Update app.py

app.py

@@ -11,16 +11,16 @@ from PIL import Image, ImageFilter, ImageOps
 from huggingface_hub import login, hf_hub_download
 import os
 
-# Log in to Hugging Face for private repo access
+# Log auth config
 if "HF_TOKEN" in os.environ:
     login(os.environ["HF_TOKEN"])
 else:
     raise ValueError("HF_TOKEN not found in environment variables. Please set it in Space settings.")
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-# Download the private model file
-repo_id = "DreamingOracle/Quagmaform_alpha-1"  # Your private repo
-filename = "DPS_Quagmaform_Alpha1.safetensors"  # Your model file
+# dl
+repo_id = "DreamingOracle/Quagmaform_alpha-1"  # dl proprietary
+filename = "DPS_Quagmaform_Alpha1.safetensors"  # dl from source proprietary
 model_path = hf_hub_download(repo_id=repo_id, filename=filename)
 if torch.cuda.is_available(): 
     torch_dtype = torch.float16
@@ -129,8 +129,8 @@ def infer(
 
 examples = [ 
     "photorealistic portrait of a young woman, cinematic rim lighting, soft golden hour backlight, detailed skin pores, realistic eyelashes, 85mm lens, shallow depth of field, ultra-detailed, high dynamic range, film grain, detailed, 8k", 
-    "full body portrait of a futuristic armored soldier, worn brushed metal armor with neon blue accents, realistic cloth under-armor, weathering and scratches, volumetric rim light, cinematic pose, high detail, photoreal", 
-    "neon cyberpunk street at night, wet pavement reflecting lights, pedestrians with umbrellas, dense signage in the distance, cinematic composition, realistic depth, crisp details, atmospheric fog, long lens compression",] 
+    "head helmet portrait of a futuristic armored soldier, worn brushed metal armor with neon blue accents, realistic cloth under-armor, weathering and scratches, volumetric rim light, cinematic pose, high detail, photoreal", 
+    "silver bars stacked in a metal case, neon cyberpunk theme, rainbow glare effect, cinematic composition, realistic depth, crisp details",] 
 
 css = """#col-container { margin: 0 auto; max-width: 640px;}""" 
 
@@ -153,17 +153,17 @@ with gr.Blocks(css=css) as demo:
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True) 
             with gr.Row(): 
                 width = gr.Slider( 
-                    label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=768, # Replace with defaults that work for your model 
+                    label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=768,
                 ) 
                 height = gr.Slider( 
-                    label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, # Replace with defaults that work for your model 
+                    label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, 
                 ) 
             with gr.Row(): 
                 guidance_scale = gr.Slider( 
-                    label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=5, # Replace with defaults that work for your model 
+                    label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=5, 
                 ) 
                 num_inference_steps = gr.Slider( 
-                    label="Number of inference steps", minimum=1, maximum=50, step=1, value=22, # Replace with defaults that work for your model 
+                    label="Number of inference steps", minimum=1, maximum=50, step=1, value=22, 
                 ) 
             scheduler = gr.Dropdown( 
                 label="Sampler/Scheduler",