README.md

---
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](https://mann-e.com)'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

<div style="text-align:center;">
  
<img src="./hf-1.png" width=512 height=512>

<img src="./hf-2.png" width=512 height=512>

<img src="./hf-3.png" width=512 height=512>

<img src="./hf-4.png" width=512 height=512>

</div>

## 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
```py
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")
```