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@@ -31,9 +31,6 @@ ENV OPENVINO_TELEMETRY_DIR=/app/openvino_cache
 RUN python -c "from optimum.intel.openvino import OVStableDiffusionPipeline; \
     OVStableDiffusionPipeline.from_pretrained('rupeshs/hyper-sd-sdxl-1-step-openvino-int8', ov_config={'CACHE_DIR': '/app/cache/openvino'})"
 
-# Pre-download a default LoRA model
-RUN python -c "from diffusers import LoraLoaderMixin; \
-    LoraLoaderMixin.download_lora_weights('latent-consistency/lcm-lora-sdxl', cache_dir='/app/cache/huggingface')"
 
 # Copy application code
 COPY app.py .

app.py

@@ -1,7 +1,6 @@
 import os
 from flask import Flask, request, jsonify, send_file
 from optimum.intel.openvino.modeling_diffusion import OVStableDiffusionPipeline
-from diffusers import LoraLoaderMixin
 from PIL import Image
 import io
 import torch
@@ -46,23 +45,23 @@ def generate_image():
         height = data.get('height', 512)
         num_inference_steps = data.get('num_inference_steps', 4)
         guidance_scale = data.get('guidance_scale', 1.0)
-        lora_model_id = data.get('lora_model_id', None)
-        lora_weight = data.get('lora_weight', 0.8)
+        # lora_model_id = data.get('lora_model_id', None)
+        # lora_weight = data.get('lora_weight', 0.8)
 
         # Load LoRA weights if specified
         local_pipeline = pipeline
-        if lora_model_id:
-            try:
-                local_pipeline = LoraLoaderMixin.load_lora_weights(
-                    local_pipeline,
-                    lora_model_id,
-                    lora_scale=lora_weight,
-                    cache_dir="/app/cache/huggingface"
-                )
-                logger.info(f"LoRA model {lora_model_id} loaded successfully")
-            except Exception as e:
-                logger.error(f"Failed to load LoRA model: {str(e)}")
-                return jsonify({'error': f"Failed to load LoRA model: {str(e)}"}), 400
+        # if lora_model_id:
+        #     try:
+        #         local_pipeline = LoraLoaderMixin.load_lora_weights(
+        #             local_pipeline,
+        #             lora_model_id,
+        #             lora_scale=lora_weight,
+        #             cache_dir="/app/cache/huggingface"
+        #         )
+        #         logger.info(f"LoRA model {lora_model_id} loaded successfully")
+        #     except Exception as e:
+        #         logger.error(f"Failed to load LoRA model: {str(e)}")
+        #         return jsonify({'error': f"Failed to load LoRA model: {str(e)}"}), 400
 
         # Generate image
         image = local_pipeline(