Instructions to use areshi/sdxl-mk1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use areshi/sdxl-mk1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("areshi/sdxl-mk1") prompt = "<s0><s1>" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| license: creativeml-openrail-m | |
| tags: | |
| - text-to-image | |
| - stable-diffusion | |
| - lora | |
| - diffusers | |
| base_model: stabilityai/stable-diffusion-xl-base-1.0 | |
| instance_prompt: <s0><s1> | |
| inference: false | |
| # sdxl-mk1 LoRA by [asronline](https://replicate.com/asronline) | |
| ### Generate Mortal Kombat 1 fighters and character skins | |
|  | |
| > | |
| ## Inference with Replicate API | |
| Grab your replicate token [here](https://replicate.com/account) | |
| ```bash | |
| pip install replicate | |
| export REPLICATE_API_TOKEN=r8_************************************* | |
| ``` | |
| ```py | |
| import replicate | |
| output = replicate.run( | |
| "sdxl-mk1@sha256:7ad4307597a51cbe61a568e835f8f475d33b97b0987117c5a89cbf0e699eb69c", | |
| input={"prompt": "In the style of MK1, scorpion in the style of a dark and mysterious ninja costume, black and misty details, fiery eyes and hands, skull as his head"} | |
| ) | |
| print(output) | |
| ``` | |
| You may also do inference via the API with Node.js or curl, and locally with COG and Docker, [check out the Replicate API page for this model](https://replicate.com/asronline/sdxl-mk1/api) | |
| ## Inference with 🧨 diffusers | |
| Replicate SDXL LoRAs are trained with Pivotal Tuning, which combines training a concept via Dreambooth LoRA with training a new token with Textual Inversion. | |
| As `diffusers` doesn't yet support textual inversion for SDXL, we will use cog-sdxl `TokenEmbeddingsHandler` class. | |
| The trigger tokens for your prompt will be `<s0><s1>` | |
| ```shell | |
| pip install diffusers transformers accelerate safetensors huggingface_hub | |
| git clone https://github.com/replicate/cog-sdxl cog_sdxl | |
| ``` | |
| ```py | |
| import torch | |
| from huggingface_hub import hf_hub_download | |
| from diffusers import DiffusionPipeline | |
| from cog_sdxl.dataset_and_utils import TokenEmbeddingsHandler | |
| from diffusers.models import AutoencoderKL | |
| pipe = DiffusionPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-xl-base-1.0", | |
| torch_dtype=torch.float16, | |
| variant="fp16", | |
| ).to("cuda") | |
| load_lora_weights("areshi/sdxl-mk1", weight_name="lora.safetensors") | |
| text_encoders = [pipe.text_encoder, pipe.text_encoder_2] | |
| tokenizers = [pipe.tokenizer, pipe.tokenizer_2] | |
| embedding_path = hf_hub_download(repo_id="areshi/sdxl-mk1", filename="embeddings.pti", repo_type="model") | |
| embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers) | |
| embhandler.load_embeddings(embedding_path) | |
| prompt="In the style of MK1, scorpion in the style of a dark and mysterious ninja costume, black and misty details, fiery eyes and hands, skull as his head" | |
| images = pipe( | |
| prompt, | |
| cross_attention_kwargs={"scale": 0.8}, | |
| ).images | |
| #your output image | |
| images[0] | |
| ``` | |