metadata

license: mit
language:
  - en
library_name: diffusers
tags:
  - lora
  - text-to-image
base_model: stabilityai/stable-diffusion-xl-base-1.0

Mann-E Turbo

This is a part of Mann-E Community Edition models. This model is a basic implementation of Mann-E's original model (which is not Stable Diffusion anymore) as a LoRa for SDXL. Since the whole business of Mann-E started around SD ecosystem, we've decided to release our LoRa for SD users and people who enjoy using models locally!

Sample Generations

Considerations

Model IS capable of making accidental and intentional NSFW material. So be careful while using the LoRa in presence of people who might be sensitive to this material.
The model has tested with SDXL 1.0, SDXL Turbo and even SDXL Lightning models and it works perfectly.
- We've tested the model with A111 and diffusers. For ComfyUI, we may need contributions if it doesn't work.
- DreamShaperXL, TurboVisioXL and RealVis were models with the best results. You may consider using them or merges/fine-tunes based on those!
This LoRa is compatible with other LoRa's as well. Be careful to weight it exactly 1 to get results like what we're presenting here.
DPM SDE++ Karras is the best scheduler. 6-10 steps for turbo models work perfectly, also CFG of 2.5 to 4.0 works perfectly!

Load with 🧨 diffusers

from diffusers import DiffusionPipeline, DPMSolverSinglestepScheduler
import torch

pipe = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
#You can use other base models such as `Lykon/dreamshaper-xl-1-0`, `0x4f1f/TurboVisionXL-v3.2` or `SG161222/RealVisXL_V4.0`

pipe.load_lora_weights(
    "mann-e/Mann-E_Turbo",
    weight_name="manne_turbo.safetensors",
)

#This is equivalent to DPM++ SDE Karras, as noted in https://huggingface.co/docs/diffusers/main/en/api/schedulers/overview
pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True)

image = pipe(
  prompt="a cat in a bustling middle eastern city",
  num_inference_steps=8,
  guidance_scale=4,
  width=768,
  height=768,
  clip_skip=1
).images[0]

image.save("a_cat.png")