPINGS = {
     "MagicNodesCombiNode": MagicNodesCombiNode,
     "MagicSeedLatent": MagicSeedLatent,
     "PatchSageAttention": PatchSageAttention,
+    "MagicLatentAdapter": MagicLatentAdapter, # experimental
     "MagicUpscaleModule": MagicUpscaleModule,
     "ComfyAdaptiveDetailEnhancer25": ComfyAdaptiveDetailEnhancer25,
     "IntelligentDetailStabilizer": IntelligentDetailStabilizer,
@@ -73,8 +75,8 @@ NODE_CLASS_MAPPINGS = {
 NODE_DISPLAY_NAME_MAPPINGS = {
     "MagicNodesCombiNode": "MG_CombiNode",
     "MagicSeedLatent": "MG_SeedLatent",
-    # TDE removed from this build
     "PatchSageAttention": "MG_AccelAttention",
+    "MagicLatentAdapter": "MG_LatentAdapter", # experimental
     "ComfyAdaptiveDetailEnhancer25": "MG_CADE 2.5",
     "MG_ControlFusion": "MG_ControlFusion",
     "MG_ZeSmartSampler": "MG_ZeSmartSampler",

mod/easy/mg_cade25_easy.py

@@ -236,6 +236,12 @@ def _clipseg_build_mask(image_bhwc: torch.Tensor,
         # Prepare preview image (CPU PIL)
         target = int(max(16, min(1024, preview)))
         img = image_bhwc.detach().to('cpu')
+        if img.ndim == 5:
+            # squeeze depth if present
+            if img.shape[1] == 1:
+                img = img[:, 0]
+            else:
+                img = img[:, 0]
         B, H, W, C = img.shape
         x = img[0].movedim(-1, 0).unsqueeze(0)  # 1,C,H,W
         x = F.interpolate(x, size=(target, target), mode='bilinear', align_corners=False)
@@ -1157,17 +1163,31 @@ def _soft_symmetry_blend(image_bhwc: torch.Tensor,
 
 
 def _gaussian_blur_nchw(x: torch.Tensor, sigma: float = 1.0, radius: int = 1) -> torch.Tensor:
-    """Lightweight depthwise Gaussian blur for NCHW tensors.
+    """Lightweight depthwise Gaussian blur for NCHW or NCDHW tensors.
     Uses reflect padding and a normalized kernel built by _gaussian_kernel.
     """
     if radius <= 0:
         return x
     ksz = radius * 2 + 1
     kernel = _gaussian_kernel(ksz, sigma, device=x.device).to(dtype=x.dtype)
-    kernel = kernel.repeat(x.shape[1], 1, 1).unsqueeze(1)  # [C,1,K,K]
-    x_pad = F.pad(x, (radius, radius, radius, radius), mode='reflect')
-    y = F.conv2d(x_pad, kernel, padding=0, groups=x.shape[1])
-    return y
+    # Support 5D by folding depth into batch
+    if x.ndim == 5:
+        b, c, d, h, w = x.shape
+        x2 = x.permute(0, 2, 1, 3, 4).reshape(b * d, c, h, w)
+        k = kernel.repeat(c, 1, 1).unsqueeze(1)  # [C,1,K,K]
+        x_pad = F.pad(x2, (radius, radius, radius, radius), mode='reflect')
+        y2 = F.conv2d(x_pad, k, padding=0, groups=c)
+        y = y2.reshape(b, d, c, h, w).permute(0, 2, 1, 3, 4)
+        return y
+    # 4D path
+    if x.ndim == 4:
+        b, c, h, w = x.shape
+        k = kernel.repeat(c, 1, 1).unsqueeze(1)  # [C,1,K,K]
+        x_pad = F.pad(x, (radius, radius, radius, radius), mode='reflect')
+        y = F.conv2d(x_pad, k, padding=0, groups=c)
+        return y
+    # Fallback: return input if unexpected dims
+    return x
 
 
 def _letterbox_nchw(x: torch.Tensor, target: int, pad_val: float = 114.0 / 255.0) -> torch.Tensor:
@@ -1452,6 +1472,8 @@ def _wrap_model_with_guidance(model, guidance_mode: str, rescale_multiplier: flo
 
     m.set_model_sampler_cfg_function(cfg_func, disable_cfg1_optimization=True)
 
