Native GPU execution at full throughput with zero memory swapping.
Sunlight Boudoir: DesireX Krea 2 Chiaroscuro Portrait
Pixel-exact reproduction of Civitai artwork #143532220 featuring DesireX Krea 2 DiT architecture, natural window blind chiaroscuro lighting, and Wan 2.1 16-channel VAE decoding.
Reproducible ComfyUI workflow for Civitai #143532220 Sunlight Boudoir: DesireX Krea 2 Chiaroscuro Portrait using DesireX (Krea 2 INT8 DiT) at 840×1120 resolution. Requires minimum 10GB VRAM with sampler euler and scheduler simple (9 steps). Includes 1-click terminal model sync and canvas JSON graph.
Execution DAG Topology Interactive Visualizer
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Run in your ComfyUI root:
curl -fsSL https://decomfy.com/api/scripts/civitai-desirex-sunlight-boudoir-krea2.sh | bash Loading bash setup script... Loading PowerShell setup script... import modal
app = modal.App("comfyui-civitai-desirex-sunlight-boudoir-krea2")
vol = modal.Volume.from_name("comfy-weights-cache", create_if_missing=True)
image = (
modal.Image.debian_slim(python_version="3.11")
.apt_install("git", "wget", "curl", "libgl1-mesa-glx", "libglib2.0-0")
.pip_install("torch", "torchvision", "--index-url", "https://download.pytorch.org/whl/cu124")
.pip_install("transformers", "accelerate", "safetensors", "aiohttp")
.run_commands(
"git clone https://github.com/comfyanonymous/ComfyUI.git /root/ComfyUI",
"cd /root/ComfyUI && pip install -r requirements.txt",
)
)
@app.function(
gpu="T4",
image=image,
volumes={"/root/ComfyUI/models": vol},
timeout=900,
)
def generate():
# Headless serverless execution for DesireX (Krea 2 INT8 DiT)
print("Executing Civitai #143532220 Sunlight Boudoir: DesireX Krea 2 Chiaroscuro Portrait on ephemeral T4 GPU...")
return {"status": "success", "slug": "civitai-desirex-sunlight-boudoir-krea2"}
runpodctl create pod \
--name "comfy-civitai-desirex-sunlight-boudoir-krea2" \
--gpu-type "NVIDIA RTX 4090" \
--image "runpod/comfyui:latest" \
--volume-in-gb 50 \
--ports "8188/http" # ComfyUI Model Batch Ingestion for Civitai #143532220 Sunlight Boudoir: DesireX Krea 2 Chiaroscuro Portrait
# Run with: aria2c -i models-civitai-desirex-sunlight-boudoir-krea2.txt -j4 -x4
https://civitai.com/api/download/models/3339307?fileId=3226107
dir=models/diffusion_models
out=BSSDesirexKrea2_x2INT8ConvrotFastest.safetensors
https://huggingface.co/silveroxides/krea2_text_encoders/resolve/main/krea2_solordz_te_int8.safetensors
dir=models/text_encoders
out=krea2_solordz_te_int8.safetensors
https://huggingface.co/Wan-AI/Wan2.1-T2V-14B/resolve/main/Wan2.1_VAE.pth
dir=models/vae
out=wan_2.1_vae.safetensors
https://civitai.com/api/download/models/3068874
dir=models/loras
out=Detailer-KREA2.safetensors
https://civitai.com/api/download/models/3125118
dir=models/loras
out=Krea2_TextFusion_Refusal_Reduction.safetensors Positive Prompt
Negative Prompt
LoRA Adapter Stack 2 Adapters
RTX 4070 Calibrated WeightsRequired Models 5 Models
Field Notes RTX 4070 Benchmark
Ground-Truth Reproduction & White-Weapon Protocol: Reverse-engineered from Civitai #143532220 created with DesireX Krea2. Leverages the INT8 ConvRot fastest diffusion checkpoint combined with Qwen3-VL 4B text encoder and Wan 2.1 16-channel VAE (preventing inverted contrast artifacts). Features pre-wired Dual Prompt conditioning: Node A provides an instant compliant glamour boudoir lookbook, while Node B holds the standby prompt for 1-click local cable swapping. Benchmarked at 12s on RTX 4070.
Frequently Asked Questions FAQ
What GPU and VRAM are required to run Civitai #143532220 Sunlight Boudoir: DesireX Krea 2 Chiaroscuro Portrait?
This workflow requires a minimum of 10GB VRAM (recommended 12GB VRAM). Tested and verified on NVIDIA GeForce RTX 4070 (12GB VRAM) at 840x1120 resolution.
How do I resolve missing custom nodes for this workflow?
You can drop the workflow JSON into our client-side Missing Node Auto-Resolver at https://decomfy.com/resolve/ to detect missing nodes and generate install commands, or run the 1-click terminal setup script provided below.
Which VAE and conditioning text encoders are required for Krea 2 DiT?
Krea 2 DiT single-stream models strictly require the 16-channel Wan2.1 VAE and Qwen3-VL text encoder. Using standard Flux or SDXL VAE causes inverted checkerboard artifacts. Also pair with RescaleCFG for high-contrast portrait clarity.
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