In my previous posts I talked about how you can use LTX-2 as an WAN upscaler/refiner and how to add external actors and elements references without img2vid (you need an empty scene without them and need them to come into the scene). But why not both ? LTX-2 sux in action sequences and human interactions so the alternative at this point is wan 2.2 . But wan is lowres and has the same issue as ltx, no way for now to add actors in latent space. So I used the same technique as for LTX2 to add actors to wan and then reinforce them in LTX-2 using the same method. Here are some results:
Idea : Generate a very low res wan 2.2 video as reference for LTX but still pre-appending the actors and elements images at the beginning of the video,, then have the first image from the actual shot and referencing the characters from the beginning in the video. This step at 480P is very fast and good enough for characters interaction/movement coherence etc to be used as vid2vid in ltx-2. We save it at 12 fps so we can upscale with temporal upscaler in ltx. Then in the LTX step we bring the same intro images but at highest resolution possible so ltx knows how the characters actually look like in maximum detail and paints them over the lowres wan video at at a 4x resolution. So the 480p video becomes 1440p in this case (but you can go lower if you don’t have the resources, I have an 3090 and 64GB system ram). Both qwen image edit and flux klein were used for generating the actors, scene, zoom ins on the scene, removing characters etc.
This for me was the biggest problem with LTX-2, the inability to add characters from outside the camera without training a lora. So I finally managed to get something working (workflow). please check out the other article where I expanded to wan2.2 and used ltx on top. much better for some cases like character interaction and action where ltx is a mess.
After I made myfull photo archive available for free sume reddit users that I thank like NobodyButMeow created a Qwen Image Lora after my photos. What stroke me was that using the initial caption text the photos resemble the original a lot, as you can se bellow. I have to mention that I am also using a WAN 2.2 refiner like in the workflow here . The LORA is available here, no triggerwords needed. Here is a sample prompt for the second image : “A landscape at sunset, featuring a prominent, conical mountain in the foreground. The mountain is covered with snow, and its peak is illuminated by the setting sun, casting a warm, golden glow across the scene. The sky is filled with dramatic clouds, adding depth and texture to the composition. In the foreground, there is a small waterfall cascading over a rocky surface, partially covered in ice and snow. The water appears to be flowing gently, creating a sense of tranquility. The background reveals a vast, open landscape with more mountains and a body of water reflecting the sunset colors.”
A lot of people asked how they could get results like mine using chroma Radiance. In short you cannot get good results out of the box. You need a good negative prompt like the one I set up and use technical terms in the main prompt like: point lighting, volumetric light, dof, vignette, surface shading, blue and orange colors etc. You don’t neet very long prompts and it tends to lose itself when doing so. It is based on Flux so prompting is closer to flux. And the most important thing is the wan 2.2 refiner that is also in the workflow. Play around with the denoising, I am using between 0.15 and 0.25 but never ever more, usually 0.20. This also get rids of the grid pattern that is so visible in Chroma radiance and wrong hands and fingers. The model is very good for “fever dreams” kind of images, abstract, combining materials and elements into something new, playing around with new visual ideas. In a way like SD 1.5 models are. It is also very hit and miss. While using the same seed allows for tuning the prompt keeping the same rest of the composition and subjects changing the seed radically changes the result so you need to have pacience with it. Imho the results are worth it. Also sometimes you need to correct things in photoshop using generative fill. The workflow I am using is here . Here is a small gallery :
This is the refiner/upscaler I am using for most of my images. It uses the realism and details of wan 2.2 video model but for images to polish images from qwen/Chroma/SD1.4/SDXL/Flux etc.
Because somebody on Reddit asked how could he caption a dataset for Qwen Image and mentain consistancy I made a small ComfyUI workflow that uses Qwen 2.5 VL 7B Instruct to autocaption the images in a folder, name them, caption them and save them all in another folder. It should be straightforward to use but you will have to manage the missing nodes and models yourself
