Merge branch 'master' into beta

comfy_extras/nodes_images.py

@@ -23,7 +23,22 @@ def crop(self, image, width, height, x, y):
         img = image[:,y:to_y, x:to_x, :]
         return (img,)
 
+class RepeatImageBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "image": ("IMAGE",),
+                              "amount": ("INT", {"default": 1, "min": 1, "max": 64}),
+                              }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "repeat"
+
+    CATEGORY = "image/batch"
+
+    def repeat(self, image, amount):
+        s = image.repeat((amount, 1,1,1))
+        return (s,)
 
 NODE_CLASS_MAPPINGS = {
     "ImageCrop": ImageCrop,
+    "RepeatImageBatch": RepeatImageBatch,
 }

comfy_extras/nodes_latent.py

@@ -1,4 +1,5 @@
 import comfy.utils
+import torch
 
 def reshape_latent_to(target_shape, latent):
     if latent.shape[1:] != target_shape[1:]:
@@ -67,8 +68,43 @@ def op(self, samples, multiplier):
         samples_out["samples"] = s1 * multiplier
         return (samples_out,)
 
+class LatentInterpolate:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples1": ("LATENT",),
+                              "samples2": ("LATENT",),
+                              "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                              }}
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "op"
+
+    CATEGORY = "latent/advanced"
+
+    def op(self, samples1, samples2, ratio):
+        samples_out = samples1.copy()
+
+        s1 = samples1["samples"]
+        s2 = samples2["samples"]
+
+        s2 = reshape_latent_to(s1.shape, s2)
+
+        m1 = torch.linalg.vector_norm(s1, dim=(1))
+        m2 = torch.linalg.vector_norm(s2, dim=(1))
+
+        s1 = torch.nan_to_num(s1 / m1)
+        s2 = torch.nan_to_num(s2 / m2)
+
+        t = (s1 * ratio + s2 * (1.0 - ratio))
+        mt = torch.linalg.vector_norm(t, dim=(1))
+        st = torch.nan_to_num(t / mt)
+
+        samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio))
+        return (samples_out,)
+
 NODE_CLASS_MAPPINGS = {
     "LatentAdd": LatentAdd,
     "LatentSubtract": LatentSubtract,
     "LatentMultiply": LatentMultiply,
+    "LatentInterpolate": LatentInterpolate,
 }

execution.py

@@ -704,6 +704,7 @@ def validate_prompt(prompt):
 
     return (True, None, list(good_outputs), node_errors)
 
+MAXIMUM_HISTORY_SIZE = 10000
 
 class PromptQueue:
     def __init__(self, server):
@@ -736,6 +737,8 @@ def get(self):
     def task_done(self, item_id, outputs):
         with self.mutex:
             prompt = self.currently_running.pop(item_id)
+            if len(self.history) > MAXIMUM_HISTORY_SIZE:
+                self.history.pop(next(iter(self.history)))
             self.history[prompt[1]] = { "prompt": prompt, "outputs": {} }
             for o in outputs:
                 self.history[prompt[1]]["outputs"][o] = outputs[o]
@@ -770,10 +773,20 @@ def delete_queue_item(self, function):
                     return True
         return False
 
-    def get_history(self, prompt_id=None):
+    def get_history(self, prompt_id=None, max_items=None, offset=-1):
         with self.mutex:
             if prompt_id is None:
-                return copy.deepcopy(self.history)
+                out = {}
+                i = 0
+                if offset < 0 and max_items is not None:
+                    offset = len(self.history) - max_items
+                for k in self.history:
+                    if i >= offset:
+                        out[k] = self.history[k]
+                        if max_items is not None and len(out) >= max_items:
+                            break
+                    i += 1
+                return out
             elif prompt_id in self.history:
                 return {prompt_id: copy.deepcopy(self.history[prompt_id])}
             else:

server.py

@@ -431,7 +431,10 @@ async def get_object_info_node(request):
 
         @routes.get("/history")
         async def get_history(request):
-            return web.json_response(self.prompt_queue.get_history())
+            max_items = request.rel_url.query.get("max_items", None)
+            if max_items is not None:
+                max_items = int(max_items)
+            return web.json_response(self.prompt_queue.get_history(max_items=max_items))
 
         @routes.get("/history/{prompt_id}")
         async def get_history(request):

web/scripts/api.js

@@ -256,7 +256,7 @@ class ComfyApi extends EventTarget {
 	 */
 	async getHistory() {
 		try {
-			const res = await this.fetchApi("/history");
+			const res = await this.fetchApi("/history?max_items=200");
 			return { History: Object.values(await res.json()) };
 		} catch (error) {
 			console.error(error);