examples/community/latent_consistency_img2img.py

which can be called like this

import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
                "SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_txt2img", custom_revision="main")

            # To save GPU memory, torch.float16 can be used, but it may compromise image quality.
pipe.to(torch_device="cuda", torch_dtype=torch.float32)

img2img=LatentConsistencyModelPipeline_img2img(
    vae=pipe.vae,
    text_encoder=pipe.text_encoder,
    tokenizer=pipe.tokenizer,
    unet=pipe.unet,
    #scheduler=pipe.scheduler,
    scheduler=None,
    safety_checker=None,
    feature_extractor=pipe.feature_extractor,
    requires_safety_checker=False,
)

img = Image.open("thisismyimage.png")

result = img2img(prompt,img,strength,num_inference_steps=4)

Fix name formatting for scheduler

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>