Update app.py

app.py

@@ -1,7 +1,6 @@
 import os, torch
-from huggingface_hub import HfApi, hf_hub_download, create_repo
 from diffusers import StableDiffusionXLPipeline
-from diffusers.pipelines.stable_diffusion.convert_from_diffusers import convert_sdxl_checkpoint
+from huggingface_hub import HfApi, hf_hub_download, create_repo
 
 # ====================== CONFIG ======================
 BASE_MODEL = "stabilityai/stable-diffusion-xl-base-1.0"
@@ -41,23 +40,23 @@ print("🧬 Merging LoRA into model...")
 pipe.load_lora_weights(lora_path)
 pipe.fuse_lora()
 
-# Save temporary merged diffusers pipeline
-TMP_DIR = "merged_pipeline"
-print("💾 Saving temporary merged pipeline...")
-pipe.save_pretrained(TMP_DIR, safe_serialization=True)
-
-# Convert to single safetensors checkpoint
-print("🔄 Converting merged model to .safetensors checkpoint...")
-convert_sdxl_checkpoint(
-    TMP_DIR,
-    OUTPUT_CKPT,
-    use_safetensors=True
+# Save merged checkpoint
+print("💾 Saving merged model to safetensors...")
+pipe.save_pretrained(
+    "./out",
+    safe_serialization=True
 )
 
-print(f"✅ Checkpoint created: {OUTPUT_CKPT}")
+# Rename the main model file to deployment format
+for file in os.listdir("./out"):
+    if file.endswith(".safetensors"):
+        os.rename(f"./out/{file}", OUTPUT_CKPT)
+        break
+
+print(f"✅ Model saved as {OUTPUT_CKPT}")
 
 # Upload to Hugging Face
-print("☁️ Uploading model checkpoint to HF...")
+print("☁️ Uploading to Hugging Face...")
 api.upload_file(
     path_or_fileobj=OUTPUT_CKPT,
     path_in_repo=OUTPUT_CKPT,
@@ -65,6 +64,5 @@ api.upload_file(
     token=TOKEN
 )
 
-print("\n🎉 DONE!")
-print(f"✅ Your model will now support Inference API:")
-print(f"https://huggingface.co/{DEST_REPO}")
+print("\n🎉 DONE! Model is now inference-ready")
+print(f"✅ https://huggingface.co/{DEST_REPO}")