read me + examples demo

FaceEnhancementProdApi.json

@@ -1,403 +0,0 @@
-{
-  "1": {
-    "inputs": {
-      "noise": [
-        "39",
-        0
-      ],
-      "guider": [
-        "122",
-        0
-      ],
-      "sampler": [
-        "50",
-        0
-      ],
-      "sigmas": [
-        "131",
-        0
-      ],
-      "latent_image": [
-        "35",
-        0
-      ]
-    },
-    "class_type": "SamplerCustomAdvanced",
-    "_meta": {
-      "title": "SamplerCustomAdvanced"
-    }
-  },
-  "23": {
-    "inputs": {
-      "text": "",
-      "clip": [
-        "94",
-        0
-      ]
-    },
-    "class_type": "CLIPTextEncode",
-    "_meta": {
-      "title": "CLIP Text Encode (Prompt)"
-    }
-  },
-  "24": {
-    "inputs": {
-      "image": "woman_face.jpg"
-    },
-    "class_type": "LoadImage",
-    "_meta": {
-      "title": "Load Image"
-    }
-  },
-  "35": {
-    "inputs": {
-      "pixels": [
-        "40",
-        0
-      ],
-      "vae": [
-        "95",
-        0
-      ]
-    },
-    "class_type": "VAEEncode",
-    "_meta": {
-      "title": "VAE Encode"
-    }
-  },
-  "37": {
-    "inputs": {
-      "strength": 1,
-      "start_percent": 0.1,
-      "end_percent": 0.8,
-      "positive": [
-        "42",
-        0
-      ],
-      "negative": [
-        "23",
-        0
-      ],
-      "control_net": [
-        "41",
-        0
-      ],
-      "image": [
-        "40",
-        0
-      ],
-      "vae": [
-        "95",
-        0
-      ]
-    },
-    "class_type": "ControlNetApplyAdvanced",
-    "_meta": {
-      "title": "Apply ControlNet"
-    }
-  },
-  "39": {
-    "inputs": {
-      "noise_seed": 1
-    },
-    "class_type": "RandomNoise",
-    "_meta": {
-      "title": "RandomNoise"
-    }
-  },
-  "40": {
-    "inputs": {
-      "image": "woman1_gpt_2.png"
-    },
-    "class_type": "LoadImage",
-    "_meta": {
-      "title": "Load Image"
-    }
-  },
-  "41": {
-    "inputs": {
-      "type": "tile",
-      "control_net": [
-        "49",
-        0
-      ]
-    },
-    "class_type": "SetUnionControlNetType",
-    "_meta": {
-      "title": "SetUnionControlNetType"
-    }
-  },
-  "42": {
-    "inputs": {
-      "text": "",
-      "clip": [
-        "94",
-        0
-      ]
-    },
-    "class_type": "CLIPTextEncode",
-    "_meta": {
-      "title": "CLIP Text Encode (Prompt)"
-    }
-  },
-  "44": {
-    "inputs": {
-      "pulid_file": "pulid_flux_v0.9.1.safetensors"
-    },
-    "class_type": "PulidFluxModelLoader",
-    "_meta": {
-      "title": "Load PuLID Flux Model"
-    }
-  },
-  "45": {
-    "inputs": {},
-    "class_type": "PulidFluxEvaClipLoader",
-    "_meta": {
-      "title": "Load Eva Clip (PuLID Flux)"
-    }
-  },
-  "46": {
-    "inputs": {
-      "provider": "CUDA"
-    },
-    "class_type": "PulidFluxInsightFaceLoader",
-    "_meta": {
-      "title": "Load InsightFace (PuLID Flux)"
-    }
-  },
-  "49": {
-    "inputs": {
-      "control_net_name": "Flux_Dev_ControlNet_Union_Pro_ShakkerLabs.safetensors"
-    },
-    "class_type": "ControlNetLoader",
-    "_meta": {
-      "title": "Load ControlNet Model"
-    }
-  },
-  "50": {
-    "inputs": {
-      "sampler_name": "euler"
-    },
-    "class_type": "KSamplerSelect",
-    "_meta": {
-      "title": "KSamplerSelect"
-    }
-  },
-  "93": {
-    "inputs": {
-      "unet_name": "flux1-dev.safetensors",
-      "weight_dtype": "default"
-    },
-    "class_type": "UNETLoader",
-    "_meta": {
-      "title": "Load Diffusion Model"
-    }
-  },
-  "94": {
-    "inputs": {
-      "clip_name1": "t5xxl_fp16.safetensors",
-      "clip_name2": "clip_l.safetensors",
-      "type": "flux",
-      "device": "default"
-    },
-    "class_type": "DualCLIPLoader",
