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
![](/mann-e/Mann-E_Turbo/resolve/main/hf-1.png)
![](/mann-e/Mann-E_Turbo/resolve/main/hf-2.png)
![](/mann-e/Mann-E_Turbo/resolve/main/hf-3.png)
![](/mann-e/Mann-E_Turbo/resolve/main/hf-4.png)
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!
- We've tested the model with A111 and
- 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")
- Downloads last month
- 70
Model tree for mann-e/Mann-E_Turbo
Base model
stabilityai/stable-diffusion-xl-base-1.0
Adapter
this model