From 88c350bfed1a959585cfc3f08f036e1af906bdd7 Mon Sep 17 00:00:00 2001
From: Yousef Rafat <81116377+yousef-rafat@users.noreply.github.com>
Date: Fri, 28 Nov 2025 20:43:01 +0200
Subject: [PATCH] corrected img_ratio

---
 comfy/ldm/hunyuan_image_3/model.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/comfy/ldm/hunyuan_image_3/model.py b/comfy/ldm/hunyuan_image_3/model.py
index f4cf935ef..099732a6c 100644
--- a/comfy/ldm/hunyuan_image_3/model.py
+++ b/comfy/ldm/hunyuan_image_3/model.py
@@ -15,6 +15,7 @@ from transformers.cache_utils import StaticCache
 from concurrent.futures import ThreadPoolExecutor
 from typing import Optional, Tuple, Any, List, Dict
 from comfy.ldm.modules.attention import optimized_attention
+from comfy_extras.nodes_hunyuan_image import COMPUTED_RESO_GROUPS
 from comfy.ldm.modules.diffusionmodules.openaimodel import ResBlock
 
 INIT_MOE = torch.cuda.device_count() != 1
@@ -1048,10 +1049,14 @@ class HunyuanImage3ForCausalMM(nn.Module):
         def fn(string, func = self.encode_tok):
             return self.model.wte(torch.tensor(func(string) if not isinstance(func, dict) else func[string], device=inputs_embeds.device))\
                 .unsqueeze(0).expand(bsz, -1, -1)
+
+        hw = f"{int(height)}x{int(width)}"
+        ratio_idx = [i for i, reso in enumerate(COMPUTED_RESO_GROUPS) if reso == hw][0]
+        img_ratio = fn(f"<img_ratio_{ratio_idx}>", self.special_tok)
         
         if cond_exists:
             with torch.no_grad():
-                joint_image[:, 2:3, :] = fn(f"<img_ratio_{int(height) // int(width)}>", self.special_tok) # updates image ratio 
+                joint_image[:, 2:3, :] = img_ratio
         
         img_slices = []
         
@@ -1060,7 +1065,7 @@ class HunyuanImage3ForCausalMM(nn.Module):
 
         if self.first_step:
             x, token_height, token_width = self.patch_embed(x, t_emb)
-            x = torch.cat([fn("<boi>"), fn("<img_size_1024>", func = self.special_tok), fn(f"<img_ratio_{int(height) // int(width)}>", self.special_tok), fn("<timestep>", self.special_tok), x, fn("<eoi>")], dim = 1)
+            x = torch.cat([fn("<boi>"), fn("<img_size_1024>", func = self.special_tok), img_ratio, fn("<timestep>", self.special_tok), x, fn("<eoi>")], dim = 1)
             x[:, gen_timestep_scatter_index:gen_timestep_scatter_index+1, :] = self.timestep_emb(timestep.reshape(-1)).reshape(bsz, -1, n_embd)
         else:
             x, token_height, token_width = self.patch_embed(x, t_emb)