Model changed

app.py

@@ -124,6 +124,19 @@ default_local_storage = {
         "generation-mode": "image",
     }
 
+def load_transfomer():
+    print("Loading transformer ...")
+    transformer = HunyuanVideoTransformer3DModelPacked.from_pretrained(
+        "lllyasviel/FramePackI2V_HY", torch_dtype=torch.bfloat16
+    ).cpu()
+    transformer.eval()
+    transformer.high_quality_fp32_output_for_inference = True
+    print("transformer.high_quality_fp32_output_for_inference = True")
+
+    transformer.to(dtype=torch.bfloat16)
+    transformer.requires_grad_(False)
+    return transformer
+
 @torch.no_grad()
 def video_encode(video_path, resolution, no_resize, vae, vae_batch_size=16, device="cuda", width=None, height=None):
     """
@@ -319,6 +332,13 @@ def worker(input_image, image_position, prompts, n_prompt, seed, resolution, tot
         lora_multiplier,
         fp8_optimization):
     global transformer, previous_lora_file, previous_lora_multiplier, previous_fp8_optimization
+
+    model_changed = transformer is None or (
+        lora_file != previous_lora_file
+        or lora_multiplier != previous_lora_multiplier
+        or fp8_optimization != previous_fp8_optimization
+    )
+
     is_last_frame = (image_position == 100)
     def encode_prompt(prompt, n_prompt):
         llama_vec, clip_l_pooler = encode_prompt_conds(prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2)
@@ -559,6 +579,13 @@ def worker_video(input_video, prompts, n_prompt, seed, batch, resolution, total_
         lora_multiplier,
         fp8_optimization):
     global transformer, previous_lora_file, previous_lora_multiplier, previous_fp8_optimization
+
+    model_changed = transformer is None or (
+        lora_file != previous_lora_file
+        or lora_multiplier != previous_lora_multiplier
+        or fp8_optimization != previous_fp8_optimization
+    )
+
     def encode_prompt(prompt, n_prompt):
         llama_vec, clip_l_pooler = encode_prompt_conds(prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2)