minor: undo

app.py

@@ -3,7 +3,6 @@ import spaces
 import gradio as gr
 import torch
 import logging
-from diffusers import DiffusionPipeline
 from transformers import LlamaForCausalLM, PreTrainedTokenizerFast, BitsAndBytesConfig
 from transformer_hidream_image import HiDreamImageTransformer2DModel
 from pipeline_hidream_image import HiDreamImagePipeline
@@ -34,7 +33,7 @@ RESOLUTION_OPTIONS = [
     "1248 × 832 (Landscape)",
     "832 × 1248 (Portrait)"
 ]
-quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+# quantization_config = BitsAndBytesConfig(load_in_4bit=True)
 
 MODEL_PREFIX = "azaneko"
 LLAMA_MODEL_NAME = "hugging-quants/Meta-Llama-3.1-8B-Instruct-GPTQ-INT4"
@@ -72,7 +71,7 @@ pipe = HiDreamImagePipeline.from_pretrained(
     tokenizer_4=tokenizer_4,
     text_encoder_4=text_encoder_4,
     torch_dtype=torch.bfloat16,
-    quantization_config=quantization_config
+    # quantization_config=quantization_config
 )
 pipe.transformer = transformer
 log_vram("✅ Pipeline loaded!")
@@ -149,7 +148,7 @@ def parse_resolution(resolution_str):
 @spaces.GPU()
 def generate_image(pipe: HiDreamImagePipeline, model_type: str, prompt: str, resolution: tuple[int, int], seed: int):
     # Get configuration for current model
-    config = MODEL_CONFIGS[model_type]
+    # config = MODEL_CONFIGS[model_type]
     guidance_scale = 0.0
     num_inference_steps = 16