Update config.json

config.json

@@ -1,21 +1,25 @@
 {
-    "_class_name": "DiffusionPipeline",  // Replace with the appropriate class name for your model
-    "model_type": "Image-Generation",      // e.g., "unet", "autoencoder", etc.
-    "params": {
-        "num_train_timesteps": 1000,      // Number of timesteps for training
-        "image_size": 256,                 // Size of the images your model expects
-        "channels": 3,                     // Number of input channels (3 for RGB)
-        "use_fp16": true,                  // Use float16 precision (true/false)
-        "dropout": 0.1                     // Dropout rate, if applicable
-    },
-    "scheduler": {
-        "type": "pndm",                    // Scheduler type (e.g., "pndm", "ddim", etc.)
-        "beta_start": 0.00085,             // Start value for beta
-        "beta_end": 0.012,                 // End value for beta
-        "beta_schedule": "linear"           // Schedule for beta updates
-    },
-    "tokenizer": {
-        "type": "simple_tokenizer",         // Type of tokenizer used
-        "vocab_size": 30522                 // Size of the vocabulary
+    "model_type": "ImageGeneration",
+    "components": {
+        "unet": {
+            "_class_name": "UNet2DConditionModel",
+            "file": "unet/unet.bin"
+        },
+        "vae": {
+            "_class_name": "AutoencoderKL",
+            "file": "vae/vae.bin"
+        },
+        "scheduler": {
+            "_class_name": "DDIMScheduler",
+            "file": "scheduler/scheduler.json"
+        },
+        "text_encoder": {
+            "_class_name": "CLIPTextModel",
+            "file": "text_encoder/text_encoder.bin"
+        },
+        "tokenizer": {
+            "_class_name": "CLIPTokenizer",
+            "file": "tokenizer/tokenizer.json"
+        }
     }
 }