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---
license:
- apache-2.0
- other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
library_name: diffusers
pipeline_tag: text-to-image
datasets:
- SA1B
- opendiffusionai/laion2b-squareish-1024px
base_model:
- jimmycarter/LibreFLUX
---
# LibreFLUX-IP-Adapter-ControlNet
This model/pipeline combines my [LibreFlux-IP-Adapter](https://huggingface.co/neuralvfx/LibreFlux-IP-Adapter) and [LibreFlux ControlNet](https://huggingface.co/neuralvfx/LibreFlux-ControlNet), into a single pipeline. [LibreFLUX](https://huggingface.co/jimmycarter/LibreFLUX) is used as the underlying Transformer model.
# How does this relate to LibreFLUX?
- Base model is [LibreFLUX](https://huggingface.co/jimmycarter/LibreFLUX)
- Trained in same non-distilled fashion
- Uses Attention Masking
- Uses CFG during Inference
# Compatibility
```py
pip install -U diffusers==0.35.2
pip install -U transformers==4.57.1
```
Low VRAM:
```py
pip install optimum.quanto
```
# Load Pipeline
```py
import torch
from diffusers import DiffusionPipeline
from huggingface_hub import hf_hub_download
model_id = "neuralvfx/LibreFlux-IP-Adapter-ControlNet"
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16 if device == "cuda" else torch.float32
pipe = DiffusionPipeline.from_pretrained(
model_id,
custom_pipeline=model_id,
trust_remote_code=True,
torch_dtype=dtype,
safety_checker=None
)
# Optional way to download the weights
hf_hub_download(repo_id="neuralvfx/LibreFlux-IP-Adapter-ControlNet",
filename="ip_adapter.pt",
local_dir=".",
local_dir_use_symlinks=False)
pipe.load_ip_adapter('ip_adapter.pt')
pipe.to(device)
```
# Inference
```py
from PIL import Image
from torchvision.transforms import ToTensor
# Optional way to download test Control Net Image
hf_hub_download(repo_id="neuralvfx/LibreFlux-IP-Adapter-ControlNet",
filename="examples/libre_flux_control_image.png",
local_dir=".",
local_dir_use_symlinks=False)
# Load Control Image
cond = Image.open("examples/libre_flux_control_image.png").convert("RGB")
cond = cond.resize((1024, 1024))
# Optional way to download test IP Adapter Image
hf_hub_download(repo_id="neuralvfx/LibreFlux-IP-Adapter-ControlNet",
filename="examples/merc.jpeg",
local_dir=".",
local_dir_use_symlinks=False)
# Load IP Adapter Image
ip_image = Image.open("examples/merc.jpeg").convert("RGB")
ip_image = ip_image.resize((512, 512))
out = pipe(
prompt="the words libre flux",
negative_prompt="blurry",
control_image=cond, # Use the tensor here
num_inference_steps=75,
guidance_scale=4.0,
controlnet_conditioning_scale=1.0,
ip_adapter_image=ip_image,
ip_adapter_scale=1.0,
num_images_per_prompt=1,
generator= torch.Generator().manual_seed(74),
return_dict=True,
)
out.images[0]
```
# Load Pipeline ( Low VRAM )
```py
import torch
from huggingface_hub import hf_hub_download
from diffusers import DiffusionPipeline
from optimum.quanto import freeze, quantize, qint8
model_id = "neuralvfx/LibreFlux-IP-Adapter-ControlNet"
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16 if device == "cuda" else torch.float32
pipe = DiffusionPipeline.from_pretrained(
model_id,
custom_pipeline=model_id,
trust_remote_code=True,
torch_dtype=dtype,
safety_checker=None
)
# Optional way to download the weights
hf_hub_download(repo_id="neuralvfx/LibreFlux-IP-Adapter-ControlNet",
filename="ip_adapter.pt",
local_dir=".",
local_dir_use_symlinks=False)
# Load the IP Adapter First
pipe.load_ip_adapter('ip_adapter.pt')
# Quantize and Freeze
quantize(
pipe.transformer,
weights=qint8,
exclude=[
"*.norm", "*.norm1", "*.norm2", "*.norm2_context",
"proj_out", "x_embedder", "norm_out", "context_embedder",
],
)
quantize(
pipe.ip_adapter,
weights=qint8,
exclude=[
"*.norm", "*.norm1", "*.norm2", "*.norm2_context",
"proj_out", "x_embedder", "norm_out", "context_embedder",
],
)
quantize(
pipe.controlnet,
weights=qint8,
exclude=[
"*.norm", "*.norm1", "*.norm2", "*.norm2_context",
"proj_out", "x_embedder", "norm_out", "context_embedder",
],
)
freeze(pipe.transformer)
freeze(pipe.ip_adapter)
freeze(pipe.controlnet)
# Enable Model Offloading
pipe.enable_model_cpu_offload()
```
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