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AmritaBha
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sd15_fill_mscoco

Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
diffusers-training
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  1. .gitattributes +3 -0
  2. README.md +47 -0
  3. checkpoint-10000/controlnet/config.json +52 -0
  4. checkpoint-10000/controlnet/diffusion_pytorch_model.safetensors +3 -0
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.gitattributes CHANGED
@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ license: creativeml-openrail-m
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+ library_name: diffusers
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+ tags:
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+ - stable-diffusion
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+ - stable-diffusion-diffusers
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+ - text-to-image
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+ - diffusers
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+ - controlnet
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+ - diffusers-training
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+ base_model: runwayml/stable-diffusion-v1-5
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+ inference: true
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the training script had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+
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+ # controlnet-AmritaBha/sd15_fill_mscoco
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+
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+ These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
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+ You can find some example images below.
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+
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+ prompt: Create an image where the objects of each category have the corresponding colors:\nCategory: bowl, Colors: midnightblue (2,24,109), black (21,25,7), goldenrod (226,164,6), gold (246,225,13), sienna (161,90,20)\nCategory: broccoli, Colors: midnightblue (2,24,109), black (20,25,7), darkgreen (39,86,23), olivedrab (87,159,37), mediumseagreen (97,158,93)\nCategory: bowl, Colors: midnightblue (2,24,109), firebrick (172,17,30), mediumvioletred (202,49,114), khaki (248,240,161), gold (249,211,19)\nCategory: bowl, Colors: midnightblue (2,24,109), palevioletred (228,89,152), indianred (225,81,113), hotpink (242,115,200), maroon (110,30,29)\nCategory: orange, Colors: midnightblue (2,24,109), gold (252,213,8), orange (248,173,13), yellow (252,239,95), khaki (252,245,153)\n
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+ ![images_0)](./images_0.png)
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+ prompt: Create an image where the objects of each category have the corresponding colors:\nCategory: giraffe, Colors: black (8,9,4), sandybrown (224,147,97), beige (250,247,226), navajowhite (246,212,168), sienna (158,92,46)\nCategory: giraffe, Colors: black (8,9,4), cornsilk (250,243,220), sienna (160,95,46), sienna (163,109,80), black (35,23,9)\n
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+ ![images_1)](./images_1.png)
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+ prompt: Create an image where the objects of each category have the corresponding colors:\nCategory: potted plant, Colors: black (27,32,28), darkgray (173,177,167), lightgray (209,209,205), darkkhaki (152,152,104), silver (197,196,184)\nCategory: vase, Colors: black (27,32,28), darkgray (177,180,177), silver (202,201,197), silver (194,194,188), silver (188,194,184)\n
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+ ![images_2)](./images_2.png)
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+
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+
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+
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+ ## Intended uses & limitations
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+
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+ #### How to use
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+
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+ ```python
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+ # TODO: add an example code snippet for running this diffusion pipeline
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+ ```
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+
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+ #### Limitations and bias
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+
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+ [TODO: provide examples of latent issues and potential remediations]
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+
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+ ## Training details
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+
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+ [TODO: describe the data used to train the model]
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