diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..dad60b4a1f88f4de5bd65ea12f26fc9f6eeed088 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst 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 diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1bb21a9a96f523de6ca7cf1807d51ed0009dd559 --- /dev/null +++ b/README.md @@ -0,0 +1,47 @@ +--- +license: creativeml-openrail-m +library_name: diffusers +tags: +- stable-diffusion +- stable-diffusion-diffusers +- text-to-image +- diffusers +- controlnet +- diffusers-training +base_model: runwayml/stable-diffusion-v1-5 +inference: true +--- + + + + +# controlnet-AmritaBha/sd15_fill_mscoco + +These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. +You can find some example images below. + +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 +![images_0)](./images_0.png) +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 +![images_1)](./images_1.png) +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 +![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] \ No newline at end of file diff --git a/checkpoint-10000/controlnet/config.json b/checkpoint-10000/controlnet/config.json new file mode 100644 index 0000000000000000000000000000000000000000..fc41f97244e0037a86bfd5abc02a0184e3f89f86 --- /dev/null +++ b/checkpoint-10000/controlnet/config.json @@ -0,0 +1,52 @@ +{ + "_class_name": "ControlNetModel", + "_diffusers_version": "0.29.0.dev0", + "_name_or_path": "AmritaBha/sd15_fill50k", + "act_fn": "silu", + "addition_embed_type": null, + "addition_embed_type_num_heads": 64, + "addition_time_embed_dim": null, + "attention_head_dim": 8, + "block_out_channels": [ + 320, + 640, + 1280, + 1280 + ], + "class_embed_type": null, + "conditioning_channels": 3, + "conditioning_embedding_out_channels": [ + 16, + 32, + 96, + 256 + ], + "controlnet_conditioning_channel_order": "rgb", + "cross_attention_dim": 768, + "down_block_types": [ + 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