Since Kontext Dev is a guidance distilled model (works only at CFG 1), that means we can't use CFG to improve its prompt adherence or apply negative prompts... or is it?

1) Use the Normalized Attention Guidance (NAG) method.

Recently, we got a new method called Normalized Attention Guidance (NAG) that acts as a replacement to CFG on guidance distilled models:

- It improves the model's prompt adherence (with the nag_scale value)

- It allows you to use negative prompts

https://github.com/ChenDarYen/ComfyUI-NAG

You'll definitely notice some improvements compared to a setting that doesn't use NAG.

NAG vs no-NAG.

2) Increase the nag_scale value.

Let's go for one example, say you want to work with two image inputs, and you want the face of the first character to be replaced by the face of the second character.

Increasing the nag_scale value definitely helps the model to actually understand your requests.

If the model doesn't want to listen to your prompts, try to increase the nag_scale value.

3) Use negative prompts to mitigate some of the model's shortcomings.

Since negative prompting is now a thing with NAG, you can use it to your advantage.

For example, when using multiple characters, you might encounter an issue where the model clones the first character instead of rendering both.

Adding "clone, twins" as negative prompts can fix this.

Use negative prompts to your advantage.

4) Increase the render speed.

Since using NAG almost doubles the rendering time, it might be interesting to find a method to speed up the workflow overall. Fortunately for us, the speed boost LoRAs that were made for Flux Dev also work on Kontext Dev.

https://civitai.com/models/686704/flux-dev-to-schnell-4-step-lora

https://civitai.com/models/678829/schnell-lora-for-flux1-d

With this in mind, you can go for quality images with just 8 steps.

Personally, my favorite speed LoRA for Kontext Dev is \"Schnell LoRA for Flux.1 D\".

I provide a workflow for the "face-changing" example, including the image inputs I used. This will allow you to replicate my exact process and results.

https://files.catbox.moe/ftwmwn.json

https://files.catbox.moe/qckr9v.png (That one goes to the "load image" from the bottom of the workflow)

https://files.catbox.moe/xsdrbg.png (That one goes to the "load image" from the top of the workflow)

Flux Kontext can change a poster title/text while keeping the font and style. It's really simple, just a simple prompt.

Prompt: "replace the title "The New Avengers" with "Temu Avengers", keep the typography and style, reduce font size to fit."

Workflow: https://github.com/casc1701/workflowsgalore/blob/main/Flux%20Kontext%20I2I

As many people have noticed, Flux.1 Kontext doesn’t really "see" like OmniGen2 or UniWorld-V1—it’s probably not meant for flexible subject-driven image generation.

When you input stitched images side by side, the spatial layout stays the same in the output—which is expected, given how the model works.

But as an image editing model, it’s surprisingly flexible. So I tried approaching the "object transfer" task a bit differently: what if you treat it like refining a messy collage—letting the model smooth things out and make them look natural together?

It’s not perfect, but it gets pretty close to what I had in mind. Could be a fun way to bridge the gap between rough ideas and finished images.

Prompt : https://scrapbox.io/work4ai/FLUX.1_Kontext%E3%81%A7%E9%9B%91%E3%82%B3%E3%83%A9%E3%82%92%E3%83%AA%E3%83%95%E3%82%A1%E3%82%A4%E3%83%B3%E3%81%99%E3%82%8B

I was reading that some were having difficulty using Kontext to faceswap. This is just a basic Kontext workflow that can take a face from one source image and apply it to another image. It's not perfect, but when it works, it works very well. It can definitely be improved. Take it, make it your own, and hopefully you will post your improvements.

I tried to lay it out to make it obvious what is going on. The more of the face that occupies the destination image, the higher the denoise you can use. An upper-body portrait can go as high as 0.95 before Kontext loses the positioning. A full body shot might need 0.90 or lower to keep the face in the right spot. I will probably wind up adding a bbox crop and upscale on the face so I can keep the denoise as high as possible to maximize the resemblance. Please tell me if you see other things that could be changed or added.

https://pastebin.com/Hf3D9tnK

P.S. Kontext really needs a good non-identity altering chin LoRA. The Flux LoRAs I've tried so far don't do that great a job.

This was meant to be an extended ToonCrafter-based animation that took way longer than expected, so much so that Wan came out while I was working on it and changed the workflow I used for the dancing dragon.

The music is Ferry Corsten's trance remix of "Why Does My Heart Feel So Bad" by Moby.

I used Krita with the Acly plugin for generating animation keyframes and inpainting (sometimes frame-by-frame). I mainly used the AutismMix models for image generation. In order to create a LoRA for the knight, I used Trellis (an image-to-3d model), and used different views of the resulting 3D model to generate a (bad) LoRA dataset. I used the LoRA block loader to improve the outputs, and eventually a script I found on Github (chop_blocks.py in elias-gaeros' resize_lora repo) to create a LoRA copy with removed/reweighted blocks for ease of use from within Krita.

For the LoRA of the dragon, I instead used Wan i2v with a spinning LORA and used the frames in some of the resulting videos as a dataset. This led to better training data and a LoRA that was easier to work with.

The dancing was based on a SlimeVR mocap recording of myself dancing to the music, which was retargeted in Blender using Auto-Rig Pro (since both the knight and the dragon have different body ratios from me), and extensively manually corrected. I used toyxyz's "Character bones that look like Openpose for blender" addon to generate animated pose controlnet images.

