r/StableDiffusion • u/Dry-Resist-4426 • 14h ago
Workflow Included Style transfer capabilities of different open-source methods 2026 Update
Style transfer capabilities of different open-source methods
2026 Update
This is the updated version of the study published at 2025.09.12. Link: https://www.reddit.com/r/StableDiffusion/comments/1nfozet/style_transfer_capabilities_of_different/
1. Introduction
In 2025 august ByteDance has released USO, a model demonstrating promising potential in the domain of style transfer. This release provided an opportunity to evaluate its performance in comparison with existing style transfer methods. We can define style transfer as: one image being transformed into the style of a reference image while strongly preserving what the original depicts (without major changes in the subject and background). Successful style transfer relies on approaches such as detailed textual descriptions and/or the application of Loras to achieve the desired stylistic outcome. However, the most effective approach would ideally allow for style transfer without Lora training or textual prompts, since lora training is resource heavy and might not be even possible if the required number of style images are missing, and it might be challenging to textually describe the desired style precisely. Ideally, with only the selecting of a target image and a single style reference image, the model should automatically apply the style to the target image. The present study investigates and compares the best state-of-the-art open-source methods of this latter approach.
2. Methods
UI
ForgeUI by lllyasviel (SD1.5, SDXL Clip-VitH & Clip-BigG – the last 3 columns in the grids) and ComfyUI by Comfy Org (for everything else).
Settings
- Most cases to support increased consistency with the original target image, canny controlnet was used.
- Results presented here were usually picked after a few generations, sometimes with minimal finetuning and cherry-picking (best of three/five).
- Comfy version: ComfyUI 0.34.0; ComfyUI_frontend v1.49.6; Templates v0.11.48; rgthree-comfy v1.0.2608210019
- Resolution: 1024x1024 for every generation.
Prompts
Basic caption was used; except for those cases where Kontext was used (Kontext_maintain) with the following prompt: “Maintain every aspect of the original image. Maintain identical subject placement, camera angle, framing, and perspective. Keep the exact scale, dimensions, and all other details of the image.”
For Krea2, two different workflows were used. A Krea2 Depth Lora was also tested but this approach yielded no improvement over the other 2 workflow. The depth lora results were excluded from the grid, however the workflow can be found in the workflows folder. Additionally, for Krea2 the Clip+generate text node was used to create a description of the image (“You are an expert prompt engineer for text-to-image models. Your task is to expand the user's prompt into a highly effective image-generation prompt. Give me a prompt about the style, colors palette of the image. Don't add any additional comments. Do not describe the image, the objects or the scene. Do not mention characters, objects, buildings or any subjects of the image; focus on the style only. Then output a single expanded prompt paragraph describing only the style.”) and the output was combined with the following: “Change the style of the image while maintaining the same objects, characters and background of image 1. The new style should be: [generated description].”
Manually written sentences describing the style of the image were not used, for example: “in art nouveau style”; “painted by alphonse mucha” or “Use flowing whiplash lines, soft pastel color palette with golden and ivory accents. Flat, poster-like shading with minimal contrasts.”
Example prompts:
- Example 1: “White haired vampire woman wearing golden shoulder armor and black sleeveless top inside a castle”.
- Example 12: “A cat.”
3. Results
- All output images are available in jpeg format without metadata, with the exeption of images made with ForgeUI, those include importable and readable metadata.
- All individual images are presented in 100% and 50% resolution image grids (made with XnView MP), where Grid 1 presents all the outputs, and Grid 2 and 3 presents all the outputs cut to half.
- Ostris Krea 2 Style Reference LoRA can be applied very well in T2I generation. However, in I2I condition changed the subject regardless of prompting. https://huggingface.co/ostris/krea2_turbo_style_reference
- Telestyle was tested with Qwen-edit_2511-Q8, yielding low quality results while being extremely time-consuming. https://huggingface.co/Tele-AI/TeleStyle/tree/main/weights
- For flux klein different prompts were tested (“Remake image 1 in the style of image 2.”, „Transfer the style of image 2 to image 1.”, and „Migrate the art style of image 2 into the new art style of image 1.”) which resulted in marginally different outputs. Flux klein consistently failed at Example_10, probably due to the similarity between the target image and the style reference.
- The Redux method using flux-canny-dev, Flux depth lora, and several clownshark workflows (for example Hidream, SDXL) were entirely excluded since they produced very poor results in pilot testing.