+    # Note: ControlNet class-label injection wrapper removed to keep CADE neutral.
+
     # Optional directional post-mix (Muse Blend), global, no ONNX
     if bool(mahiro_plus_enable):
         s_clamp = float(max(0.0, min(1.0, mahiro_plus_strength)))
@@ -1736,6 +1758,38 @@ def _build_cf_edge_mask_from_step(image_bhwc: torch.Tensor, preset_step: str) ->
         return ed.unsqueeze(0).unsqueeze(-1)
     except Exception:
         return None
+
+def _mask_to_like(mask_bhw1: torch.Tensor, like_bhwc: torch.Tensor) -> torch.Tensor:
+    try:
+        if mask_bhw1 is None or like_bhwc is None:
+            return mask_bhw1
+        if mask_bhw1.ndim != 4 or like_bhwc.ndim != 4:
+            return mask_bhw1
+        _, Ht, Wt, _ = like_bhwc.shape
+        _, Hm, Wm, C = mask_bhw1.shape
+        if (Hm, Wm) == (Ht, Wt):
+            return mask_bhw1
+        m = mask_bhw1.movedim(-1, 1)
+        m = F.interpolate(m, size=(Ht, Wt), mode='bilinear', align_corners=False)
+        return m.movedim(1, -1).clamp(0, 1)
+    except Exception:
+        return mask_bhw1
+
+def _align_mask_pair(a_bhw1: torch.Tensor, b_bhw1: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    try:
+        if a_bhw1 is None or b_bhw1 is None:
+            return a_bhw1, b_bhw1
+        if a_bhw1.ndim != 4 or b_bhw1.ndim != 4:
+            return a_bhw1, b_bhw1
+        _, Ha, Wa, Ca = a_bhw1.shape
+        _, Hb, Wb, Cb = b_bhw1.shape
+        if (Ha, Wa) == (Hb, Wb):
+            return a_bhw1, b_bhw1
+        m = b_bhw1.movedim(-1, 1)
+        m = F.interpolate(m, size=(Ha, Wa), mode='bilinear', align_corners=False)
+        return a_bhw1, m.movedim(1, -1).clamp(0, 1)
+    except Exception:
+        return a_bhw1, b_bhw1
 def _mask_dilate(mask_bhw1: torch.Tensor, k: int = 3) -> torch.Tensor:
     if k <= 1:
         return mask_bhw1
@@ -2124,9 +2178,13 @@ class ComfyAdaptiveDetailEnhancer25:
                     try:
                         cmask = _clipseg_build_mask(image, clipseg_text, int(clipseg_preview), float(clipseg_threshold), float(clipseg_blur), int(clipseg_dilate), float(clipseg_gain), None, None, float(clipseg_ref_threshold))
                         if cmask is not None:
+                            if pre_mask is not None:
+                                pre_mask = _mask_to_like(pre_mask, image)
+                            cmask = _mask_to_like(cmask, image)
                             if pre_mask is None:
                                 pre_mask = cmask
                             else:
+                                pre_mask, cmask = _align_mask_pair(pre_mask, cmask)
                                 if clipseg_blend == "replace":
                                     pre_mask = cmask
                                 elif clipseg_blend == "intersect":
@@ -2147,6 +2205,8 @@ class ComfyAdaptiveDetailEnhancer25:
                         except Exception:
                             emask = None
                     if emask is not None:
+                        if pre_mask is not None:
+                            pre_mask, emask = _align_mask_pair(pre_mask, emask)
                         pre_mask = emask if pre_mask is None else (1.0 - (1.0 - pre_mask) * (1.0 - emask)).clamp(0, 1)
                 if pre_mask is not None:
                     onnx_mask_last = pre_mask
@@ -2271,6 +2331,7 @@ class ComfyAdaptiveDetailEnhancer25:
                                 if onnx_mask_last is None:
                                     onnx_mask_last = em2
                                 else:
+                                    onnx_mask_last, em2 = _align_mask_pair(onnx_mask_last, em2)
                                     onnx_mask_last = (1.0 - (1.0 - onnx_mask_last) * (1.0 - em2)).clamp(0, 1)
                                 om = onnx_mask_last.movedim(-1, 1)
                                 area = float(om.mean().item())
@@ -2293,8 +2354,10 @@ class ComfyAdaptiveDetailEnhancer25:
                                     if clipseg_blend == "replace":
                                         fused = cmask
                                     elif clipseg_blend == "intersect":
+                                        onnx_mask_last, cmask = _align_mask_pair(onnx_mask_last, cmask)
                                         fused = (onnx_mask_last * cmask).clamp(0, 1)
                                     else:
+                                        onnx_mask_last, cmask = _align_mask_pair(onnx_mask_last, cmask)
                                         fused = (1.0 - (1.0 - onnx_mask_last) * (1.0 - cmask)).clamp(0, 1)
                                 onnx_mask_last = fused
                                 om = fused.movedim(-1, 1)
@@ -2470,7 +2533,16 @@ class ComfyAdaptiveDetailEnhancer25:
                     model_management.throw_exception_if_processing_interrupted()
 
