mann-e/Mann-E_Turbo

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")