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efficient_net/config.json

@@ -1,6 +1,6 @@
 {
   "_class_name": "EfficientNetEncoder",
-  "_diffusers_version": "0.18.0.dev0",
+  "_diffusers_version": "0.19.0.dev0",
   "c_latent": 16,
   "effnet": "efficientnet_v2_s"
 }

generator/config.json

@@ -1,6 +1,6 @@
 {
   "_class_name": "DiffNeXt",
-  "_diffusers_version": "0.18.0.dev0",
+  "_diffusers_version": "0.19.0.dev0",
   "blocks": [
     4,
     4,

model_index.json

@@ -1,6 +1,6 @@
 {
   "_class_name": "WuerstchenGeneratorPipeline",
-  "_diffusers_version": "0.18.0.dev0",
+  "_diffusers_version": "0.19.0.dev0",
   "efficient_net": [
     "wuerstchen",
     "EfficientNetEncoder"
@@ -15,6 +15,6 @@
   ],
   "vqgan": [
     "diffusers",
-    "PaellaVQModel"
+    "VQModelPaella"
   ]
 }

scheduler/scheduler_config.json

@@ -1,16 +1,18 @@
 {
   "_class_name": "DDPMScheduler",
-  "_diffusers_version": "0.18.0.dev0",
+  "_diffusers_version": "0.19.0.dev0",
   "beta_end": 0.02,
   "beta_schedule": "squaredcos_cap_v2",
   "beta_start": 0.0001,
-  "clip_sample": true,
+  "clip_sample": false,
   "clip_sample_range": 1.0,
   "dynamic_thresholding_ratio": 0.995,
   "num_train_timesteps": 1000,
   "prediction_type": "epsilon",
   "sample_max_value": 1.0,
+  "steps_offset": 0,
   "thresholding": false,
+  "timestep_spacing": "leading",
   "trained_betas": null,
   "variance_type": "fixed_small"
 }

vqgan/config.json

@@ -1,6 +1,6 @@
 {
-  "_class_name": "PaellaVQModel",
-  "_diffusers_version": "0.18.0.dev0",
+  "_class_name": "VQModelPaella",
+  "_diffusers_version": "0.19.0.dev0",
   "bottleneck_blocks": 12,
   "c_hidden": 384,
   "c_latent": 4,