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--- |
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tags: |
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- stable-diffusion-xl |
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- stable-diffusion-xl-diffusers |
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- text-to-image |
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- diffusers |
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- lora |
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- template:sd-lora |
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widget: |
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- text: A photo of <s0><s1> a man wearing headphones and a blue shirt |
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output: |
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url: image-0.png |
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- text: A photo of <s0><s1> a bald man wearing glasses and a white t - shirt |
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output: |
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url: image-1.png |
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- text: A photo of <s0><s1> a man with glasses and a beard smiles |
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output: |
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url: image-2.png |
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- text: A photo of <s0><s1> a bald man with glasses and a colorful shirt |
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output: |
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url: image-3.png |
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- text: A photo of <s0><s1> a man with glasses and a hat wearing an orange cap |
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output: |
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url: image-4.png |
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- text: A photo of <s0><s1> a man wearing glasses and a yellow hat taking a selfie |
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output: |
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url: image-5.png |
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base_model: stabilityai/stable-diffusion-xl-base-1.0 |
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instance_prompt: A photo of <s0><s1> |
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license: openrail++ |
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--- |
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# SDXL LoRA DreamBooth - multimodalart/poli-multiplier-75-face |
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<Gallery /> |
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## Model description |
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### These are multimodalart/poli-multiplier-75-face LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. |
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## Trigger words |
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To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: |
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to trigger concept `TOK` → use `<s0><s1>` in your prompt |
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## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) |
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```py |
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from diffusers import AutoPipelineForText2Image |
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import torch |
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from huggingface_hub import hf_hub_download |
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from safetensors.torch import load_file |
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pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') |
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pipeline.load_lora_weights('multimodalart/poli-multiplier-75-face', weight_name='pytorch_lora_weights.safetensors') |
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embedding_path = hf_hub_download(repo_id='multimodalart/poli-multiplier-75-face', filename="embeddings.safetensors", repo_type="model") |
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state_dict = load_file(embedding_path) |
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pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) |
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pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) |
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image = pipeline('A photo of <s0><s1>').images[0] |
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``` |
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For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) |
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## Download model |
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### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke |
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- Download the LoRA *.safetensors [here](/multimodalart/poli-multiplier-75-face/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder. |
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- Download the text embeddings *.safetensors [here](/multimodalart/poli-multiplier-75-face/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder. |
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All [Files & versions](/multimodalart/poli-multiplier-75-face/tree/main). |
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## Details |
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The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). |
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LoRA for the text encoder was enabled. False. |
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Pivotal tuning was enabled: True. |
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Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. |
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