diff --git a/CHANGELOG.md b/CHANGELOG.md
index fa13aeacb..bd26c93d7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,8 +1,8 @@
 # Change Log for SD.Next
 
-## Update for 2025-07-18
+## Update for 2025-07-20
 
-### Highlights for 2025-07-18
+### Highlights for 2025-07-20
 
 Feature highlights include:  
 - **ModernUI** layout redesign which should make it more user friendly and easier to navigate  
@@ -26,7 +26,7 @@ Although upgrades and existing installations are tested and should work fine!
 
 [ReadMe](https://github.com/vladmandic/automatic/blob/master/README.md) | [ChangeLog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) | [Docs](https://vladmandic.github.io/sdnext-docs/) | [WiKi](https://github.com/vladmandic/automatic/wiki) | [Discord](https://discord.com/invite/sd-next-federal-batch-inspectors-1101998836328697867)
 
-### Details for 2025-07-18
+### Details for 2025-07-20
 
 - **License**  
   - SD.Next [license](https://github.com/vladmandic/sdnext/blob/dev/LICENSE.txt) switched from **aGPL-v3.0** to **Apache-v2.0**  
@@ -85,6 +85,7 @@ Although upgrades and existing installations are tested and should work fine!
     enable in *settings -> compute settings -> sdp options*  
     *note*: SD.Next will use either SageAttention v1/v2/v2++, depending which one is installed  
     until authors provide pre-build wheels for v2++, you need to install it manually or SD.Next will auto-install v1  
+  - support for `torch.compile` for LLM: captioning/prompt-enhannce  
   - support for `torch.compile` with repeated-blocks  
     reduces time-to-compile 5x without loss of performance!  
     enable in *settings -> model compile -> repeated*  
diff --git a/TODO.md b/TODO.md
index 9a760a8cc..0fddce9e6 100644
--- a/TODO.md
+++ b/TODO.md
@@ -8,7 +8,6 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 
 - [Modular pipelines and guiders](https://github.com/huggingface/diffusers/issues/11915)  
 - Refactor: Sampler options  
-- Refactor: [torch.compile](https://pytorch.org/blog/torch-compile-and-diffusers-a-hands-on-guide-to-peak-performance/)  
 - Feature: Diffusers [group offloading](https://github.com/vladmandic/sdnext/issues/4049)  
 - Feature: Common repo for `T5` and `CLiP`  
 - Feature: LoRA add OMI format support for SD35/FLUX.1  
diff --git a/modules/interrogate/vqa.py b/modules/interrogate/vqa.py
index f75342703..0457af7ea 100644
--- a/modules/interrogate/vqa.py
+++ b/modules/interrogate/vqa.py
@@ -7,7 +7,7 @@ import torch
 import transformers
 import transformers.dynamic_module_utils
 from PIL import Image
-from modules import shared, devices, errors, sd_models, model_quant
+from modules import shared, devices, errors, model_quant, sd_models, sd_models_compile
 
 
 processor = None
@@ -129,6 +129,8 @@ def qwen(question: str, image: Image.Image, repo: str = None, system_prompt: str
             **quant_args,
         )
         processor = transformers.AutoProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
+        if 'LLM' in shared.opts.cuda_compile:
+            model = compile_torch(model)
         loaded = repo
         devices.torch_gc()
     sd_models.move_model(model, devices.device)
@@ -177,6 +179,8 @@ def gemma(question: str, image: Image.Image, repo: str = None, system_prompt: st
             cache_dir=shared.opts.hfcache_dir,
             **quant_args,
         )
+        if 'LLM' in shared.opts.cuda_compile:
+            model = compile_torch(model)
         processor = transformers.AutoProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
         loaded = repo
         devices.torch_gc()
@@ -302,6 +306,8 @@ def smol(question: str, image: Image.Image, repo: str = None, system_prompt: str
             **quant_args,
             )
         processor = transformers.AutoProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
+        if 'LLM' in shared.opts.cuda_compile:
+            model = compile_torch(model)
         loaded = repo
         devices.torch_gc()
     sd_models.move_model(model, devices.device)
diff --git a/modules/sd_models_compile.py b/modules/sd_models_compile.py
index a396b8390..498df59c6 100644
--- a/modules/sd_models_compile.py
+++ b/modules/sd_models_compile.py
@@ -213,8 +213,11 @@ def compile_torch(sd_model):
 
         setup_logging() # compile messes with logging so reset is needed
         if 'precompile' in shared.opts.cuda_compile_options:
-            shared.log.debug("Model compile: task=torch precompile")
-            sd_model("dummy prompt")
+            try:
+                shared.log.debug("Model compile: task=torch precompile")
+                sd_model("dummy prompt")
+            except Exception:
+                pass
         t1 = time.time()
         shared.log.info(f"Model compile: task=torch time={t1-t0:.2f}")
     except Exception as e:
diff --git a/modules/shared.py b/modules/shared.py
index c0e01399e..32b8b180a 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -419,7 +419,7 @@ options_templates.update(options_section(('advanced', "Pipeline Modifiers"), {
 
