dataautogpt3/synthetic-animeV1.1

SDXL LoRA - dataautogpt3/synthetic-animev1-1

Prompt

in the style of <s0><s1>

Prompt

in the style of <s0><s1>

Prompt

in the style of <s0><s1>, manga in the early 1990s,surreal,digitally rendered with glitches appearing throughout,depicted in matte colors,created using a digital medium. illustrated by Junji Ito,Yoshiyuki Sadamoto, and Rumiko Takahashi, a green cartoon frog, pepe,

Prompt

in the style of <s0><s1>

Prompt

in the style of <s0><s1>, manga in the early 1990s,surreal,digitally rendered with glitches appearing throughout,depicted in matte colors,created using a digital medium. illustrated by Junji Ito,Yoshiyuki Sadamoto, and Rumiko Takahashi,

Model description

These are dataautogpt3/synthetic-animev1-1 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.

Download model

Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke

• LoRA: download synthetic-animev1-1.safetensors here 💾.
  • Place it on your models/Lora folder.
  • On AUTOMATIC1111, load the LoRA by adding <lora:synthetic-animev1-1:1> to your prompt. On ComfyUI just load it as a regular LoRA.
• Embeddings: download synthetic-animev1-1_emb.safetensors here 💾.
  • Place it on it on your embeddings folder
  • Use it by adding synthetic-animev1-1_emb to your prompt. For example, in the style of synthetic-animev1-1_emb (you need both the LoRA and the embeddings as they were trained together for this LoRA)

Use it with the 🧨 diffusers library

from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
        
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('dataautogpt3/synthetic-animev1-1', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='dataautogpt3/synthetic-animev1-1', filename='synthetic-animev1-1_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
        
image = pipeline('in the style of <s0><s1>').images[0]

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers

Trigger words

To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:

to trigger concept TOK → use <s0><s1> in your prompt

Details

All Files & versions.

LoRA for the text encoder was enabled. False.

Pivotal tuning was enabled: True.

Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.