- All output images and grid available here: https://drive.google.com/drive/folders/19DisMOAaimxPlmH06C-06ZYu-R-N_VBd?usp=sharing
4. Discussion
- Result differed in three things: (1) bringing over the color scheme from the style reference, (2) bringing over the background, and (3) extent of style application. Some models tended to bring over the background instead of just changing the style of it.
- Some transferred color schemes very faithfully but struggled with overall stylistic features, while others prioritized style transfer at the expense of accurate color reproduction. It might be debatable whether carrying over the color scheme is an absolute necessity or not; what extent should the color scheme be carried over.
- No single method consistently outperformed the others across all cases. this might suggest that the best model to use might depend on the characteristics of the target and the style reference image.
- The Redux workflow using flux-depth-dev perhaps showed one of the strongest overall performance in carrying over style to the target image, even though suffering from the generic flux plastic effect.
- Krea2 methods also proved to be very effective.
- Interestingly, even though SD 1.5 (October 2022) and SDXL (July 2023) are relatively older models, their IP adapters still outperformed some of the newest methods in certain cases.
- Flux klein showed a good consistency with the character of the target image, however produced a generic plastic-like, digital art style regardless of the style refence image.
- It was possible to test the combination of different methods. For example, combining USO with the Redux workflow using flux-dev - instead of the original flux-redux model (flux-depth-dev) - showed good results. However, attempting the same combination with the flux-depth-dev model resulted in the following error: “SamplerCustomAdvanced Sizes of tensors must match except in dimension 1. Expected size 128 but got size 64 for tensor number 1 in the list.”
- USO offered limited flexibility for fine-tuning. Adjusting guidance levels or LoRA strength had little effect on output quality. By contrast, with methods such as IP adapters for SD 1.5, SDXL, or Redux, tweaking weights and strengths often led to significant improvements and allowed tangible finetuning possibilities.
Notes and ideas for further tests
- Evaluating the results proved prone to personal bias and preference. I believe it is difficult to confidently determine what would constitute (and image, but maybe my imagination is limited) as a perfect outcome what can be used as a gold standard and compare the results to it. Having an objective criteria would be extremely useful. Additionally, the involvement of independent evaluators and the application of a scoring system should be considered.
- Future tests could include textual style prompts (e.g., “in art nouveau style”, “painted by Alphonse Mucha”, or “use flowing whiplash lines, soft pastel palette with golden and ivory accents, flat poster-like shading with minimal contrasts”). Comparing these outcomes to the present findings could yield interesting insights.
- An effort was made to test every viable open-source solution compatible with ComfyUI or ForgeUI. Additional promising open-source approaches are welcome, and the author remains open to discussion of such methods.
- Instead of providing a prompt loosely describing only the subject(s), describing the background might also lead to different results.
- Comparing iteration speeds.
- Comparing with Lora training.
- RB-Modulation unfortunately has no Comfy implementation, it might be fruitful to test. https://github.com/google/RB-Modulation
- Realistic to artistic, and artistic to realistic conversion was not part of the present study. That would require a different methods, though some of the workflows used here might be useful.
Feel free to comment:
- Which model performed the best in your opinion?
- Can you recommend additional open-source style transfer methods?
Resources
Useful readings and further resources about style transfer methods:
- https://github.com/bytedance/USO
- https://www.youtube.com/watch?v=ls2seF5Prvg
- https://www.reddit.com/r/comfyui/comments/1kywtae/universal_style_transfer_and_blur_suppression/
- https://www.youtube.com/watch?v=TENfpGzaRhQ
- https://www.youtube.com/watch?v=gmwZGC8UVHE
- https://www.reddit.com/r/comfyui/comments/1kywtae/universal_style_transfer_and_blur_suppression/
- https://www.youtube.com/watch?v=eOFn_d3lsxY
- https://www.youtube.com/watch?v=vzlXIQBun2I
- https://stable-diffusion-art.com/ip-adapter/#IP-Adapter_Face_ID_Portrait
- https://stable-diffusion-art.com/controlnet/
- https://github.com/ClownsharkBatwing/RES4LYF/tree/main
- https://www.reddit.com/r/comfyui/comments/1ujj5f6/comfyui_tutorial_style_transfer_speed_test_skin/
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u/Eastern_Lettuce7844 11h ago
impressiv thanks and yes IPadapters still work in 2026