main

gradio_app.py

@@ -29,7 +29,7 @@ login(token=hf_token)
 model_paths = {
     'Wood Sculpture': {
         'BASE_FLUX_CHECKPOINT': "showlab/makeanything",
-        'BASE_FILE': "flux_merge_lora/flux_merge_4f_wood-fp8_e4m3fn.safetensors",
+        'BASE_FILE': "flux_merge_lora/flux_merge_4f_wood_sculpture-fp8_e4m3fn.safetensors",
         'LORA_REPO': "showlab/makeanything",
         'LORA_FILE': "recraft/recraft_4f_wood_sculpture.safetensors",
         "Frame": 4
@@ -186,7 +186,7 @@ def infer(prompt, sample_image, recraft_model, seed=0):
     # lora_model.eval()
     # lora_model.to(device)
 
-    logger.debug(f"Using seed: {seed}")
+    logger.info(f"Using seed: {seed}")
 
     # Preprocess the conditional image
     resize_transform = ResizeWithPadding(size=512) if frame_num == 4 else ResizeWithPadding(size=352)
@@ -233,6 +233,7 @@ def infer(prompt, sample_image, recraft_model, seed=0):
     packed_latent_height = height // 16
     packed_latent_width = width // 16
     
+    torch.manual_seed(seed)
     noise = torch.randn(1, packed_latent_height * packed_latent_width, 16 * 2 * 2, device=device, dtype=torch.float16)
     logger.debug("Noise prepared.")