FLUX.1 [dev] Canny with LoRAs
FLUX.1 [dev] Canny: guided image creation with edge control.
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FLUX.1 [dev] Canny with LoRAs builds on the core FLUX.1 [dev] model by adding Canny edge conditioning and support for multiple LoRA adapters. Canny edges preserve the outline structure of your reference image, allowing the model to fill in details, colors, and textures while maintaining the original composition. LoRAs let you further steer the output toward specific artistic styles, characters, or subjects without retraining.
Use this model when you need to generate variations of an existing design, apply a consistent look across multiple images, or blend real-world references with imaginative elements. It excels at tasks like product mockups, character concept art, and style-consistent asset creation.
What sets it apart is the combination of structural guidance from Canny edges and the stylistic flexibility of LoRAs. You can keep the pose of a person while changing their outfit, or maintain the layout of a scene while transforming the art style. This makes it ideal for iterative design workflows where consistency matters.
- Generating product mockups from edge sketches
- Creating consistent character art across different styles
- Reposing subjects while preserving original composition
- Applying specific artistic styles with LoRA models
- Producing design variations from a base template
Canny edges lock in the main outlines so generated images stay true to the source composition.
Combine multiple LoRA adapters to fine-tune aesthetics like anime, oil painting, or sketch styles.
Reuse the same edge map and LoRAs to quickly batch-generate coherent design alternatives.
- Start with a clear, high-contrast edge map for best structural control.
- Use lower guidance scale values to give the model more creative freedom.
- Combine multiple LoRAs by adjusting their weights to blend styles smoothly.
- Increase steps to 30-40 for sharper edge alignment in final output.
- Complex or overlapping edges can produce noisy results if the Canny thresholds are not tuned.
- Very fine details in the original image may be lost or reinterpreted by the model.
- Not ideal for photo-realistic reproduction of existing images without additional refinement.
A futuristic city street, cyberpunk aesthetic, neon signs, rain-slicked pavement, cinematic lighting, ultra-detailed
A female knight with a lion emblem on her shield, fantasy illustration, rich colors, dynamic pose, heroic
A glass-walled modern house in a forest at dawn, architecture render, clean lines, soft sunlight, photorealistic
A number that sets the random starting point; use the same seed to reproduce an image.
Number of denoising iterations; more steps improve detail but take longer.
The width-to-height ratio of the output image (e.g., 16:9 for widescreen).
Strength multiplier for each loaded LoRA adapter; adjust to mix styles.
Controls how closely the image follows your prompt; higher values stick more to the text, lower values allow more creativity.
The lower bound for edge detection; lower values capture more edges.
The upper bound for edge detection; higher values keep only strong edges.
Os preços exibidos são o que você paga na Muitto. Sem surpresas.
Can I use this model for photo restoration?
It is better suited for creative variations than precise restoration because it reinterrather than copies details.
How do I get good edge maps?
Use the Canny edge filter with low and high thresholds around 100-200 for balanced outlines.
Can I use custom LoRAs with this model?
Yes, you can load any compatible LoRA adapter to modify style or subject.