 options_templates.update(options_section(('compile', "Model Compile"), {
     "cuda_compile_sep": OptionInfo("<h2>Model Compile</h2>", "", gr.HTML),
-    "cuda_compile": OptionInfo([] if not cmd_opts.use_openvino else ["Model", "VAE", "Upscaler"], "Compile Model", gr.CheckboxGroup, {"choices": ["Model", "TE", "VAE", "Upscaler"]}),
+    "cuda_compile": OptionInfo([] if not cmd_opts.use_openvino else ["Model", "VAE", "Upscaler"], "Compile Model", gr.CheckboxGroup, {"choices": ["Model", "TE", "VAE", "LLM", "Upscaler"]}),
     "cuda_compile_backend": OptionInfo("inductor" if not cmd_opts.use_openvino else "openvino_fx", "Model compile backend", gr.Radio, {"choices": ['none', 'inductor', 'cudagraphs', 'aot_ts_nvfuser', 'hidet', 'migraphx', 'ipex', 'onediff', 'stable-fast', 'deep-cache', 'olive-ai', 'openvino_fx']}),
     "cuda_compile_mode": OptionInfo("default", "Model compile mode", gr.Radio, {"choices": ['default', 'reduce-overhead', 'max-autotune', 'max-autotune-no-cudagraphs']}),
     "cuda_compile_options": OptionInfo(["precompile", "repeated", "fullgraph", "dynamic"], "Model compile options", gr.CheckboxGroup, {"choices": ["precompile", "repeated", "fullgraph", "dynamic", "verbose"]}),
diff --git a/requirements.txt b/requirements.txt
index 4ec99eac7..30b00f06d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -41,10 +41,10 @@ torchsde==0.2.6
 antlr4-python3-runtime==4.9.3
 requests==2.32.4
 tqdm==4.67.1
-accelerate==1.8.1
+accelerate==1.9.0
 opencv-contrib-python-headless==4.11.0.86
 einops==0.8.1
-huggingface_hub==0.33.2
+huggingface_hub==0.33.4
 numexpr==2.11.0
 numpy==2.1.2
 pandas==2.3.0
diff --git a/scripts/prompt_enhance.py b/scripts/prompt_enhance.py
index 7cc5ef064..3d27d95a4 100644
--- a/scripts/prompt_enhance.py
+++ b/scripts/prompt_enhance.py
@@ -3,6 +3,7 @@ import io
 import os
 import re
 import time
+import random
 import base64
 import torch
 import transformers
@@ -68,7 +69,8 @@ class Options:
             'file': 'Llama-3.2-1B-Instruct-Uncensored.i1-Q4_0.gguf', # gguf file inside repo
         },
     }
-    default = list(models)[1] # gemma-3-4b-it
+    # default = list(models)[1] # gemma-3-4b-it
+    default = 'Qwen/Qwen3-0.6B-FP8'
     supported = list(transformers.integrations.ggml.GGUF_CONFIG_MAPPING)
     t2i_prompt: str = 'You are a helpful assistant. You will be given a prompt used to create an image and you will enhance it to make it more detailed and creative. '
     i2i_prompt: str = 'You are a helpful assistant. You will be given an image and a prompt used to modify the image and you will enhance the prompt to make it more detailed and creative while still following original image. '
@@ -101,6 +103,12 @@ class Script(scripts_manager.Script):
     def show(self, _is_img2img):
         return scripts_manager.AlwaysVisible
 
+    def compile(self):
+        if self.llm is None or 'LLM' not in shared.opts.cuda_compile:
+            return
+        from modules.sd_models_compile import compile_torch
+        self.llm = compile_torch(self.llm)
+
     def load(self, name:str=None, model_repo:str=None, model_gguf:str=None, model_type:str=None, model_file:str=None):
         name = name or self.options.default
         if self.busy:
@@ -171,6 +179,7 @@ class Script(scripts_manager.Script):
             self.model = name
             t1 = time.time()
             shared.log.info(f'Prompt enhance: cls={self.llm.__class__.__name__} name="{name}" repo="{model_repo}" fn="{model_file}" time={t1-t0:.2f} loaded')
+            self.compile()
         except Exception as e:
             shared.log.error(f'Prompt enhance: load {e}')
             errors.display(e, 'Prompt enhance')
@@ -253,8 +262,10 @@ class Script(scripts_manager.Script):
         while self.busy:
             time.sleep(0.1)
         self.load(model)
-        if seed is not None and seed >= 0:
-            torch.manual_seed(seed)
+        if seed is None or seed == -1:
+            random.seed()
+            seed = int(random.randrange(4294967294))
+        torch.manual_seed(seed)
         if self.llm is None:
             shared.log.error('Prompt enhance: model not loaded')
             return prompt
@@ -394,7 +405,6 @@ class Script(scripts_manager.Script):
             return prompt # Return original full prompt on censorship
         return response
 
-    # --- START OF CORRECTED METHOD ---
     def apply(self, prompt, image, apply_prompt, llm_model, prompt_system, prompt_prefix, prompt_suffix, max_tokens, do_sample, temperature, repetition_penalty, thinking_mode, nsfw_mode): # Added nsfw_mode
         response = self.enhance(
             prompt=prompt,
@@ -413,7 +423,6 @@ class Script(scripts_manager.Script):
         if apply_prompt:
             return [response, response]
         return [response, gr.update()]
-    # --- END OF CORRECTED METHOD ---
 
     def get_custom(self, name):
         model_repo = self.options.models.get(name, {}).get('repo', None) or name
@@ -504,6 +513,7 @@ class Script(scripts_manager.Script):
         shared.state.begin('LLM')
         p.prompt = self.enhance(
             prompt=p.prompt,
+            seed=p.seed,
             image=self_image,
             prefix=prompt_prefix,
             suffix=prompt_suffix,