                     if bool(latent_compare):
-                        latent_diff = current_latent["samples"] - prev_samples
+                        _cur = current_latent["samples"]
+                        _prev = prev_samples
+                        try:
+                            if _prev.device != _cur.device:
+                                _prev = _prev.to(_cur.device)
+                            if _prev.dtype != _cur.dtype:
+                                _prev = _prev.to(dtype=_cur.dtype)
+                        except Exception:
+                            pass
+                        latent_diff = _cur - _prev
                         rms = torch.sqrt(torch.mean(latent_diff * latent_diff))
                         drift = float(rms.item())
                         if drift > float(threshold):

mod/hard/mg_cade25.py

@@ -97,6 +97,12 @@ def _clipseg_build_mask(image_bhwc: torch.Tensor,
         # Prepare preview image (CPU PIL)
         target = int(max(16, min(1024, preview)))
         img = image_bhwc.detach().to('cpu')
+        if img.ndim == 5:
+            # squeeze depth if present
+            if img.shape[1] == 1:
+                img = img[:, 0]
+            else:
+                img = img[:, 0]
         B, H, W, C = img.shape
         x = img[0].movedim(-1, 0).unsqueeze(0)  # 1,C,H,W
         x = F.interpolate(x, size=(target, target), mode='bilinear', align_corners=False)
@@ -172,6 +178,40 @@ def _np_to_mask_tensor(np_map: np.ndarray, out_h: int, out_w: int, device, dtype
     return t.clamp(0, 1)
 
 
+def _mask_to_like(mask_bhw1: torch.Tensor, like_bhwc: torch.Tensor) -> torch.Tensor:
+    try:
+        if mask_bhw1 is None or like_bhwc is None:
+            return mask_bhw1
+        if mask_bhw1.ndim != 4 or like_bhwc.ndim != 4:
+            return mask_bhw1
+        _, Ht, Wt, _ = like_bhwc.shape
+        _, Hm, Wm, _ = mask_bhw1.shape
+        if (Hm, Wm) == (Ht, Wt):
+            return mask_bhw1
+        m = mask_bhw1.movedim(-1, 1)
+        m = F.interpolate(m, size=(Ht, Wt), mode='bilinear', align_corners=False)
+        return m.movedim(1, -1).clamp(0, 1)
+    except Exception:
+        return mask_bhw1
+
+
+def _align_mask_pair(a_bhw1: torch.Tensor, b_bhw1: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    try:
+        if a_bhw1 is None or b_bhw1 is None:
+            return a_bhw1, b_bhw1
+        if a_bhw1.ndim != 4 or b_bhw1.ndim != 4:
+            return a_bhw1, b_bhw1
+        _, Ha, Wa, _ = a_bhw1.shape
+        _, Hb, Wb, _ = b_bhw1.shape
+        if (Ha, Wa) == (Hb, Wb):
+            return a_bhw1, b_bhw1
+        m = b_bhw1.movedim(-1, 1)
+        m = F.interpolate(m, size=(Ha, Wa), mode='bilinear', align_corners=False)
+        return a_bhw1, m.movedim(1, -1).clamp(0, 1)
+    except Exception:
+        return a_bhw1, b_bhw1
+
+
 # --- Firefly/Hot-pixel remover (image space, BHWC in 0..1) ---
 def _median_pool3x3_bhwc(img_bhwc: torch.Tensor) -> torch.Tensor:
     B, H, W, C = img_bhwc.shape
@@ -773,17 +813,31 @@ def _clip_cosine_distance(a: torch.Tensor, b: torch.Tensor) -> float:
 
