Krea 2 Cleanup 2.6 - Local Batch (Three-Stage Depth Control + Presets)

RunningHub workflow page: https://www.runninghub.ai/zh-cn/post/2080681468573085697?inviteCode=rh-v1111

Krea 2 Cleanup 2.6 - Local Batch (Three-Stage Depth Control + Presets)

Purpose: a batch image "cleanup" graph that walks a folder of images through three depth-conditioned stages (high-noise shaping, mid-noise structure hold, low-sigma detail convergence), with RAW and Turbo base models and a 4x upscale pass at the end. This is the local-folder variant of the set.

Graph (as saved)

92 nodes / 154 links / 39 groups.

• Depth chain: three Krea2ControlImageEncode stages (high / mid / final size), each feeding its own Krea2ControlApply and its own Krea2ControlLoRALoader on depth-control-lora.safetensors at strengths 1.00 / 0.85 / 0.65 (preset-driven).
• Three further LoraLoaderModelOnly slots also point at depth-control-lora.safetensors but are saved at strength 0 - they are empty / disabled slots, not active LoRAs.
• Models: krea2_raw.safetensors for the high-noise first stage, krea2_turbo_fp8.safetensors for the mid and low stages. CLIP qwen3vl_4b_fp8_scaled.safetensors (type krea2), VAE qwen_image_HDR_vae_fp32_comfy.safetensors.
• Depth maps are generated in-graph with the DA3 nodes (LoadDA3Model / DA3Inference / DA3Render); the three encodes use match_latent_size, lanczos, center, grayscale, per-image min/max.
• Sampler chain: three SamplerCustomAdvanced nodes sharing one SamplerEulerAncestral (1, 1). Schedulers: 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 (8 steps 0.95, 10 steps 0.3, 8 steps 0.34) are saved bypassed.
• Upscale: UpscaleModelLoader 4x-UltraSharp + ImageUpscaleWithModel; two LatentUpscale nodes.
• Preset dial: one integer selects 0 = tightly controlled / faithful, 1 = balanced (default), 2 = diverging / high-noise repaint; it switches the three denoise values, Split checkpoints, RAW CFG and depth-LoRA strengths together (preset 2 sets the depth LoRA to 0).
• Batch loop: easy loadImagesForLoop + easy forLoopEnd + easy batchAnything; SaveImage prefix Krea2_本地批量洗图_2.4_RAWTurbo.

How to use

Point the loader at a folder, choose the preset number, and leave the depth-LoRA trio alone unless you deliberately override the preset. The example prompt path uses a JSON/LLM caption (RH_LLMAPI-style node present in the online variant); this local variant expects the prompt to be supplied from the JSON field or the CLIP text node.

Limits and not-verified items

No inference was run for this listing: no speed, VRAM, batch time, fidelity or upscale-quality claim is made. The saved nodes and notes are the author's configuration, not measured results. Weights, LoRA, depth model and input media are not bundled; check their licenses separately. The cover image is a promotional still from the source video, 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.