Update app.py

app.py

@@ -37,20 +37,30 @@ ATTENTION = os.environ.get("H3_ATTENTION", "_native_cudnn").lower()
 GPU_SIZE = os.environ.get("H3_GPU_SIZE", "xlarge")
 
 LORA_REPO = os.environ.get("H3_LORA_REPO", "dagloop5/LoRA")
+# Each entry is (repo, filename) so a LoRA can come from any repo, not just LORA_REPO — the two Lightx2v files
+# live in lightx2v/Minimax-h3-Turbo, not dagloop5/LoRA.
 LORA_FILES = {
-    "lora1": os.environ.get("H3_LORA_1_FILE", "minimax_h3_turbo_v4_step600_ema.safetensors"),
-    "loraa": os.environ.get("H3_LORA_A_FILE", "Mylo_lora_epoch31.safetensors"),
-    "lorab": os.environ.get("H3_LORA_B_FILE", "VBVR_H3_attn_only.safetensors"),
-    "lorac": os.environ.get("H3_LORA_C_FILE", "AIO_V2.safetensors"),
-    "lorad": os.environ.get("H3_LORA_D_FILE", "Furry enhancer Video H3 V2.54.safetensors"),
-    "lorae": os.environ.get("H3_LORA_E_FILE", "sb_H3_i2v_v1.1.safetensors"),
-    "loraf": os.environ.get("H3_LORA_F_FILE", "moawxx_000002000.safetensors"),
-    "lorag": os.environ.get("H3_LORA_G_FILE", "H3_ref2va_shot_v1_fp16.safetensors"),
+    "lora1": (LORA_REPO, os.environ.get("H3_LORA_1_FILE", "minimax_h3_turbo_v4_step600_ema.safetensors")),
+    "loraa": (LORA_REPO, os.environ.get("H3_LORA_A_FILE", "Mylo_lora_epoch31.safetensors")),
+    "lorab": (LORA_REPO, os.environ.get("H3_LORA_B_FILE", "VBVR_H3_attn_only.safetensors")),
+    "lorac": (LORA_REPO, os.environ.get("H3_LORA_C_FILE", "AIO_V2.safetensors")),
+    "lorad": (LORA_REPO, os.environ.get("H3_LORA_D_FILE", "Furry enhancer Video H3 V2.54.safetensors")),
+    "lorae": (LORA_REPO, os.environ.get("H3_LORA_E_FILE", "sb_H3_i2v_v1.1.safetensors")),
+    "loraf": (LORA_REPO, os.environ.get("H3_LORA_F_FILE", "moawxx_000002000.safetensors")),
+    "lorag": (LORA_REPO, os.environ.get("H3_LORA_G_FILE", "H3_ref2va_shot_v1_fp16.safetensors")),
+    "lorah": (
+        os.environ.get("H3_LORA_H_REPO", "lightx2v/Minimax-h3-Turbo"),
+        os.environ.get("H3_LORA_H_FILE", "minimax_h3_fl2v_turbo_4step_v1.1_768p_bf16.safetensors"),
+    ),
+    "lorai": (
+        os.environ.get("H3_LORA_I_REPO", "lightx2v/Minimax-h3-Turbo"),
+        os.environ.get("H3_LORA_I_FILE", "minimax_h3_fl2v_turbo_8step_v1.0_bf16.safetensors"),
+    ),
 }
 # Display names, keyed the same as LORA_FILES — used in the UI slider labels, the per-request report line, and
 # the status line's failure list. Keep these two dicts' keys in sync when adding a LoRA.
 LORA_LABELS = {
-    "lora1": "Distilled LoRA",
+    "lora1": "Larryvrh-MiniMax-H3 Turbo LoRA",
     "loraa": "Anthro Enhancer",
     "lorab": "Reasoning Enhancer",
     "lorac": "HM-AIO", # hmmotion
@@ -58,6 +68,8 @@ LORA_LABELS = {
     "lorae": "SB",
     "loraf": "Moaxx", # moawxx
     "lorag": "Fluid Enhancer",
+    "lorah": "Lightx2v-Minimax-H3 Turbo 768p LoRA",
+    "lorai": "Lightx2v-Minimax-H3 Turbo 8-step LoRA",
 }
 DEFAULT_LORA_1_STRENGTH = 0.0
 DEFAULT_LORA_A_STRENGTH = 0.0
@@ -67,6 +79,8 @@ DEFAULT_LORA_D_STRENGTH = 0.0
 DEFAULT_LORA_E_STRENGTH = 0.0
 DEFAULT_LORA_F_STRENGTH = 0.0
 DEFAULT_LORA_G_STRENGTH = 0.0
+DEFAULT_LORA_H_STRENGTH = 0.0
+DEFAULT_LORA_I_STRENGTH = 0.0
 # Per-LoRA, not global: different training pipelines can store SwiGLU's fc1 gate/value halves in either order,
 # and one flag can only be right for however many of the 8 files happen to agree. `lora1` (the Distilled/Turbo
 # LoRA) is confirmed needing the swap by InstantX's official conversion of the same lineage
@@ -363,9 +377,9 @@ def load_models() -> str | None:
             from huggingface_hub import hf_hub_download
             from safetensors import safe_open
 