-    "_meta": {
-      "title": "DualCLIPLoader"
-    }
-  },
-  "95": {
-    "inputs": {
-      "vae_name": "ae.safetensors"
-    },
-    "class_type": "VAELoader",
-    "_meta": {
-      "title": "Load VAE"
-    }
-  },
-  "114": {
-    "inputs": {
-      "samples": [
-        "1",
-        0
-      ],
-      "vae": [
-        "95",
-        0
-      ]
-    },
-    "class_type": "VAEDecode",
-    "_meta": {
-      "title": "VAE Decode"
-    }
-  },
-  "117": {
-    "inputs": {
-      "similarity_metric": "cosine",
-      "filter_thresh": 100,
-      "filter_best": 0,
-      "generate_image_overlay": true,
-      "analysis_models": [
-        "118",
-        0
-      ],
-      "reference": [
-        "24",
-        0
-      ],
-      "image": [
-        "114",
-        0
-      ]
-    },
-    "class_type": "FaceEmbedDistance",
-    "_meta": {
-      "title": "Face Embeds Distance"
-    }
-  },
-  "118": {
-    "inputs": {
-      "library": "insightface",
-      "provider": "CUDA"
-    },
-    "class_type": "FaceAnalysisModels",
-    "_meta": {
-      "title": "Face Analysis Models"
-    }
-  },
-  "121": {
-    "inputs": {
-      "output": "",
-      "source": [
-        "117",
-        1
-      ]
-    },
-    "class_type": "Display Any (rgthree)",
-    "_meta": {
-      "title": "Display Any (rgthree)"
-    }
-  },
-  "122": {
-    "inputs": {
-      "model": [
-        "133",
-        0
-      ],
-      "conditioning": [
-        "37",
-        0
-      ]
-    },
-    "class_type": "BasicGuider",
-    "_meta": {
-      "title": "BasicGuider"
-    }
-  },
-  "123": {
-    "inputs": {
-      "rgthree_comparer": {
-        "images": [
-          {
-            "name": "A",
-            "selected": true,
-            "url": "/api/view?filename=rgthree.compare._temp_bikvi_00001_.png&type=temp&subfolder=&rand=0.3573763312590408"
-          },
-          {
-            "name": "B",
-            "selected": true,
-            "url": "/api/view?filename=rgthree.compare._temp_bikvi_00002_.png&type=temp&subfolder=&rand=0.1379430430729296"
-          }
-        ]
-      },
-      "image_a": [
-        "40",
-        0
-      ],
-      "image_b": [
-        "114",
-        0
-      ]
-    },
-    "class_type": "Image Comparer (rgthree)",
-    "_meta": {
-      "title": "Image Comparer (rgthree)"
-    }
-  },
-  "128": {
-    "inputs": {
-      "filename_prefix": "FaceEnhanced",
-      "images": [
-        "114",
-        0
-      ]
-    },
-    "class_type": "SaveImage",
-    "_meta": {
-      "title": "Save Image"
-    }
-  },
-  "129": {
-    "inputs": {
-      "filename_prefix": "FaceEmbedDist",
-      "images": [
-        "117",
-        0
-      ]
-    },
-    "class_type": "SaveImage",
-    "_meta": {
-      "title": "Save Image"
-    }
-  },
-  "131": {
-    "inputs": {
-      "scheduler": "beta",
-      "steps": 28,
-      "denoise": 0.75,
-      "model": [
-        "133",
-        0
-      ]
-    },
-    "class_type": "BasicScheduler",
-    "_meta": {
-      "title": "BasicScheduler"
-    }
-  },
-  "133": {
-    "inputs": {
-      "weight": 0.7500000000000001,
-      "start_at": 0.10000000000000002,
-      "end_at": 1,
-      "fusion": "mean",
-      "fusion_weight_max": 1,
-      "fusion_weight_min": 0,
-      "train_step": 1000,
-      "use_gray": true,
-      "model": [
-        "93",
-        0
-      ],
-      "pulid_flux": [
-        "44",
-        0
-      ],
-      "eva_clip": [
-        "45",
-        0
-      ],
-      "face_analysis": [
-        "46",
-        0
-      ],
-      "image": [
-        "24",
-        0
-      ]
-    },
-    "class_type": "ApplyPulidFlux",
-    "_meta": {
-      "title": "Apply PuLID Flux"
-    }
-  }
-}

