Instructions to use AlekseyCalvin/ColorizeTruer_KontextFlux_V6resized_BySAP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AlekseyCalvin/ColorizeTruer_KontextFlux_V6resized_BySAP with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("AlekseyCalvin/ColorizeTruer_KontextFlux_V6resized_BySAP") prompt = "Colorize this photo (optional)" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Update README.md
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README.md
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---
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tags:
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- image-to-image
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- lora
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- diffusers
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- template:sd-lora
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- ai-toolkit
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- colorization
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base_model: black-forest-labs/FLUX.1-Kontext-dev
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instance_prompt: >-
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colorize
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license: unlicense
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widget:
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- text: Colorize this photo (optional)
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output:
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url: outputs/01.png
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- src: inputs/01.jpg
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prompt: Colorize this photo (optional)
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output:
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url: outputs/01.png
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- text: Colorize this photo (optional)
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output:
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url: outputs/02.png
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- src: inputs/02.jpg
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prompt: Colorize this photo (optional)
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output:
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url: outputs/02.png
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- text: Colorize this photo (optional)
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output:
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url: outputs/03.png
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- src: inputs/03.jpg
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prompt: Colorize this photo (optional)
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output:
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url: outputs/03.png
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---
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# COLORIZE TRUER (Variant 6)
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## Low(ish) Rank Adapter (LoRA) for Flux Kontext
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## Towards Consistent + Promptless Kontext Photo Colorization
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## |||| By SilverAgePoets.com ||||
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<Gallery />
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This version is from the same training as [Colorize Truer Var6](https://huggingface.co/AlekseyCalvin/ColorizeTruer_KontextFluxVar6_BySAP), but resized down from Rank 64 to Rank 16, for portability + easier merging with other variants. <br>
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Our aim with this checkpoint, & other Colorize Truer/+ LoRAs, is to help restore Kontext Dev towards the far more impressive photo colorization capabilities of Kontext Pro/Max, whilst further reinforcing task-specific reliability & realism at the standards of professional manual colorization. <br>
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This adapter was trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) over roughly 600 grayscale/colorized pairings of real archival photos. <br>
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The colorized (output) portion of the custom dataset we consolidated to train these "Colorize Truer" LoRAs consists of: <br>
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Open use manual/professional colorizations of historical photos (roughly half of the data). <br>
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Colorized outputs from Flux Kontext Max & Pro (the rest of the data). <br>
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The source photos were sourced from public domain, and represent a broad scope of geographical, cultural, aesthetic, historical (from late 1830s thru today), & other paradigms/contexts. <br>
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## Trigger words
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No trigger words neccessary. <br>
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With that said, with some inputs, it may be neccessary to specify `colorize` or `colorize and upscale this photo. Leave all else the same.` as the instruction prompt. <br>
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One may also prompt in suggestions/corrections/directions, as far as specific choices of color. <br>
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To "modernize" outputs, one may try: `Colorize and upscale the photo. Restore crisp detailing. Rich tonality. Make indistinguishable from top quality UHD 50MP+ or 12k+ pro 2025 DSLR camera colorized_02.CR2 photo. restore blurred details. Retain facial features with great fidelity source. Add fine detailing only where missing. Render real life like tones. Leave unchanged all else about photo.`, etc... <br>
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## Further usage tips:
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For many inputs, a somewhat lower LoRA scale measure (0.7 or 0.8) seems to work best. Other inputs, however, seem to benefit from scales at around 1.2/1.3. <br>
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Make sure to match-up input/output dimensions, primarily to ensure fidelity of details/identity. <br>
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With starkly differing dimensions, the model might also get confused in regards to its instruction. <br>
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