End of training

.gitattributes

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README.md

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+---
+base_model: stabilityai/stable-diffusion-2-1-base
+library_name: diffusers
+license: creativeml-openrail-m
+inference: true
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- diffusers
+- controlnet
+- diffusers-training
+---
+
+<!-- This model card has been generated automatically according to the information the training script had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+
+# controlnet-rookiewxy/model_out
+
+These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning.
+You can find some example images below.
+
+prompt: High-quality close-up dslr photo of man wearing a hat with trees in the background
+![images_0)](./images_0.png)
+prompt: Girl smiling, professional dslr photograph, dark background, studio lights, high quality
+![images_1)](./images_1.png)
+prompt: Portrait of a clown face, oil on canvas, bittersweet expression
+![images_2)](./images_2.png)
+
+
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+# TODO: add an example code snippet for running this diffusion pipeline
+```
+
+#### Limitations and bias
+
+[TODO: provide examples of latent issues and potential remediations]
+
+## Training details
+
+[TODO: describe the data used to train the model]

checkpoint-10000/controlnet/config.json

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+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
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+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
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+  "resnet_time_scale_shift": "default",
+  "transformer_layers_per_block": 1,
+  "upcast_attention": false,
+  "use_linear_projection": true
+}

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+  "transformer_layers_per_block": 1,
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+  "use_linear_projection": true
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config.json

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