Merge branch 'Kosinkadink:main' into main

animatediff/model_injection.py

@@ -66,6 +66,12 @@ def __init__(self, m: ModelPatcher):
         self.motion_injection_params: InjectionParams = InjectionParams()
         self.sample_settings: SampleSettings = SampleSettings()
         self.motion_models: MotionModelGroup = None
+        # backwards-compatible calculate_weight
+        if hasattr(comfy.lora, "calculate_weight"):
+            self.do_calculate_weight = comfy.lora.calculate_weight
+        else:
+            self.do_calculate_weight = self.calculate_weight
+
 
     def clone(self, hooks_only=False):
         cloned = ModelPatcherAndInjector(self)
@@ -379,7 +385,7 @@ def patch_hooked_weight_to_device(self, lora_hooks: LoraHookGroup, combined_patc
 
         # TODO: handle model_params_lowvram stuff if necessary
         temp_weight = comfy.model_management.cast_to_device(weight, weight.device, torch.float32, copy=True)
-        out_weight = self.calculate_weight(combined_patches[key], temp_weight, key).to(weight.dtype)
+        out_weight = self.do_calculate_weight(combined_patches[key], temp_weight, key).to(weight.dtype)
         if self.lora_hook_mode == LoraHookMode.MAX_SPEED:
             self.cached_hooked_patches.setdefault(lora_hooks, {})
             self.cached_hooked_patches[lora_hooks][key] = out_weight
@@ -809,6 +815,7 @@ def load(self, device_to=None, lowvram_model_memory=0, *args, **kwargs):
         to_return = super().load(device_to=device_to, lowvram_model_memory=lowvram_model_memory, *args, **kwargs)
         if lowvram_model_memory > 0:
             self._patch_lowvram_extras(device_to=device_to)
+        self._handle_float8_pe_tensors()
         return to_return
 
     def _patch_lowvram_extras(self, device_to=None):
@@ -828,6 +835,17 @@ def _patch_lowvram_extras(self, device_to=None):
             if device_to is not None:
                 comfy.utils.set_attr(self.model, key, comfy.utils.get_attr(self.model, key).to(device_to))
 
+    def _handle_float8_pe_tensors(self):
+        remaining_tensors = list(self.model.state_dict().keys())
+        pe_tensors = [x for x in remaining_tensors if '.pe' in x]
+        is_first = True
+        for key in pe_tensors:
+            if is_first:
+                is_first = False
+                if comfy.utils.get_attr(self.model, key).dtype not in [torch.float8_e5m2, torch.float8_e4m3fn]:
+                    break
+            comfy.utils.set_attr(self.model, key, comfy.utils.get_attr(self.model, key).half())
+
     def pre_run(self, model: ModelPatcherAndInjector):
         self.cleanup()
         self.model.set_scale(self.scale_multival, self.per_block_list)

animatediff/nodes_scheduling.py

@@ -134,7 +134,7 @@ def create_schedule(self, prompts: str, clip, print_schedule=False, max_length:
         options = PromptOptions(interp=tensor_interp, prepend_text=prepend_text, append_text=append_text,
                                 values_replace=values_replace, print_schedule=print_schedule)
         conditioning = evaluate_prompt_schedule(prompts, max_length, clip, options)
-        return (conditioning)
+        return (conditioning,)
 
 
 class ValueSchedulingLatentsNode:

animatediff/scheduling.py

@@ -372,6 +372,7 @@ def handle_prompt_interpolation(pairs: list[InputPair], length: int, clip: CLIP,
                 prev_holder = CondHolder(idx=i, prompt=real_prompt, raw_prompt=prev_holder.raw_prompt, cond=cond, pooled=pooled, hold=prev_holder.hold)
             real_cond[i] = prev_holder.cond
             real_pooled[i] = prev_holder.pooled
+            real_holders[i] = prev_holder
         else:
             prev_holder = real_holders[i]
         pbar.update(1)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-animatediff-evolved"
 description = "Improved AnimateDiff integration for ComfyUI."
-version = "1.2.0"
+version = "1.2.3"
 license = { file = "LICENSE" }
 dependencies = []