Instructions to use codermert/malikafinal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use codermert/malikafinal with Diffusers:
pip install -U diffusers transformers accelerate
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("codermert/malikafinal") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
metadata
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- stable-diffusion
- text-to-image
base_model: black-forest-labs/FLUX.1-dev
pipeline_tag: text-to-image
inference: true
Malika
Usage with 🧨 Diffusers
from diffusers import DiffusionPipeline
import torch
# Load base model
pipeline = DiffusionPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
torch_dtype=torch.float16
).to("cuda")
# Load your LoRA
pipeline.load_lora_weights(
"codermert/malikafinal",
weight_name="lora.safetensors",
adapter_name="malika"
)
# Generate image
image = pipeline(
prompt="portrait of TOK, <malika>, photorealistic, 8K",
negative_prompt="blurry, deformed"
).images[0]