Merge 76909f1dfa into bbe2c13a70

Move count increment before isinstance(item, dict) check so that
non-dict output items (like text strings from PreviewAny node)
are included in outputs_count.

This aligns OSS Python with Cloud's Go implementation which uses
len(itemsArray) to count ALL items regardless of type.

Amp-Thread-ID: https://ampcode.com/threads/T-019c0bb5-14e0-744f-8808-1e57653f3ae3

Co-authored-by: Amp <amp@ampcode.com>

comfy/k_diffusion/sampling.py

@@ -1322,9 +1322,10 @@ def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback
             r = 1 / 2
             h = t_next - t
             s = t + r * h
-            x_2 = (sigma_fn(s) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h * r).expm1() * denoised
+            uncond_denoised = temp[0]
+            x_2 = (sigma_fn(s) / sigma_fn(t)) * (x + (denoised - uncond_denoised)) - (-h * r).expm1() * denoised
             denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
-            x = (sigma_fn(t_next) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h).expm1() * denoised_2
+            x = (sigma_fn(t_next) / sigma_fn(t)) * (x + (denoised - uncond_denoised)) - (-h).expm1() * denoised_2
         # Noise addition
         if sigmas[i + 1] > 0:
             x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up

comfy_execution/jobs.py

@@ -171,9 +171,10 @@ def get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
                 continue
 
             for item in items:
+                count += 1
+
                 if not isinstance(item, dict):
                     continue
-                count += 1
 
                 if preview_output is None and is_previewable(media_type, item):
                     enriched = {

comfy_extras/nodes_hunyuan.py

@@ -56,7 +56,7 @@ class EmptyHunyuanLatentVideo(io.ComfyNode):
     @classmethod
     def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
         latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-        return io.NodeOutput({"samples":latent})
+        return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 8})
 
     generate = execute  # TODO: remove
 
@@ -73,7 +73,7 @@ class EmptyHunyuanVideo15Latent(EmptyHunyuanLatentVideo):
     def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
         # Using scale factor of 16 instead of 8
         latent = torch.zeros([batch_size, 32, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device())
-        return io.NodeOutput({"samples": latent})
+        return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 16})
 
 
 class HunyuanVideo15ImageToVideo(io.ComfyNode):