Expose denoise parameter (0.1–1.0) in node UI

Maps directly to total_noise_levels (denoise * 1000). Default 0.9 matches
the original STAR inference script.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

nodes.py

@@ -311,7 +311,7 @@ class STARVideoSuperResolution:
                 }),
                 "prompt": ("STRING", {
                     "default": "", "multiline": True,
-                    "tooltip": "Text prompt describing the desired output. Leave empty to use STAR's built-in quality prompt.",
+                    "tooltip": "Optional text prompt. STAR's built-in quality prompt is always appended. Leave empty to use only the quality prompt.",
                 }),
                 "solver_mode": (["fast", "normal"], {
                     "default": "fast",
@@ -325,6 +325,10 @@ class STARVideoSuperResolution:
                     "default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF,
                     "tooltip": "Random seed for reproducible results.",
                 }),
+                "denoise": ("FLOAT", {
+                    "default": 0.9, "min": 0.1, "max": 1.0, "step": 0.05,
+                    "tooltip": "How much noise to add before denoising. 0.9 = STAR default (preserves input structure). 1.0 = full denoise (starts from pure noise). Lower values preserve more of the original.",
+                }),
                 "color_fix": (["adain", "wavelet", "none"], {
                     "default": "adain",
                     "tooltip": "Post-processing color correction. adain: match color stats from input. wavelet: preserve input low-frequency color. none: no correction.",
@@ -353,6 +357,7 @@ class STARVideoSuperResolution:
         solver_mode,
         max_chunk_len,
         seed,
+        denoise,
         color_fix,
         segment_size=0,
     ):
@@ -368,6 +373,7 @@ class STARVideoSuperResolution:
             solver_mode=solver_mode,
             max_chunk_len=max_chunk_len,
             seed=seed,
+            denoise=denoise,
             color_fix=color_fix,
         )
 

star_pipeline.py

@@ -141,6 +141,7 @@ def run_star_inference_segmented(
     solver_mode: str = "fast",
     max_chunk_len: int = 32,
     seed: int = 0,
+    denoise: float = 0.9,
     color_fix: str = "adain",
 ) -> torch.Tensor:
     """Run STAR inference in overlapping segments to bound peak RAM usage.
@@ -156,7 +157,8 @@ def run_star_inference_segmented(
         return run_star_inference(
             star_model=star_model, images=images, upscale=upscale, steps=steps,
             guide_scale=guide_scale, prompt=prompt, solver_mode=solver_mode,
-            max_chunk_len=max_chunk_len, seed=seed, color_fix=color_fix,
+            max_chunk_len=max_chunk_len, seed=seed, denoise=denoise,
+            color_fix=color_fix,
         )
 
     overlap = max(2, segment_size // 4)
@@ -195,6 +197,7 @@ def run_star_inference_segmented(
             solver_mode=solver_mode,
             max_chunk_len=max_chunk_len,
             seed=seed,
+            denoise=denoise,
             color_fix=color_fix,
         )
         # seg_result: [F_seg, H, W, 3] float32 on CPU
@@ -246,6 +249,7 @@ def run_star_inference(
     solver_mode: str = "fast",
     max_chunk_len: int = 32,
     seed: int = 0,
+    denoise: float = 0.9,
     color_fix: str = "adain",
 ) -> torch.Tensor:
     """Run STAR video super-resolution and return ComfyUI IMAGE batch."""
@@ -265,7 +269,7 @@ def run_star_inference(
     if offload == "aggressive":
         vae_dec_chunk = 1
 
-    total_noise_levels = 900
+    total_noise_levels = int(round(denoise * 1000))
 
     # -- Convert ComfyUI frames to STAR format --
     video_data = comfyui_to_star_frames(images)  # [F, 3, H, W]