-            for filename in LORA_FILES.values():
+            for repo, filename in LORA_FILES.values():
                 try:
-                    path = hf_hub_download(LORA_REPO, filename)
+                    path = hf_hub_download(repo, filename)
                     with safe_open(path, framework="pt") as handle:
                         keys = sorted(handle.keys())
                         print(f"[lora-debug] {filename}: {len(keys)} keys", flush=True)
@@ -404,9 +418,9 @@ def load_models() -> str | None:
             # `_convert_diffusion_model_lora` for why this can't be read fresh per-file.
             base_shapes = {k: tuple(v.shape) for k, v in pipe.transformer.state_dict().items()}
             failures = []
-            for name, filename in LORA_FILES.items():
+            for name, (repo, filename) in LORA_FILES.items():
                 try:
-                    path = hf_hub_download(LORA_REPO, filename)
+                    path = hf_hub_download(repo, filename)
                     with safe_open(path, framework="pt") as handle:
                         raw = {k: handle.get_tensor(k) for k in handle.keys()}
                     converted, network_alphas = _convert_diffusion_model_lora(
@@ -683,6 +697,8 @@ def generate(
     seed=42,
     upsample=False,
     lora_1_strength=DEFAULT_LORA_1_STRENGTH,
+    lora_h_strength=DEFAULT_LORA_H_STRENGTH,
+    lora_i_strength=DEFAULT_LORA_I_STRENGTH,
     lora_a_strength=DEFAULT_LORA_A_STRENGTH,
     lora_b_strength=DEFAULT_LORA_B_STRENGTH,
     lora_c_strength=DEFAULT_LORA_C_STRENGTH,
@@ -735,7 +751,7 @@ def generate(
 
     # Every UI LoRA slider gets packed into one dict here — this is the only place a new LoRA's slider value
     # needs wiring in; `_generate`, `set_adapters`, and the report line below are all keyed off this dict.
-    lora_strengths = {"lora1": float(lora_1_strength), "loraa": float(lora_a_strength), "lorab": float(lora_b_strength), "lorac": float(lora_c_strength), "lorad": float(lora_d_strength), "lorae": float(lora_e_strength), "loraf": float(lora_f_strength), "lorag": float(lora_g_strength)}
+    lora_strengths = {"lora1": float(lora_1_strength), "lorah": float(lora_h_strength), "lorai": float(lora_i_strength), "loraa": float(lora_a_strength), "lorab": float(lora_b_strength), "lorac": float(lora_c_strength), "lorad": float(lora_d_strength), "lorae": float(lora_e_strength), "loraf": float(lora_f_strength), "lorag": float(lora_g_strength)}
 
     progress(0.1, desc=f"Denoising {int(steps)} steps at {width}x{height}, {num_frames} frames ...")
     call = (
@@ -938,13 +954,31 @@ with gr.Blocks(title="PlagueKind · MiniMax-H3") as demo:
         with gr.Column():
             video = gr.Video(label="Video + soundtrack")
             report = gr.Markdown()
-            lora_1_strength = gr.Slider(
-                label="Distilled LoRA",
-                minimum=0.0,
-                maximum=2.0,
-                step=0.05,
-                value=DEFAULT_LORA_1_STRENGTH,
-            )
+            with gr.Accordion("Distilled / Turbo LoRAs", open=False):
+                lora_1_strength = gr.Slider(
+                    label="Larryvrh-MiniMax-H3 Turbo LoRA",
+                    minimum=0.0,
+                    maximum=2.0,
+                    step=0.05,
+                    value=DEFAULT_LORA_1_STRENGTH,
+                    info="Video/Audio Shift = 6/3",
+                )
+                lora_h_strength = gr.Slider(
+                    label="Lightx2v-Minimax-H3 Turbo 768p LoRA",
+                    minimum=0.0,
+                    maximum=2.0,
+                    step=0.05,
+                    value=DEFAULT_LORA_H_STRENGTH,
+                    info="Video/Audio Shift = 6/3",
+                )
+                lora_i_strength = gr.Slider(
+                    label="Lightx2v-Minimax-H3 Turbo 8-step LoRA",
+                    minimum=0.0,
+                    maximum=2.0,
+                    step=0.05,
+                    value=DEFAULT_LORA_I_STRENGTH,
+                    info="Video/Audio Shift = 12/3",
+                )
             with gr.Accordion("Custom LoRAs", open=False):
                 lora_a_strength = gr.Slider(
                     label="Anthro Enhancer LoRA",
@@ -1012,6 +1046,8 @@ with gr.Blocks(title="PlagueKind · MiniMax-H3") as demo:
         seed,
         upsample,
         lora_1_strength,
+        lora_h_strength,
+        lora_i_strength,
         lora_a_strength,
         lora_b_strength,
         lora_c_strength,