Use Sulphur LoRAs and strengthen prompt handling

app.py

@@ -28,6 +28,10 @@ OUTPUT_DIR = PERSISTENT / "sulphur_outputs"
 MAX_SEED = np.iinfo(np.int32).max
 DEFAULT_FRAME_RATE = 24.0
 DEFAULT_PROMPT = "Make this image come alive with cinematic motion, smooth animation."
+PROMPT_ADHERENCE_SUFFIX = (
+    "Follow the prompt literally. Keep the named subjects, actions, setting, style, "
+    "camera motion, and timing consistent with the user's request."
+)
 
 RESOLUTIONS = {
     "high": {"16:9": (1536, 1024), "9:16": (1024, 1536), "1:1": (1024, 1024)},
@@ -185,6 +189,37 @@ def _download(repo_id, filename, local_dir):
     return path
 
 
+def _safetensors_header(path):
+    with open(path, "rb") as reader:
+        header_len = struct.unpack("<Q", reader.read(8))[0]
+        return json.loads(reader.read(header_len).decode("utf-8"))
+
+
+def _validate_lora(name, lora):
+    path = Path(lora.path)
+    if not path.exists():
+        raise FileNotFoundError(f"{name} LoRA missing: {path}")
+    keys = [key for key in _safetensors_header(path) if key != "__metadata__"]
+    mapped = [lora.sd_ops.apply_to_key(key) for key in keys]
+    mapped = [key for key in mapped if key is not None]
+    lora_a = sum(1 for key in mapped if key.endswith(".lora_A.weight"))
+    lora_b = sum(1 for key in mapped if key.endswith(".lora_B.weight"))
+    alpha = sum(1 for key in mapped if key.endswith(".alpha"))
+    if lora_a == 0 or lora_b == 0:
+        raise RuntimeError(f"{name} LoRA has no usable lora_A/lora_B keys after mapping: {path}")
+    print(
+        f"[lora] active: {name} strength={lora.strength} path={path} "
+        f"lora_A={lora_a} lora_B={lora_b} alpha={alpha} size={path.stat().st_size / 1024**3:.2f}GB"
+    )
+
+
+def _prompt_for_model(prompt):
+    prompt = " ".join(str(prompt or DEFAULT_PROMPT).split())
+    if PROMPT_ADHERENCE_SUFFIX.lower() in prompt.lower():
+        return prompt
+    return f"{prompt}\n\n{PROMPT_ADHERENCE_SUFFIX}"
+
+
 def _ensure_gemma_model_alias(gemma_weight):
     gemma_weight = Path(gemma_weight)
     alias = gemma_weight.with_name("model.safetensors")
@@ -205,8 +240,8 @@ def download_assets():
         ASSETS_DIR / "loras",
     )
     distilled_lora = _download(
-        "DeepBeepMeep/LTX-2",
-        "ltx-2.3-22b-distilled-lora-384.safetensors",
+        "SulphurAI/Sulphur-2-base",
+        "distill_loras/ltx-2.3-22b-distilled-lora-1.1_fro90_ceil72_condsafe.safetensors",
         ASSETS_DIR / "loras",
     )
     upsampler = _download(
@@ -256,6 +291,8 @@ LORAS = [
     LoraPathStrengthAndSDOps(str(ASSETS["distilled_lora"]), 1.0, LTXV_LORA_COMFY_RENAMING_MAP),
     LoraPathStrengthAndSDOps(str(ASSETS["sulphur_lora"]), 1.0, LTXV_LORA_COMFY_RENAMING_MAP),
 ]
+_validate_lora("sulphur_distilled_condsafe", LORAS[0])
+_validate_lora("sulphur_experimental_lora_v1", LORAS[1])
 
 pipeline = DistilledPipeline(
     distilled_checkpoint_path=str(ASSETS["checkpoint"]),
@@ -319,8 +356,10 @@ def generate_video(
             num_frames = ((num_frames - 1 + 7) // 8) * 8 + 1
             height = int(height)
             width = int(width)
+            model_prompt = _prompt_for_model(prompt)
 
             print(f"[generate] {width}x{height}, frames={num_frames}, seed={current_seed}")
+            print(f"[prompt] {model_prompt}")
 
             images = []
             if input_image is not None:
@@ -334,7 +373,7 @@ def generate_video(
             log_memory("before pipeline")
 
             video, audio = pipeline(
-                prompt=prompt,
+                prompt=model_prompt,
                 seed=current_seed,
                 height=height,
                 width=width,
@@ -372,7 +411,7 @@ with gr.Blocks(title="Sulphur LTX Image to Video") as demo:
             with gr.Row():
                 duration = gr.Slider(1.0, 5.0, value=3.0, step=0.1, label="Duration")
                 with gr.Column():
-                    enhance_prompt = gr.Checkbox(label="Enhance Prompt", value=False)
+                    enhance_prompt = gr.Checkbox(label="Enhance Prompt", value=True)
                     high_res = gr.Checkbox(label="High Resolution", value=False)
 
             generate_btn = gr.Button("Generate", variant="primary")