End of training 2k + 2.5k Samples

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 images_0.png filter=lfs diff=lfs merge=lfs -text
 images_1.png filter=lfs diff=lfs merge=lfs -text
+images_2.png filter=lfs diff=lfs merge=lfs -text

10000/diffusion_flax_model.msgpack

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25000/config.json

@@ -0,0 +1,33 @@
+{
+  "_class_name": "FlaxControlNetModel",
+  "_diffusers_version": "0.16.0.dev0",
+  "attention_head_dim": 8,
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "conditioning_embedding_out_channels": [
+    16,
+    32,
+    96,
+    256
+  ],
+  "controlnet_conditioning_channel_order": "rgb",
+  "cross_attention_dim": 768,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "dropout": 0.0,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "only_cross_attention": false,
+  "sample_size": 32,
+  "use_linear_projection": false
+}

25000/diffusion_flax_model.msgpack

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README.md

@@ -16,8 +16,13 @@ inference: true
 
 These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images in the following. 
 
-prompt: modern main room of a house
+prompt: contemporary living room of a house
+negative prompt: low quality
 ![images_0)](./images_0.png)
-prompt: a street full of people waiting for a car to pass
+prompt: new york buildings,  Vincent Van Gogh starry night 
+negative prompt: low quality, monochrome
 ![images_1)](./images_1.png)
+prompt: contemporary living room,  high quality, 4k, realistic
+negative prompt: low quality, monochrome, low res
+![images_2)](./images_2.png)
 

diffusion_flax_model.msgpack

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