FLUX.1-merged
This repository provides the merged params for black-forest-labs/FLUX.1-dev
and black-forest-labs/FLUX.1-schnell. Please be aware of the licenses of both
the models before using the params commercially.
Sub-memory-efficient merging code
from diffusers import FluxTransformer2DModel
from huggingface_hub import snapshot_download
from accelerate import init_empty_weights
from diffusers.models.model_loading_utils import load_model_dict_into_meta
import safetensors.torch
import glob
import torch
with init_empty_weights():
config = FluxTransformer2DModel.load_config("black-forest-labs/FLUX.1-dev", subfolder="transformer")
model = FluxTransformer2DModel.from_config(config)
dev_ckpt = snapshot_download(repo_id="black-forest-labs/FLUX.1-dev", allow_patterns="transformer/*")
schnell_ckpt = snapshot_download(repo_id="black-forest-labs/FLUX.1-schnell", allow_patterns="transformer/*")
dev_shards = sorted(glob.glob(f"{dev_ckpt}/transformer/*.safetensors"))
schnell_shards = sorted(glob.glob(f"{schnell_ckpt}/transformer/*.safetensors"))
merged_state_dict = {}
guidance_state_dict = {}
for i in range(len((dev_shards))):
state_dict_dev_temp = safetensors.torch.load_file(dev_shards[i])
state_dict_schnell_temp = safetensors.torch.load_file(schnell_shards[i])
keys = list(state_dict_dev_temp.keys())
for k in keys:
if "guidance" not in k:
merged_state_dict[k] = (state_dict_dev_temp.pop(k) + state_dict_schnell_temp.pop(k)) / 2
else:
guidance_state_dict[k] = state_dict_dev_temp.pop(k)
if len(state_dict_dev_temp) > 0:
raise ValueError(f"There should not be any residue but got: {list(state_dict_dev_temp.keys())}.")
if len(state_dict_schnell_temp) > 0:
raise ValueError(f"There should not be any residue but got: {list(state_dict_dev_temp.keys())}.")
merged_state_dict.update(guidance_state_dict)
load_model_dict_into_meta(model, merged_state_dict)
model.to(torch.bfloat16).save_pretrained("merged-flux")
Inference code
from diffusers import FluxPipeline
import torch
pipeline = FluxPipeline.from_pretrained(
"sayakpaul/FLUX.1-merged", torch_dtype=torch.bfloat16
).to("cuda")
image = pipeline(
prompt="a tiny astronaut hatching from an egg on the moon",
guidance_scale=3.5,
num_inference_steps=4,
height=880,
width=1184,
max_sequence_length=512,
generator=torch.manual_seed(0),
).images[0]
image.save("merged_flux.png")