
This is a copy of the SSD-1B model (https://huggingface.co/segmind/SSD-1B) with the unet replaced
with the LCM distilled unet (https://huggingface.co/latent-consistency/lcm-ssd-1b) and scheduler config set to default to the LCM Scheduler.

This makes LCM SSD-1B run as a standard Diffusion Pipeline

```py
from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained(
    "Vargol/lcm-ssd-1b-full-model", variant='fp16', torch_dtype=torch.float16
).to("mps")

prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
generator = torch.manual_seed(0)

image = pipe(
    prompt=prompt, num_inference_steps=4, generator=generator, guidance_scale=8.0
).images[0]

image.save('distilled.png')

```

{"license": "openrail++", "tags": ["text-to-image", "stable-diffusion"]}
diffusers safetensors text-to-image stable-diffusion license:openrail++ endpoints_compatible diffusers:StableDiffusionXLPipeline region:us