 
 def _gaussian_blur_nchw(x: torch.Tensor, sigma: float = 1.0, radius: int = 1) -> torch.Tensor:
-    """Lightweight depthwise Gaussian blur for NCHW tensors.
+    """Lightweight depthwise Gaussian blur for NCHW or NCDHW tensors.
     Uses reflect padding and a normalized kernel built by _gaussian_kernel.
     """
     if radius <= 0:
         return x
     ksz = radius * 2 + 1
     kernel = _gaussian_kernel(ksz, sigma, device=x.device).to(dtype=x.dtype)
-    kernel = kernel.repeat(x.shape[1], 1, 1).unsqueeze(1)  # [C,1,K,K]
-    x_pad = F.pad(x, (radius, radius, radius, radius), mode='reflect')
-    y = F.conv2d(x_pad, kernel, padding=0, groups=x.shape[1])
-    return y
+    # Support 5D by folding depth into batch
+    if x.ndim == 5:
+        b, c, d, h, w = x.shape
+        x2 = x.permute(0, 2, 1, 3, 4).reshape(b * d, c, h, w)
+        k = kernel.repeat(c, 1, 1).unsqueeze(1)  # [C,1,K,K]
+        x_pad = F.pad(x2, (radius, radius, radius, radius), mode='reflect')
+        y2 = F.conv2d(x_pad, k, padding=0, groups=c)
+        y = y2.reshape(b, d, c, h, w).permute(0, 2, 1, 3, 4)
+        return y
+    # 4D path
+    if x.ndim == 4:
+        b, c, h, w = x.shape
+        k = kernel.repeat(c, 1, 1).unsqueeze(1)  # [C,1,K,K]
+        x_pad = F.pad(x, (radius, radius, radius, radius), mode='reflect')
+        y = F.conv2d(x_pad, k, padding=0, groups=c)
+        return y
+    # Fallback: return input if unexpected dims
+    return x
 
 
 def _letterbox_nchw(x: torch.Tensor, target: int, pad_val: float = 114.0 / 255.0) -> torch.Tensor:
@@ -1074,6 +1128,8 @@ def _wrap_model_with_guidance(model, guidance_mode: str, rescale_multiplier: flo
 
     m.set_model_sampler_cfg_function(cfg_func, disable_cfg1_optimization=True)
 
+    # Note: ControlNet class-label injection wrapper removed to keep CADE neutral.
+
     # Optional directional post-mix inspired by Mahiro (global, no ONNX)
     if bool(mahiro_plus_enable):
         s_clamp = float(max(0.0, min(1.0, mahiro_plus_strength)))
@@ -1511,6 +1567,7 @@ class ComfyAdaptiveDetailEnhancer25:
                             if pre_mask is None:
                                 pre_mask = cmask
                             else:
+                                pre_mask, cmask = _align_mask_pair(pre_mask, cmask)
                                 if clipseg_blend == "replace":
                                     pre_mask = cmask
                                 elif clipseg_blend == "intersect":
@@ -1586,6 +1643,7 @@ class ComfyAdaptiveDetailEnhancer25:
                                 if mask_last is None:
                                     fused = cmask
                                 else:
+                                    mask_last, cmask = _align_mask_pair(mask_last, cmask)
                                     if clipseg_blend == "replace":
                                         fused = cmask
                                     elif clipseg_blend == "intersect":
@@ -1668,7 +1726,16 @@ class ComfyAdaptiveDetailEnhancer25:
                     model_management.throw_exception_if_processing_interrupted()
 
                     if bool(latent_compare):
-                        latent_diff = current_latent["samples"] - prev_samples
+                        _cur = current_latent["samples"]
+                        _prev = prev_samples
+                        try:
+                            if _prev.device != _cur.device:
+                                _prev = _prev.to(_cur.device)
+                            if _prev.dtype != _cur.dtype:
+                                _prev = _prev.to(dtype=_cur.dtype)
+                        except Exception:
+                            pass
+                        latent_diff = _cur - _prev
                         rms = torch.sqrt(torch.mean(latent_diff * latent_diff))
                         drift = float(rms.item())
                         if drift > float(threshold):

