update config

README.md

@@ -24,7 +24,7 @@ tags:
 
 ```python
 !pip install git+https://github.com/huggingface/diffusers.git
-from diffusers import UNetUnconditionalModel, DDIMScheduler, VQModel
+from diffusers import UNet2DModel, DDIMScheduler, VQModel
 import torch
 import PIL.Image
 import numpy as np
@@ -33,7 +33,7 @@ import tqdm
 seed = 3
 
 # load all models
-unet = UNetUnconditionalModel.from_pretrained("CompVis/latent-diffusion-celeba-256", subfolder="unet")
+unet = UNet2DModel.from_pretrained("CompVis/latent-diffusion-celeba-256", subfolder="unet")
 vqvae = VQModel.from_pretrained("CompVis/latent-diffusion-celeba-256", subfolder="vqvae")
 scheduler = DDIMScheduler.from_config("CompVis/latent-diffusion-celeba-256", subfolder="scheduler")
 

model_index.json

@@ -7,7 +7,7 @@
   ],
   "unet": [
     "diffusers",
-    "UNetUnconditionalModel"
+    "UNet2DModel"
   ],
   "vqvae": [
     "diffusers",

run.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-from diffusers import UNetUnconditionalModel, DDIMScheduler, VQModel
+from diffusers import UNet2DModel, DDIMScheduler, VQModel
 import torch
 import PIL.Image
 import numpy as np
@@ -10,7 +10,7 @@ seed = 3
 # 1. Unroll the full loop
 # ==================================================================
 # load all models
-unet = UNetUnconditionalModel.from_pretrained("./", subfolder="unet")
+unet = UNet2DModel.from_pretrained("./", subfolder="unet")
 vqvae = VQModel.from_pretrained("./", subfolder="vqvae")
 scheduler = DDIMScheduler.from_config("./", subfolder="scheduler")
 

unet/config.json

@@ -1,40 +1,49 @@
 {
-  "_class_name": "UNetUnconditionalModel",
+  "_class_name": "UNet2DModel",
   "_diffusers_version": "0.0.4",
-  "attention_resolutions": [
-    8,
-    4,
-    2
-  ],
-  "attn_resolutions": null,
+  "act_fn": "silu",
+  "attention_head_dim": 32,
   "block_channels": [
     224,
     448,
     672,
     896
   ],
-  "conv_resample": true,
-  "ddpm": false,
+  "block_out_channels": [
+    224,
+    448,
+    672,
+    896
+  ],
+  "center_input_sample": false,
   "down_blocks": [
-    "UNetResDownBlock2D",
-    "UNetResAttnDownBlock2D",
-    "UNetResAttnDownBlock2D",
-    "UNetResAttnDownBlock2D"
+    "DownBlock2D",
+    "AttnDownBlock2D",
+    "AttnDownBlock2D",
+    "AttnDownBlock2D"
   ],
   "downsample_padding": 1,
   "downscale_freq_shift": 0,
-  "dropout": 0,
   "flip_sin_to_cos": true,
-  "image_size": 64,
+  "freq_shift": 0,
+  "image_size": null,
   "in_channels": 3,
-  "name_or_path": "fusing/latent-diffusion-celeba-256",
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
   "num_head_channels": 32,
-  "num_res_blocks": 2,
+  "num_res_blocks": null,
   "out_channels": 3,
+  "resnet_act_fn": "silu",
+  "resnet_eps": 1e-05,
+  "resnet_num_groups": 32,
+  "sample_size": 64,
+  "time_embedding_type": "positional",
   "up_blocks": [
-    "UNetResAttnUpBlock2D",
-    "UNetResAttnUpBlock2D",
-    "UNetResAttnUpBlock2D",
-    "UNetResUpBlock2D"
+    "AttnUpBlock2D",
+    "AttnUpBlock2D",
+    "AttnUpBlock2D",
+    "UpBlock2D"
   ]
 }

unet/{diffusion_model.pt → diffusion_pytorch_model.bin}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 1096368033
+oid sha256:9302717f933ebf63fd2f35b7311e558d8d08eec2df6d68d4e925c1dde5509604
+size 1096382177