adams-story/lcm-lora-hpsv2-rl

This is a LORA for stable diffusion 1.5, that improves the generation quality at 4 steps. It uses direct backpropogation and HPSV2 reward scoring.

It can generate decent quality images at only 4 inference steps.

Load with LCM https://huggingface.co/latent-consistency/lcm-lora-sdv1-5

Like this:

import torch
from diffusers import LCMScheduler, AutoPipelineForText2Image

model_id = "Lykon/dreamshaper-7"
adapter_id = "latent-consistency/lcm-lora-sdv1-5"

pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")

# load and fuse lcm lora
pipe.load_lora_weights(adapter_id)
pipe.fuse_lora()

# load and fuse my lcm-lora-hpsv2 lora
pipe.load_lora_weights("adams-story/lcm-lora-hpsv2-rl")
pipe.fuse_lora()

prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"

# disable guidance_scale by passing 0
image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0).images[0]