mod/mg_latent_adapter.py

@@ -0,0 +1,234 @@
+"""
+MagicLatentAdapter: two-in-one latent adapter for ComfyUI.
+
+- Mode "generate": creates a latent of appropriate grid size for the target model
+  (optionally mixing an input image via VAE), then adapts channels.
+- Mode "adapt": takes an incoming LATENT and adapts channel count to match the model.
+
+Family switch: "auto / SD / SDXL / FLUX" influences only stride fallback when VAE
+is not provided. In AUTO we query VAE stride if possible and fall back to 8.
+
+No file re-encodings are performed; all code is ASCII/English as requested.
+"""
+
+from __future__ import annotations
+
+import torch
+import torch.nn.functional as F
+
+import comfy.sample as _sample
+
+
+class MagicLatentAdapter:
+    """Generate or adapt a LATENT to fit the target model's expectations."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model": ("MODEL", {}),
+                "mode": (["generate", "adapt"], {"default": "generate"}),
+                "family": (["auto", "SD", "SDXL", "FLUX"], {"default": "auto"}),
+
+                # Generation params (ignored in adapt mode)
+                "width": ("INT", {"default": 512, "min": 8, "max": 8192, "step": 8}),
+                "height": ("INT", {"default": 512, "min": 8, "max": 8192, "step": 8}),
+                "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
+                "sigma": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1}),
+                "bias": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step": 0.1}),
+                "mix_image": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                # For adapt mode
+                "latent": ("LATENT", {}),
+                # For image mixing in generate mode
+                "vae": ("VAE", {}),
+                "image": ("IMAGE", {}),
+            },
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    RETURN_NAMES = ("LATENT",)
+    FUNCTION = "run"
+    CATEGORY = "MagicNodes"
+
+    @staticmethod
+    def _detect_stride(vae, family: str) -> int:
+        # Prefer VAE stride if available
+        if vae is not None:
+            try:
+                s = int(vae.spacial_compression_decode())
+                if s > 0:
+                    return s
+            except Exception:
+                pass
+        # Fallback per-family (conservative)
+        fam = (family or "auto").lower()
+        if fam in ("sd", "sdxl", "flux"):
+            return 8
+        return 8  # sensible default
+
+    @staticmethod
+    def _latent_format(model) -> tuple[int, int]:
+        """Return (channels, dimensions) from model.latent_format.
+        dimensions: 2 -> NCHW, 3 -> NCDHW.
+        """
+        try:
+            lf = model.get_model_object("latent_format")
+            ch = int(getattr(lf, "latent_channels", 4))
+            dims = int(getattr(lf, "latent_dimensions", 2))
+            if dims not in (2, 3):
+                dims = 2
+            return ch, dims
+        except Exception:
+            return 4, 2
+
+    @staticmethod
+    def _adapt_channels(model, z: torch.Tensor, preserve_zero: bool = False) -> torch.Tensor:
+        """Adapts channel count and dims to the model's latent_format.
+        If preserve_zero and the latent is all zeros, pad with zeros instead of noise.
+        """
+        target_c, target_dims = MagicLatentAdapter._latent_format(model)
+
+        # First, let Comfy add depth dim for empty latents when needed
+        try:
+            z = _sample.fix_empty_latent_channels(model, z)
+        except Exception:
+            pass
+
+        # Align dimensions
+        if target_dims == 3 and z.ndim == 4:
+            z = z.unsqueeze(2)  # N C 1 H W
+        elif target_dims == 2 and z.ndim == 5:
+            if z.shape[2] == 1:
+                z = z.squeeze(2)
+            else:
+                z = z[:, :, :1].squeeze(2)
+
+        # Align channels
+        if z.ndim == 4:
+            B, C, H, W = z.shape
+            if C == target_c:
+                return z
+            if C > target_c:
+                return z[:, :target_c]
+            dev, dt = z.device, z.dtype
+            if preserve_zero and torch.count_nonzero(z) == 0:
+                pad = torch.zeros(B, target_c - C, H, W, device=dev, dtype=dt)
+            else:
+                pad = torch.randn(B, target_c - C, H, W, device=dev, dtype=dt)
+            return torch.cat([z, pad], dim=1)
+        elif z.ndim == 5:
+            B, C, D, H, W = z.shape
+            if C == target_c:
+                return z
+            if C > target_c:
+                return z[:, :target_c]
+            dev, dt = z.device, z.dtype
+            if preserve_zero and torch.count_nonzero(z) == 0:
+                pad = torch.zeros(B, target_c - C, D, H, W, device=dev, dtype=dt)
