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icespice_lora_flux_nf4 takes inspiration from this post (https://huggingface.co/blog/flux-qlora). The training was executed on a local computer with 1000 timesteps and the same parameters as the link mentioned above, which took around 6 hours on 8GB VRAM 4060. The peak VRAM usage was around 7.7GB. To avoid running low on VRAM, both transformers and text_encoder were quantized. All the images generated here are using the below parameters1import torch
2from diffusers import FluxPipeline, FluxTransformer2DModel
3from transformers import T5EncoderModel
4
5text_encoder_4bit = T5EncoderModel.from_pretrained(
6 "hf-internal-testing/flux.1-dev-nf4-pkg", subfolder="text_encoder_2",torch_dtype=torch.float16,)
7
8transformer_4bit = FluxTransformer2DModel.from_pretrained(
9 "hf-internal-testing/flux.1-dev-nf4-pkg", subfolder="transformer",torch_dtype=torch.float16,)
10
11pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.float16,
12 transformer=transformer_4bit,text_encoder_2=text_encoder_4bit)
13
14pipe.load_lora_weights("je-suis-tm/icespice_lora_flux_nf4",
15 weight_name='pytorch_lora_weights.safetensors')
16
17prompt="Surreal 4k, a beautiful elf princess called IceSpice with expressive black eyes and cosmic features. Her skin appears to be composed of intertwined bioluminescent particles, surrounded by neon lights and floating, colorful orbs in a fantastical forest environment, with exotic animals creating a mesmerizing and otherworldly atmosphere, cinematic composition. full body portrait"
18
19image = pipe(
20 prompt,
21 height=512,
22 width=512,
23 guidance_scale=5,
24 num_inference_steps=20,
25 max_sequence_length=512,
26 generator=torch.Generator("cpu").manual_seed(0),
27 ).images[0]
28
29image.save("icespice_lora_flux_nf4.png")IceSpice to trigger the image generation.