https://youtu.be/949qxjckUyI
FLUX 3 Klein 4K cloud batch upscale workflow (RunningHub online ZIP edition). Drop a ZIP of images on RunningHub and the whole folder is upscaled and re-generated at 4K with no local GPU.
Main features:
- HAIGC_LoadImagesFromZip: upload one ZIP (jpg/png), all images enter the queue automatically; HAIGC_SaveImagesToZip packs the 4K results back into a downloadable ZIP
- Auto prompt reconstruction: RHLLMChatNode reads each image and writes the rebuild prompt (visual auto captioning, no manual prompting)
- 16-step FLUX 3 Klein reconstruction: flux3_image_klein_sd_int8_convrot UNET, qwen3vl_4b CLIP, flux2-vae; KSampler 16 steps, CFG 1, euler/simple, fixed seed; ReferenceMultiLatent + ConditioningZeroOut keep the upscale faithful to the source
- 4x AI super-resolution: RealESRGAN_x4plus (ImageUpscaleWithModel) after ImageScaleToMaxDimension
- rgthree Image Comparer shows original vs 4K per image; SaveImage prefix AIKSK_FLUX3_KLEIN_FINAL_RH_BATCH
Suggested workflow:
1. Upload a ZIP of your images to RunningHub
2. Queue the graph; unzip, caption, upscale, 16-step rebuild and re-zip all run automatically
3. Download the result ZIP; compare 1K vs 4K in the comparer
Dependencies:
- flux3_image_klein_sd_int8_convrot.safetensors (FLUX 3 Klein UNET)
- qwen3vl_4b_int8_convrot.safetensors (CLIP)
- flux2-vae.safetensors (VAE)
- RealESRGAN_x4plus.pth (upscale model)
- HAIGC ZIP batch nodes, RHLLMChatNode, rgthree Node Suite
RunningHub workflow page: https://www.runninghub.ai/zh-cn/post/2112077292083163137?inviteCode=rh-v1111
Bilibili tutorial video: https://www.bilibili.com/video/BV11xJo6nEFh/
RunningHub registration with invite code rh-v1111 (1000 RH coins): https://www.runninghub.ai/?inviteCode=rh-v1111
FLUX 3 Klein 4K cloud batch upscale workflow (RunningHub online ZIP edition). Drop a ZIP of images on RunningHub and the whole folder is upscaled and re-generated at 4K with no local GPU.
Main features:
- HAIGC_LoadImagesFromZip: upload one ZIP (jpg/png), all images enter the queue automatically; HAIGC_SaveImagesToZip packs the 4K results back into a downloadable ZIP
- Auto prompt reconstruction: RHLLMChatNode reads each image and writes the rebuild prompt (visual auto captioning, no manual prompting)
- 16-step FLUX 3 Klein reconstruction: flux3_image_klein_sd_int8_convrot UNET, qwen3vl_4b CLIP, flux2-vae; KSampler 16 steps, CFG 1, euler/simple, fixed seed; ReferenceMultiLatent + ConditioningZeroOut keep the upscale faithful to the source
- 4x AI super-resolution: RealESRGAN_x4plus (ImageUpscaleWithModel) after ImageScaleToMaxDimension
- rgthree Image Comparer shows original vs 4K per image; SaveImage prefix AIKSK_FLUX3_KLEIN_FINAL_RH_BATCH
Suggested workflow:
1. Upload a ZIP of your images to RunningHub
2. Queue the graph; unzip, caption, upscale, 16-step rebuild and re-zip all run automatically
3. Download the result ZIP; compare 1K vs 4K in the comparer
Dependencies:
- flux3_image_klein_sd_int8_convrot.safetensors (FLUX 3 Klein UNET)
- qwen3vl_4b_int8_convrot.safetensors (CLIP)
- flux2-vae.safetensors (VAE)
- RealESRGAN_x4plus.pth (upscale model)
- HAIGC ZIP batch nodes, RHLLMChatNode, rgthree Node Suite
RunningHub workflow page: https://www.runninghub.ai/zh-cn/post/2112077292083163137?inviteCode=rh-v1111
Bilibili tutorial video: https://www.bilibili.com/video/BV11xJo6nEFh/
RunningHub registration with invite code rh-v1111 (1000 RH coins): https://www.runninghub.ai/?inviteCode=rh-v1111
Description
https://youtu.be/949qxjckUyI
FLUX 3 Klein 4K cloud batch upscale workflow (RunningHub online ZIP edition). Drop a ZIP of images on RunningHub and the whole folder is upscaled and re-generated at 4K with no local GPU.
