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metadata
base_model: KHongJae/full_train_TE_D_0_10000to50000
library_name: diffusers
license: creativeml-openrail-m
tags:
  - text-to-image
  - dreambooth
  - diffusers-training
  - stable-diffusion
  - stable-diffusion-diffusers
inference: true

KHongJae/Chatting_Based_Emoji_Generation_Model

T-Academy ASAC 6κΈ° DL ν”„λ‘œμ νŠΈμ—μ„œ μ‚¬μš©ν•œ 이λͺ¨ν‹°μ½˜ 생성 λͺ¨λΈμž…λ‹ˆλ‹€.

κΈ°μ‘΄ λ¬˜μ‚¬ν˜• ν”„λ‘¬ν”„νŠΈμ—μ„œλ§Œ μž‘λ™ ν•˜λ˜ Stable Diffusion을 λŒ€ν™”ν˜• ν”„λ‘¬ν”„νŠΈμ—μ„œ μž‘λ™ν•  수 μžˆλ„λ‘ μ‹œλ„ν•œ λͺ¨λΈμž…λ‹ˆλ‹€.

DreamBooth for the text encoder was enabled: True.

Intended uses & limitations

img_0 img_1 img_2

How to use

pipeline = DiffusionPipeline.from_pretrained(
    "KHongJae/Chatting_Based_Emoji_Generation_Model",
    torch_dtype=torch.float16
).to("cuda")
pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
prompt = "Create your own prompt"
negative_prompt = "Create your own negative prompt"

pipeline(
  prompt=prompt,
  negative_prompt=negative_prompt,
  width=200,
  height=200,
  num_inference_steps=50,
  num_images_per_prompt=1,
  generator=torch.manual_seed(123456789),
).images[0]

Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

Training details

[TODO: describe the data used to train the model]