sd15_mscoco_2

<!-- 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-hazal-karakus/sd15_mscoco_2

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 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 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

Intended uses & limitations

How to use

# 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]