gradio_demo.py → demo.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import os
 import tempfile
-from main import process_face
+# from main import process_face
 from PIL import Image
 
 def enhance_face_gradio(input_image, ref_image):
@@ -30,13 +30,14 @@ def enhance_face_gradio(input_image, ref_image):
     
     try:
         # Process the face
-        process_face(
-            input_path=input_path,
-            ref_path=ref_path,
-            crop=False,
-            upscale=False,
-            output_path=output_path
-        )
+        # process_face(
+        #     input_path=input_path,
+        #     ref_path=ref_path,
+        #     crop=False,
+        #     upscale=False,
+        #     output_path=output_path
+        # )
+        pass
     except Exception as e:
         # Handle the error, log it, and return an error message
         print(f"Error processing face: {e}")
@@ -52,12 +53,23 @@ def enhance_face_gradio(input_image, ref_image):
 def create_gradio_interface():
     # Create the Gradio interface
     with gr.Blocks(title="Face Enhancement Demo") as demo:
-        gr.Markdown("# Face Enhancement Demo")
-        gr.Markdown("Upload an input image and a reference face image to enhance the input.")
-        
+        # Add instructions at the top
+        gr.Markdown("""
+        # Face Enhancement Demo
+        ### Instructions
+        1. Upload an image you want to enhance
+        2. Upload a reference face image
+        3. Click 'Enhance Face' to start the process
+        4. Processing takes about 60 seconds
+        """, elem_id="instructions")
+
+        # Add a horizontal line for separation
+        gr.Markdown("---")
+
+        # Main interface layout
         with gr.Row():
             with gr.Column():
-                input_image = gr.Image(label="Input Image", type="pil")
+                input_image = gr.Image(label="Target Image", type="pil")
                 ref_image = gr.Image(label="Reference Face", type="pil")
                 enhance_button = gr.Button("Enhance Face")
             
@@ -70,14 +82,13 @@ def create_gradio_interface():
             outputs=output_image,
             queue=True  # Enable queue for sequential processing
         )
-        
-        gr.Markdown("""
-        ## Instructions
-        1. Upload an image you want to enhance
-        2. Upload a reference face image
-        3. Click 'Enhance Face' to start the process
-        4. Processing takes about 60 seconds
-        """)
+
+        # Add examples at the bottom in a table format
+        with gr.Row():
+            gr.Markdown("### Examples")
+            with gr.Column():
+                gr.Image("examples/chatgpt_dany_1.png", label="Target Image Example")
+                gr.Image("examples/dany_face.jpg", label="Reference Face Example")
 
     # Launch the Gradio app with queue
     demo.queue(max_size=20)  # Configure queue size