--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-xl-base-1.0 dataset: NYUAD-ComNets/Ethnicity_Diversity_Data tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # Model description LoRA text2image fine-tuning - NYUAD-ComNets/Ethnicity_Diversity_Model These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the NYUAD-ComNets/Ethnicity_Diversity_Data dataset. You can find some example images. prompt: a photo of a {ethnicity} person, looking at the camera, closeup headshot facing forward, ultra quality, sharp focus # How to use this model: ``` python from diffusers import DiffusionPipeline import torch from compel import Compel, ReturnedEmbeddingsType negative_prompt = "cartoon, anime, 3d, painting, b&w, low quality" pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", variant="fp16", use_safetensors=True, torch_dtype=torch.float16).to("cuda") pipeline.load_lora_weights("NYUAD-ComNets/Ethnicity_Diversity_Model", weight_name="pytorch_lora_weights.safetensors") compel = Compel(tokenizer=[pipeline.tokenizer, pipeline.tokenizer_2] , text_encoder=[pipeline.text_encoder, pipeline.text_encoder_2], returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, requires_pooled=[False, True],truncate_long_prompts=False) conditioning, pooled = compel("a photo of an asian person, looking at the camera, closeup headshot facing forward, ultra quality, sharp focus") negative_conditioning, negative_pooled = compel(negative_prompt) [conditioning, negative_conditioning] = compel.pad_conditioning_tensors_to_same_length([conditioning, negative_conditioning]) image = pipeline(prompt_embeds=conditioning, negative_prompt_embeds=negative_conditioning, pooled_prompt_embeds=pooled, negative_pooled_prompt_embeds=negative_pooled, num_inference_steps=40).images[0] image.save('/../../x.jpg') ``` # Examples | | | | |:-------------------------:|:-------------------------:|:-------------------------:| |screen shot 2017-08-07 at 12 18 15 pm | screen shot 2017-08-07 at 12 18 15 pm|screen shot 2017-08-07 at 12 18 15 pm| |screen shot 2017-08-07 at 12 18 15 pm | screen shot 2017-08-07 at 12 18 15 pm|screen shot 2017-08-07 at 12 18 15 pm| |screen shot 2017-08-07 at 12 18 15 pm | screen shot 2017-08-07 at 12 18 15 pm|screen shot 2017-08-07 at 12 18 15 pm| |screen shot 2017-08-07 at 12 18 15 pm | screen shot 2017-08-07 at 12 18 15 pm|screen shot 2017-08-07 at 12 18 15 pm| |screen shot 2017-08-07 at 12 18 15 pm | screen shot 2017-08-07 at 12 18 15 pm|screen shot 2017-08-07 at 12 18 15 pm| |screen shot 2017-08-07 at 12 18 15 pm | screen shot 2017-08-07 at 12 18 15 pm|screen shot 2017-08-07 at 12 18 15 pm| # Training data NYUAD-ComNets/Ethnicity_Diversity_Data dataset was used to fine-tune stabilityai/stable-diffusion-xl-base-1.0 # Configurations LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. # BibTeX entry and citation info ``` @misc{ComNets, url={[https://huggingface.co/NYUAD-ComNets/Ethnicity_Diversity_Model](https://huggingface.co/NYUAD-ComNets/Ethnicity_Diversity_Model)}, title={Ethnicity_Diversity_Model}, author={Nouar AlDahoul, Talal Rahwan, Yasir Zaki} } ```