Update app.py

app.py

@@ -426,18 +426,15 @@ def encode_remote(prompt, references, canvas, num_frames, rewrite_prompt=False):
 
 
 @spaces.GPU(duration=get_duration, size=GPU_SIZE)
-def _generate(prompt_embeds, text_token_tags, references, height, width, num_frames, steps, seed, loras=()):
+def _generate(prompt_embeds, text_token_tags, references, height, width, num_frames, steps, seed):
     """The only thing on GPU time: the two reference encoders, the packed-sequence denoise loop and the decoders.
     References cross as paths and are decoded here; only the three generated outputs come back. A `@spaces.GPU`
     argument crosses a process boundary by pickling, a 5 s 1344x768 reference video is 370 MB of expanded frames, and
     the full `PipelineState` still holds the packed latents and the rotary grid on the card.
-    The adapters are attached here rather than in the caller: `spaces` runs this body in its own worker, so the
-    transformer the request sees is the one that has to carry them.
     """
     import torch
     if PLACEMENT == "lazy":
         PIPE.to("cuda")
-        apply_loras(PIPE.transformer_ref, loras or ())
     state = PIPE(
         prompt_embeds=prompt_embeds.to("cuda"),
         text_token_tags=text_token_tags,
@@ -680,16 +677,6 @@ with gr.Blocks(title="MiniMax-H3 Reference Custom Lora") as demo:
         image.change(auto_canvas, image, canvas, show_progress="hidden", api_name=False)
     video.change(auto_canvas, video, canvas, show_progress="hidden", api_name=False)
 
-    # `reference, strength, reference, strength, ...`, which is how `generate` unpacks them.
-    lora_inputs = [field for pair in zip(lora_references, lora_scales) for field in pair]
-    lora_upload.upload(_fill_lora_slots, [lora_upload, *lora_references], lora_references, api_name=False)
-    lora_preset_add.click(
-        _add_preset_lora,
-        [lora_preset, *lora_references, *lora_scales],
-        [*lora_references, *lora_scales, steps],
-        api_name=False,
-    )
-
     # Same order as `SETTINGS_KEYS`.
     settings_fields = [prompt, upsample, canvas, match, duration, steps, seed]
     save.click(save_settings, settings_fields, settings_download, api_name=False)