[Feature Request] Add optional latent-input to reference_only for img2img about comfyui_experiments HOT 17 OPEN

--- reference_only.py   2023-07-26 22:24:24.000000000 +0300
+++ reference_only2.py  2023-08-22 19:42:10.985318300 +0300
@@ -1,10 +1,11 @@
 import torch

-class ReferenceOnlySimple:
+class ReferenceOnlySimple2:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
                               "reference": ("LATENT",),
+                              "input2": ("LATENT",),
                               "batch_size": ("INT", {"default": 1, "min": 1, "max": 64})
                               }}

@@ -13,12 +14,12 @@

     CATEGORY = "custom_node_experiments"

-    def reference_only(self, model, reference, batch_size):
+    def reference_only(self, model, reference, input2, batch_size):
         model_reference = model.clone()
         size_latent = list(reference["samples"].shape)
         size_latent[0] = batch_size
-        latent = {}
-        latent["samples"] = torch.zeros(size_latent)
+        latent = input2
+        #latent["samples"] = torch.zeros(size_latent)

         batch = latent["samples"].shape[0] + reference["samples"].shape[0]
         def reference_apply(q, k, v, extra_options):
@@ -50,5 +51,5 @@
         return (model_reference, {"samples": out_latent, "noise_mask": torch.cat((out_mask, mask))})

 NODE_CLASS_MAPPINGS = {
-    "ReferenceOnlySimple": ReferenceOnlySimple,
+    "ReferenceOnlySimple2": ReferenceOnlySimple2,
 }

import torch

class Inp_ReferenceOnlySimple:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
                              "reference": ("LATENT",),
                              "latent": ("LATENT",),
                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 64})
                              }}

    RETURN_TYPES = ("MODEL", "LATENT")
    FUNCTION = "inp_reference_only"

    CATEGORY = "custom_node_experiments"

    def inp_reference_only(self, model, reference, latent, batch_size):
        model_reference = model.clone()
        size_latent = list(reference["samples"].shape)
        size_latent[0] = batch_size
        # latent = {}
        # latent["samples"] = torch.zeros(size_latent)

        batch = latent["samples"].shape[0] + reference["samples"].shape[0]
        # batch = reference["samples"].shape[0]
        def reference_apply(q, k, v, extra_options):
            k = k.clone().repeat(1, 2, 1)
            offset = 0
            if q.shape[0] > batch:
                offset = batch

            for o in range(0, q.shape[0], batch):
                for x in range(1, batch):
                    k[x + o, q.shape[1]:] = q[o,:]

            return q, k, k

        model_reference.set_model_attn1_patch(reference_apply)
        out_latent = torch.cat((reference["samples"], latent["samples"]))
        if "noise_mask" in latent:
            mask = latent["noise_mask"]
        else:
            mask = torch.ones((64,64), dtype=torch.float32, device="cpu")

        if len(mask.shape) < 3:
            mask = mask.unsqueeze(0)
        if mask.shape[0] < latent["samples"].shape[0]:
            print(latent["samples"].shape, mask.shape)
            mask = mask.repeat(latent["samples"].shape[0], 1, 1)

        out_mask = torch.zeros((1,mask.shape[1],mask.shape[2]), dtype=torch.float32, device="cpu")
        return (model_reference, {"samples": out_latent, "noise_mask": torch.cat((out_mask, mask))})

NODE_CLASS_MAPPINGS = {
    "Inpaint ReferenceOnlySimple": Inp_ReferenceOnlySimple,
}

--- reference_only.py	2023-07-26 22:24:24.000000000 +0300
+++ reference_only2.py	2023-09-23 15:11:48.232217100 +0300
@@ -1,10 +1,11 @@
 import torch
 
-class ReferenceOnlySimple:
+class ReferenceOnlySimple2:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
                               "reference": ("LATENT",),
+                              "input2": ("LATENT",),
                               "batch_size": ("INT", {"default": 1, "min": 1, "max": 64})
                               }}
 
@@ -13,12 +14,12 @@
 
     CATEGORY = "custom_node_experiments"
 
-    def reference_only(self, model, reference, batch_size):
+    def reference_only(self, model, reference, input2, batch_size):
         model_reference = model.clone()
         size_latent = list(reference["samples"].shape)
         size_latent[0] = batch_size
-        latent = {}
-        latent["samples"] = torch.zeros(size_latent)
+        latent = input2
+        #latent["samples"] = torch.zeros(size_latent)
 
         batch = latent["samples"].shape[0] + reference["samples"].shape[0]
         def reference_apply(q, k, v, extra_options):
@@ -39,16 +40,22 @@
             mask = latent["noise_mask"]
         else:
             mask = torch.ones((64,64), dtype=torch.float32, device="cpu")
+            
 
         if len(mask.shape) < 3:
             mask = mask.unsqueeze(0)
         if mask.shape[0] < latent["samples"].shape[0]:
             print(latent["samples"].shape, mask.shape)
             mask = mask.repeat(latent["samples"].shape[0], 1, 1)
-
-        out_mask = torch.zeros((1,mask.shape[1],mask.shape[2]), dtype=torch.float32, device="cpu")
+            
+        
+        if len(mask.shape) < 4:
+            out_mask = torch.zeros((1,mask.shape[1],mask.shape[2]), dtype=torch.float32, device="cpu")
+        else:
+            out_mask = torch.zeros((1,1,mask.shape[2],mask.shape[3]), dtype=torch.float32, device="cpu")
+            
         return (model_reference, {"samples": out_latent, "noise_mask": torch.cat((out_mask, mask))})
 
 NODE_CLASS_MAPPINGS = {
-    "ReferenceOnlySimple": ReferenceOnlySimple,
+    "ReferenceOnlySimple2": ReferenceOnlySimple2,
 }