Main features:
- HAIGC_LoadImagesFromZip: upload one ZIP (jpg/png), all images enter the queue automatically; HAIGC_SaveImagesToZip packs the 4K results back into a downloadable ZIP
- Auto prompt reconstruction: RHLLMChatNode reads each image and writes the rebuild prompt (visual auto captioning, no manual prompting)
- 16-step FLUX 3 Klein reconstruction: flux3_image_klein_sd_int8_convrot UNET, qwen3vl_4b CLIP, flux2-vae; KSampler 16 steps, CFG 1, euler/simple, fixed seed; ReferenceMultiLatent + ConditioningZeroOut keep the upscale faithful to the source
- 4x AI super-resolution: RealESRGAN_x4plus (ImageUpscaleWithModel) after ImageScaleToMaxDimension
- rgthree Image Comparer shows original vs 4K per image; SaveImage prefix AIKSK_FLUX3_KLEIN_FINAL_RH_BATCH
Suggested workflow:
1. Upload a ZIP of your images to RunningHub
2. Queue the graph; unzip, caption, upscale, 16-step rebuild and re-zip all run automatically
3. Download the result ZIP; compare 1K vs 4K in the comparer
Dependencies:
- flux3_image_klein_sd_int8_convrot.safetensors (FLUX 3 Klein UNET)
- qwen3vl_4b_int8_convrot.safetensors (CLIP)
- flux2-vae.safetensors (VAE)
- RealESRGAN_x4plus.pth (upscale model)
- HAIGC ZIP batch nodes, RHLLMChatNode, rgthree Node Suite
RunningHub workflow page: https://www.runninghub.ai/zh-cn/post/2112077292083163137?inviteCode=rh-v1111
Bilibili tutorial video: https://www.bilibili.com/video/BV11xJo6nEFh/
RunningHub registration with invite code rh-v1111 (1000 RH coins): https://www.runninghub.ai/?inviteCode=rh-v1111
FLUX 3 Klein 4K cloud batch upscale workflow (RunningHub online ZIP edition). Drop a ZIP of images on RunningHub and the whole folder is upscaled and re-generated at 4K with no local GPU.
Main features:
- HAIGC_LoadImagesFromZip: upload one ZIP (jpg/png), all images enter the queue automatically; HAIGC_SaveImagesToZip packs the 4K results back into a downloadable ZIP
- Auto prompt reconstruction: RHLLMChatNode reads each image and writes the rebuild prompt (visual auto captioning, no manual prompting)
- 16-step FLUX 3 Klein reconstruction: flux3_image_klein_sd_int8_convrot UNET, qwen3vl_4b CLIP, flux2-vae; KSampler 16 steps, CFG 1, euler/simple, fixed seed; ReferenceMultiLatent + ConditioningZeroOut keep the upscale faithful to the source
- 4x AI super-resolution: RealESRGAN_x4plus (ImageUpscaleWithModel) after ImageScaleToMaxDimension
- rgthree Image Comparer shows original vs 4K per image; SaveImage prefix AIKSK_FLUX3_KLEIN_FINAL_RH_BATCH
Suggested workflow:
1. Upload a ZIP of your images to RunningHub
2. Queue the graph; unzip, caption, upscale, 16-step rebuild and re-zip all run automatically
3. Download the result ZIP; compare 1K vs 4K in the comparer
Dependencies:
- flux3_image_klein_sd_int8_convrot.safetensors (FLUX 3 Klein UNET)
- qwen3vl_4b_int8_convrot.safetensors (CLIP)
- flux2-vae.safetensors (VAE)
- RealESRGAN_x4plus.pth (upscale model)
- HAIGC ZIP batch nodes, RHLLMChatNode, rgthree Node Suite
RunningHub workflow page: https://www.runninghub.ai/zh-cn/post/2112077292083163137?inviteCode=rh-v1111
Bilibili tutorial video: https://www.bilibili.com/video/BV11xJo6nEFh/
RunningHub registration with invite code rh-v1111 (1000 RH coins): https://www.runninghub.ai/?inviteCode=rh-v1111