RunningHub workflow page: https://www.runninghub.ai/zh-cn/post/2080681462248071170?inviteCode=rh-v1111
Krea 2 Cleanup 2.6 - RunningHub Online Batch (Three-Stage Depth Control + Presets)
Purpose: the RunningHub-hosted batch variant of the Krea 2 cleanup graph. Same three depth-conditioned stages and the same preset dial as the local version, but the batch I/O runs through RunningHub nodes: upload a ZIP, extract images, batch them, and bring LLM captions back through the RunningHub LLM node.
Graph (as saved)
104 nodes / 169 links / 57 groups.
- Depth chain: three
Krea2ControlImageEncodestages (high / mid / final size) each feedingKrea2ControlApplyand aKrea2ControlLoRALoaderondepth-control-lora.safetensorsat 1.00 / 0.85 / 0.65 (preset-driven). - The three
LoraLoaderModelOnlyslots on the same LoRA file are saved at strength 0: empty/disabled slots, not active LoRAs. - Models:
krea2_raw_bf16_0630.safetensorsfor the high-noise stage,krea2_turbo_fp8.safetensorsfor mid/low. CLIPqwen3vl_4b_fp8_scaled.safetensors(typekrea2), VAEqwen_image_HDR_vae_fp32_comfy.safetensors. - RunningHub-specific nodes:
RHUploadZip,RHBatchImages:,RH_ExtractImage,RH_LLMAPI_Pro_Node- the online batch path and the automatic caption/JSON path. - Samplers: three
SamplerCustomAdvancedwith one sharedSamplerEulerAncestral(1, 1); shared RAW/Turbo schedule 36 steps denoise 0.92; Turbo upscale refine 10 steps denoise 0.35; active intermediate KSampler 10 steps, CFG 1, euler_ancestral, simple, denoise 0.40; legacy KSamplers saved bypassed. - Depth maps via DA3 nodes; upscale via 4x-UltraSharp +
ImageUpscaleWithModel; SaveImage prefixKrea2_线上批量洗图_2.4_RAWTurbo. - Preset dial: 0 = tightly controlled, 1 = balanced (default), 2 = diverging high-noise repaint; switches denoise, Split checkpoints, RAW CFG and depth-LoRA strengths together.
How to use
Upload a ZIP of images, select the preset, and queue. The LLM node expects a RunningHub API/LLM endpoint; if you run this outside RunningHub, that branch and the RH I/O nodes will not resolve, so either run it on the platform or replace them with local loaders.
Limits and not-verified items
No inference was run for this listing: no speed, VRAM, batch throughput, fidelity or upscale-quality claim is made. RunningHub node behaviour and availability are platform-side; this file is a saved snapshot, not an installation guarantee. Weights, LoRA, depth model and input media are not bundled. The cover image is a promotional still, not a frame produced by this graph.
Tutorial: YouTube · Bilibili. Workflows supplied through AI-KSK; verify workflow, node, model, LoRA, input-image and cover rights separately. No blanket commercial or redistribution license is asserted.
Description
RunningHub workflow page: https://www.runninghub.ai/zh-cn/post/2080681462248071170?inviteCode=rh-v1111
Krea 2 Cleanup 2.6 - RunningHub Online Batch (Three-Stage Depth Control + Presets)Purpose: the RunningHub-hosted batch variant of the Krea 2 cleanup graph. Same three depth-conditioned stages and the same preset dial as the local version, but the batch I/O runs through RunningHub nodes: upload a ZIP, extract images, batch them, and bring LLM captions back through the RunningHub LLM node.
Graph (as saved)104 nodes / 169 links / 57 groups.
- Depth chain: three
Krea2ControlImageEncodestages (high / mid / final size) each feedingKrea2ControlApplyand aKrea2ControlLoRALoaderondepth-control-lora.safetensorsat 1.00 / 0.85 / 0.65 (preset-driven). - The three
LoraLoaderModelOnlyslots on the same LoRA file are saved at strength 0: empty/disabled slots, not active LoRAs. - Models:
krea2_raw_bf16_0630.safetensorsfor the high-noise stage,krea2_turbo_fp8.safetensorsfor mid/low. CLIPqwen3vl_4b_fp8_scaled.safetensors(typekrea2), VAEqwen_image_HDR_vae_fp32_comfy.safetensors. - RunningHub-specific nodes:
RHUploadZip,RHBatchImages:,RH_ExtractImage,RH_LLMAPI_Pro_Node- the online batch path and the automatic caption/JSON path. - Samplers: three
SamplerCustomAdvancedwith one sharedSamplerEulerAncestral(1, 1); shared RAW/Turbo schedule 36 steps denoise 0.92; Turbo upscale refine 10 steps denoise 0.35; active intermediate KSampler 10 steps, CFG 1, euler_ancestral, simple, denoise 0.40; legacy KSamplers saved bypassed. - Depth maps via DA3 nodes; upscale via 4x-UltraSharp +
ImageUpscaleWithModel; SaveImage prefixKrea2_线上批量洗图_2.4_RAWTurbo. - Preset dial: 0 = tightly controlled, 1 = balanced (default), 2 = diverging high-noise repaint; switches denoise, Split checkpoints, RAW CFG and depth-LoRA strengths together.
Upload a ZIP of images, select the preset, and queue. The LLM node expects a RunningHub API/LLM endpoint; if you run this outside RunningHub, that branch and the RH I/O nodes will not resolve, so either run it on the platform or replace them with local loaders.
Limits and not-verified itemsNo inference was run for this listing: no speed, VRAM, batch throughput, fidelity or upscale-quality claim is made. RunningHub node behaviour and availability are platform-side; this file is a saved snapshot, not an installation guarantee. Weights, LoRA, depth model and input media are not bundled. The cover image is a promotional still, not a frame produced by this graph.
Tutorial: YouTube · Bilibili. Workflows supplied through AI-KSK; verify workflow, node, model, LoRA, input-image and cover rights separately. No blanket commercial or redistribution license is asserted.