The knight's dancing animation was made by selecting a number of openpose controlnet images, generating knight images based on them, and using ToonCrafter to interpolate between them. Because of the rather bad LoRA, this resulted in the keyframes having significant differences between them even with significant inpainting, which is why the resulting animation is not very smooth. The limitations of ToonCrafter led to significant artifacts even with a very large number of generation "takes". Tooncrafter was also used for all the animation interpolations before the dancing starts (like the interpolation between mouth positions and the flowing cape). Note that extensive compositing of the resulting animations was used to fit them into the scenes.

Since I forgot to add the knight's necklace and crown when he was dancing, I created them in Blender and aligned them to the knight's animation sequence, and did extensive compositing of the results in Da Vinci Resolve.

The dragon dancing was done with Wan-Fun-Control (image-to-video with pose control), in batches of 81 frames at half speed, using the last image as the input for the next segment. This normally leads to degradation as the last image of each segment has artifacts that compound - I tried to fix this with img2img-ing the last frame in each segment, which worked but introduced discontinuities between segments. I also used Wan-Fun-InP (first-last frame) to try and smooth out these discontinuities and fix some other issues, but this may have made things worse in some cases.

Since the dragon hands in the dancing animation were often heavily messed up, I generated some 3D dragon hands based on an input image using Hunyuan-3D (which is like Trellis but better), and used Krita's Blender Layer plugin to align these 3D dragon hands to the animation, an stiched the two together using frame-by-frame inpainting (Krita has animation support, and I made extensive use of it, but it's a bit janky). This allowed me to fix the hands without messing up the inter-frame consistency too badly.

In all cases, videos were generated on a white background and composited with the help of rembg and lots of manual masking and keying in Da Vinci Resolve.

I used Krita with the Acly plugin for the backgrounds. The compositing was done in Da Vinci Resolve, and I used KDEnLive for a few things here and there. The entire project was created on Ubuntu with (I think) the exception of the mocap capture, which was done on Windows (although I believe it can be done on Linux - SlimeVR supports it, but my Quest 3 supports it less well and requires unofficial tools like ALVR or maybe WiVRn).

I'm not particularly pleased with the end result, particularly the dancing. I think I can get better results with VACE. I didn't use VACE for much here because it wasn't out when I started the dragon dance animation part. I have to look into new developments around Wan for future animations, and figure out mocap animation retargeting better. I don't think I'll use ToonCrafter in the future except for maybe some specific problems.

In the field of text-to-image generation, achieving fine-grained control over multiple subject identities and semantic attributes (such as pose, style, lighting) while maintaining high quality and consistency has been a significant challenge. Existing methods often introduce artifacts or suffer from attribute entanglement issues, especially when handling multiple subjects.

To overcome these challenges, we propose XVerse, a novel multi-subject control generation model. XVerse enables precise and independent control of specific subjects without interfering with image latent variables or features by transforming reference images into token-specific text flow modulation offsets. As a result, XVerse provides:

✅ High-fidelity, editable multi-subject image synthesis

✅ Powerful control over individual subject characteristics

✅ Fine-grained manipulation of semantic attributes

This advancement significantly improves the capability for personalization and complex scene generation.

Paper: https://bytedance.github.io/XVerse/

Github: https://github.com/bytedance/XVerse

HF: https://huggingface.co/papers/2506.21416

Video 1: Benji's AI playground V2V with depth/pose. Great results, choppy.

Video 2: Maraan's workflow with colour correcting, modified to use video reference.

...

Benji's workflow leads to these jarring cuts, but it's very consistent output.

...

Maraan's workflow does 2 things:

1: It uses an 11 frame overlap to lead into each section of generated video, leading to smooth transitions between clips.

2: It adds in colour grading nodes to combat the creep in saturation and vibrancy that tends to occur in interative renders.

I am mostly posting for discussion as I spent most of a day playing with this trying to make it work.

I had issues with:

> The renders kept adding dirt to the dancer's face, I had to put in much more significant prompt weights than I am used to to prevent that.

> For whatever reason, the workflow results in renders that pick up on and generate from the text boxes that flash up in the original video.

> Getting the colour to match is a very time consuming process. You must render, see how it compares to the previous section, adjust parameters, and try again.

...

Keep your reference image simple and your prompts explicit and weighted. A lot of the issues I was previously having were with ill defined prompts and an excessively complex character design.

...

I think other people are working on actually trying to create workflows that will generate longer consistent outputs, I'm just trying to figure out how to use what other people have made.

I have made some adjustments to Maraan's workflow in order to incorporate V2V, I shall chuck some notes into the workflow and upload it here.

If anyone can see what I'm trying to do, and knows how to actually achieve it... please let me know.

Maraan's workflow, adjusted for V2V: https://files.catbox.moe/mia2zh.png

Benji's workflow: https://files.catbox.moe/4idh2i.png (DWPose + depthanything = good)

Benji's YouTube tutorial: https://www.youtube.com/watch?v=wo1Kh5qsUc8&t=430s&ab_channel=Benji%E2%80%99sAIPlayground

...

Original video in case any of you want to figure it out: https://files.catbox.moe/hs3f0u.mp4

I didn't see any discussion about this here, so I thought it's worth sharing:

"Building on the foundation of the Ovis series, Ovis-U1 is a 3-billion-parameter unified model that seamlessly integrates multimodal understanding, text-to-image generation, and image editing within a single powerful framework."

https://huggingface.co/AIDC-AI/Ovis-U1-3B