ndinsky-2-2-decoder with your own trained decoder checkpoint!
</hfoption>
<hfoption id="decoder model">

	  Copied from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained("path/to/saved/model", torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()

prompt="A robot pokemon, 4k photo"
image = pipeline(prompt=prompt).images[0] For the decoder model, you can also perform inference 