import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("diesermo/pose-control-lora")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]control-lora-diesermo/pose-control-lora
These are Control LoRA weights trained on black-forest-labs/FLUX.1-dev with new type of conditioning. You can find some example images below.
prompt: Magenta Porsche 911 GT3 RS, low wide. Black roof/hood. Large rear wing, central exhaust. Gold multi-spoke wheels. Matte finish, glossy accents. Aggressive aero, short wheelbase. Studio lit, sharp reflections. Detailed vents, flush door handles. 992 proportions.
License
Please adhere to the licensing terms as described here
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]
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Model tree for diesermo/pose-control-lora
Base model
black-forest-labs/FLUX.1-dev