Van_Gogh_lora

63

2

1 language

—

by

Kontext-Style

Image Model

OTHER

New

63 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

Unknown

Mobile

Laptop

Server

Quick Summary

Van Gogh Style LoRA for FLUX.

Code Examples

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Inference Examplepythonpytorch

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Van Gogh style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Van_Gogh_lora", weight_name="Van_Gogh_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Van Gogh"
prompt = f"Turn this image into the Van_Gogh style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.