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mann-e/
mann-e_flux

Name: mann-e_flux
Rating: 2.5 (6 reviews)

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Mann-E FLUX[Dev] Edition

How to use the model

Install needed libraries

pip install git+https://github.com/huggingface/diffusers.git transformers==4.42.4 accelerate xformers peft sentencepiece protobuf -q

Execution code

python

import numpy as np
import random
import torch
from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
pipe = DiffusionPipeline.from_pretrained("mann-e/mann-e_flux", torch_dtype=dtype, vae=taef1).to(device)
torch.cuda.empty_cache()

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

prompt = "an astronaut riding a horse"

pipe(
            prompt=f"{prompt}",
            guidance_scale=3.5,
            num_inference_steps=10,
            width=720,
            height=1280,
            generator=generator,
            output_type="pil"
        ).images[0].save("output.png")

Tips and Tricks

Adding mj-v6.1-style to the prompts specially the cinematic and photo realistic prompts can make the result quality high as hell! Give it a try.
The best guidance_scale is somewhere between 3.5 and 5.0
Inference steps between 8 and 16 are working very well.