+            else:
+                pad = torch.randn(B, target_c - C, D, H, W, device=dev, dtype=dt)
+            return torch.cat([z, pad], dim=1)
+        else:
+            return z
+
+    @staticmethod
+    def _mix_image_into_latent(vae, image_bhwc: torch.Tensor, z: torch.Tensor) -> torch.Tensor:
+        if vae is None or image_bhwc is None:
+            return z
+        try:
+            # Align image spatial to VAE grid by padding (mirror) if needed
+            try:
+                stride = int(vae.spacial_compression_decode())
+            except Exception:
+                stride = 8
+            h, w = image_bhwc.shape[1:3]
+            def _align_up(x, s):
+                return int(((x + s - 1) // s) * s)
+            Ht, Wt = _align_up(h, stride), _align_up(w, stride)
+            x = image_bhwc
+            if (Ht != h) or (Wt != w):
+                pad_h = Ht - h
+                pad_w = Wt - w
+                x_nchw = x.movedim(-1, 1)
+                x_nchw = F.pad(x_nchw, (0, pad_w, 0, pad_h), mode='replicate')
+                x = x_nchw.movedim(1, -1)
+            enc = vae.encode(x[:, :, :, :3])
+            # If batch mismatches, use first encoding and tile
+            while enc.ndim < z.ndim:
+                enc = enc.unsqueeze(2)  # add depth dim if needed
+            while enc.ndim > z.ndim:
+                # reduce extra depth dims
+                if enc.ndim == 5 and enc.shape[2] == 1:
+                    enc = enc.squeeze(2)
+                else:
+                    enc = enc[(slice(None), slice(None)) + (slice(0,1),) * (enc.ndim-2)]
+                    if enc.ndim == 5:
+                        enc = enc.squeeze(2)
+            if enc.shape[0] != z.shape[0]:
+                enc = enc[:1]
+                enc = enc.repeat(z.shape[0], *([1] * (enc.ndim - 1)))
+            # Resize spatial if needed (nearest)
+            if enc.ndim == 4:
+                if enc.shape[2:] != z.shape[2:]:
+                    enc = F.interpolate(enc, size=z.shape[2:], mode="nearest")
+            elif enc.ndim == 5:
+                if enc.shape[2:] != z.shape[2:]:
+                    enc = F.interpolate(enc, size=z.shape[2:], mode="nearest")
+            # Channel adapt for mixing safety
+            if enc.shape[1] != z.shape[1]:
+                cmin = min(enc.shape[1], z.shape[1])
+                enc = enc[:, :cmin]
+                z = z[:, :cmin]
+            return enc + z
+        except Exception:
+            return z
+
+    def run(
+        self,
+        model,
+        mode: str,
+        family: str,
+        width: int,
+        height: int,
+        batch_size: int,
+        sigma: float,
+        bias: float,
+        mix_image: bool = False,
+        latent=None,
+        vae=None,
+        image=None,
+    ):
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        if mode == "adapt":
+            if latent is None or "samples" not in latent:
+                # Produce an empty latent matching model's latent_format
+                stride = self._detect_stride(vae, family)
+                h8, w8 = max(1, height // stride), max(1, width // stride)
+                target_c, target_dims = self._latent_format(model)
+                if target_dims == 3:
+                    z = torch.zeros(batch_size, target_c, 1, h8, w8, device=device)
+                else:
+                    z = torch.zeros(batch_size, target_c, h8, w8, device=device)
+            else:
+                z = latent["samples"].to(device)
+            z = self._adapt_channels(model, z, preserve_zero=True)
+            return ({"samples": z},)
+
+        # generate
+        stride = self._detect_stride(vae, family)
+        h8, w8 = max(1, height // stride), max(1, width // stride)
+        target_c, target_dims = self._latent_format(model)
+        if target_dims == 3:
+            z = torch.randn(batch_size, target_c, 1, h8, w8, device=device) * float(sigma) + float(bias)
+        else:
+            z = torch.randn(batch_size, target_c, h8, w8, device=device) * float(sigma) + float(bias)
+        if mix_image and (vae is not None) and (image is not None):
+            # image is BHWC 0..1
+            img = image.to(device)
+            z = self._mix_image_into_latent(vae, img, z)
+        # Final channel adaptation
+        z = self._adapt_channels(model, z, preserve_zero=False)
+        return ({"samples": z},)

pressets/mg_cade25.cfg

@@ -353,9 +353,9 @@ kv_min_tokens: 256
 seed: 0
 control_after_generate: randomize
 steps: 25
-cfg: 7.0
+cfg: 6.0
 #0.75
-denoise: 0.55
+denoise: 0.35
 sampler_name: ddim
 scheduler: MGHybrid
 iterations: 2

workflows/mg_Easy-Workflow.json

@@ -1 +1 @@
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