This is a model for High-definition magnification of the picture, trained on , and it is mainly used for losslessly enlarging images to approximately 2K size.For use in . ComfyUI Workflow This LoRA works with a modified version of Comfy's Qwen/Qwen-Image-Edit-2509 workflow . The main modification is adding a Qwen/Qwen-Image-Edit-2509 LoRA node connected to the base model. See the Downloads section above for the modified workflow. There is no fixed trigger word. The specific removal prompt needs to be tested more
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> **Ungated mirror of black-forest-labs/flux-3-image-klein-comfy.** > All credit to Black Forest Labs — this is not an official Black Forest Labs > release and is not endorsed by them. The weights are redistributed under the > FLUX Kommunity License, included here as `LICENSE.md` together > with `NOTICE.md`; any rights to use them are granted directly by > Black Forest Labs under that licence. > > **What this mirror carries** — the int8 / w4a8 builds the ComfyUI templates and > the SAUS apps use: > > | path | size | what it is | > | --- | --- | --- | > | `diffusion_models/flux3_image_klein_base_int8_convrot.safetensors` | 5.81 GB | Klein Base, undistilled — 50 steps, guidance 3.5 | > | `diffusion_models/flux3_image_klein_sd_int8_convrot.safetensors` | 5.83 GB | Klein distilled — 16 steps, no guidance | > | `text_encoders/qwen3vl_4b_int8_convrot.safetensors` | 4.86 GB | Qwen3-VL-4B-Instruct text encoder | > | `text_encoders/qwen3.5_9b_flux3_prompt_upsampler_w4a8.safetensors` | 6.72 GB | prompt upsampler | > | `vae/flux2-vae.safetensors` | 336 MB | image autoencoder | > > **Installing into ComfyUI:** copy (or symlink) `diffusion_models/` and > `text_encoders/` into `ComfyUI/models/`, and `vae/flux2-vae.safetensors` into > `ComfyUI/models/vae/`. The sub-folder layout above deliberately matches the > upstream repository, so a plain directory copy is enough. FLUX 3 Image Klein is a 5.6B open weights model for text-to-image generation and image editing. It takes a prompt and up to 10 reference images and returns one image. Text-to-image, single-reference edits and multi-reference compositions run through the same model, with Qwen3-VL-4B-Instruct as the text encoder. The 5.6B parameter label refers to the image transformer; the text encoder is separate.
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The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in as low as under a second**. Built for applications that require real-time image generation without sacrificing quality. FLUX.2 [klein] 9B is a 9 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities. Our flagship small model. Defines the Pareto frontier for quality vs. latency across text-to-image, single-reference editing, and multi-reference generation. Matches or exceeds models 5x its size—in under half a second. Built on a 9B flow model with 8B Qwen3 text embedder, step-distilled to 4 inference steps.
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Qwen-Image-Edit-2511-Lightning is a collection of optimized models tailored for image editing tasks, leveraging step distillation and quantization techniques to deliver high-efficiency inference performance. This repository hosts three core model files with distinct characteristics: This model suite supports two mainstream usage frameworks, with detailed guides provided below: For full documentation on model usage within the Qwen-Image-Lightning ecosystem (including environment setup, inference pipelines, and customization), please refer to: Qwen-Image-Lightning GitHub Repository
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Creating poses for Qwen Image Edit (2511) or its variants can be difficult. Even with openpose being baked into the model, it can still look like something is off with the result, such as the depth being incorrect or the pose not fully matching the character, with distortions that you wouldn't expect. Additionally, creating them by hand can take a bit too long, especially if you are going the blender openpose rig route. What if instead you could just skip openpose entirely and write a simple (but albeit lengthy) prompt to copy the pose of any image on your computer? Well, this is what the AnyPose LoRAs attempt to do. Made in mind with the new Qwen Image Edit 2511 lightning LoRA for fast inference, with just a single reference image as a pose guide, you can pilot any image to follow that pose. No control net needed. Use 4-step lightning LoRA for Qwen Image Edit 2511. Set both of the AnyPose LoRA weights (base & helper) strength to 0.7. Upload two images, one with the initial input character, and another with the pose you want copied to the first character. Use the following prompt, "Make the person in image 1 do the exact same pose of the person in image 2. Changing the style and background of the image of the person in image 1 is undesirable, so don't do it. The new pose should be pixel accurate to the pose we are trying to copy. The position of the arms and head and legs should be the same as the pose we are trying to copy. Change the field of view and angle to match exactly image 2. Head tilt and eye gaze pose should match the person in image 2." If additional context is needed (such as clothing that is unseen from the model), prompt it in at the end. Background not staying the same as the initial input image? Append the following to the end "Remove the background of image 2, and replace it with the background of image 1."
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Lighting is the hardest thing about AI image generation by far. Even if you specify the lighting direction with keywords, the level of control we have for lighting is primitive to say the least. It is very hard to get the desired lighting that you are actually envisioning, not just generic lighting setups like from the left, right, etc, but actual complex lighting setups that are hard to specify in just words. Professionals and people who are serious with getting the right results require it, because currently lighting is for all intents and purposes completely random. You cannot specify the distance the AI generates the light (eg. 40ft away), nor the specific X,Y,Z angle of the light. There have been other attempts to light up a scene, such as Lighting Remap Alpha0.2, which help position lights in various positions. This does help, however, it still falls to the pitfalls mentioned previously since it's based on a 2d canvas. Wouldn't it be better to have some sort of semi real-time lighting setup where you can roughly make sure the character is lit up the way that you want it while not being bound to two-dimensions? Well, this is what this LoRA attempts to do. How is this possible? Well, with a mix of a couple LoRAs (Material Preview style), Trellis2 model generation (or Hunyuan 2.1/3), and Blender, we can have more precise control over the lighting in the scene.
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This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} } Controlnet was proposed in *Adding Conditional Control to Text-to-Image Diffusion Models* by Lvmin Zhang, Maneesh Agrawala.
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This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. This checkpoint corresponds to the ControlNet conditioned on **tiled image**. Conceptually, it is similar to a super-resolution model, but its usage is not limited to that. It is also possible to generate details at the same size as the input (conditione) image. @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} }
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**Controlnet v1.1** is the successor model of Controlnet v1.0 and was released in lllyasviel/ControlNet-v1-1 by Lvmin Zhang. This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. For more details, please also have a look at the 🧨 Diffusers docs.
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**Controlnet v1.1** is the successor model of Controlnet v1.0 and was released in lllyasviel/ControlNet-v1-1 by Lvmin Zhang. This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. For more details, please also have a look at the 🧨 Diffusers docs.
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This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} } Controlnet was proposed in *Adding Conditional Control to Text-to-Image Diffusion Models* by Lvmin Zhang, Maneesh Agrawala.
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