基于 DGX Spark 部署 Stable Diffusion 3.5 及 ComfyUI 实战

第三步：部署 ComfyUI 与修复 ARM 依赖

克隆与安装

cd /workspace
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
# 安装常规依赖 (使用 --no-deps 防止破坏 Torch 环境)
pip install -r requirements.txt --no-deps
# 核心修复：补齐 ARM 架构下缺失的特殊库
# 如果缺少这些，ComfyUI 启动时会报错 "No module named kornia_rs" 或 "trampoline"
pip install trampoline kornia_rs mako comfyui-workflow-templates sqlalchemy alembic requests

挂载模型

为了利用 DGX Spark 的存储性能，我们使用软链接映射模型。

# 1. 链接主模型
ln -s /workspace/models/stabilityai/stable-diffusion-3.5-large/sd3.5_large.safetensors ./models/checkpoints/
# 2. 链接 CLIP 模型 (SD3 需要 3 个 CLIP)
# 注意：我们直接使用 FP16 精度的 T5，因为显存足够大
ln -s /workspace/models/stabilityai/stable-diffusion-3.5-large/text_encoders/clip_l.safetensors ./models/clip/
ln -s /workspace/models/stabilityai/stable-diffusion-3.5-large/text_encoders/clip_g.safetensors ./models/clip/
ln -s /workspace/models/stabilityai/stable-diffusion-3.5-large/text_encoders/t5xxl_fp16.safetensors ./models/clip/

第四步：高性能启动

针对 128GB 显存，我们开启'高显存模式'——让所有模型永久驻留显存，拒绝反复加载。

创建启动脚本 run_comfy.sh：

#!/bin/bash
WORKDIR="/workspace/ComfyUI"
PYTHON_PATH="/root/miniconda3/envs/sd-runtime/bin/python"
# 日志管理
LOG_DIR="$WORKDIR/logs"
mkdir -p "$LOG_DIR"
LOG_FILE="$LOG_DIR/server_$(date "+%Y-%m-%d_%H-%M-%S").log"
cd "$WORKDIR" || exit
echo "🚀 Starting ComfyUI on DGX Spark (Blackwell)..."
echo "⚡ Mode: High VRAM (Force Resident)"
# --highvram: 强制模型常驻显存
nohup "$PYTHON_PATH" main.py \
--listen 0.0.0.0 \
--port 8188 \
--highvram \
> "$LOG_FILE" 2>&1 &
echo "✅ Service PID: $!"

启动服务：chmod +x run_comfy.sh && ./run_comfy.sh

第五步：导入 SD3.5 标准工作流

SD3.5 采用了分离式文本编码器架构（CLIP-L + CLIP-G + T5），默认工作流无法正常出图。

请复制下方完整的 JSON 代码，保存为 sd3_workflow.json，直接拖入 ComfyUI 浏览器界面即可使用。此工作流已针对 1024x1024 分辨率和 DGX Spark 进行了优化。

{"last_node_id":10,"last_link_id":16,"nodes":[{"id":1,"type":"KSampler","pos":[960,190],"size":{"0":315,"1":262},"flags":{},"order":0,"mode":0,"inputs":[{"name":"model","type":"MODEL","link":1},{"name":"positive","type":"CONDITIONING","link":2},{"name":"negative","type":"CONDITIONING","link":3},{"name":"latent_image","type":"LATENT","link":4}],"outputs":[{"name":"LATENT","type":"LATENT","links":[5],"slot_index":0}],"properties":{"Node name for S&R":"KSampler"},"widgets_values":[1234567890,"randomize",30,4.5,"euler","sgm_uniform",1]},{"id":2,"type":"CheckpointLoaderSimple","pos":[50,50],"size":{"0":315,"1":98},"flags":{},"order":1,"mode":0,"outputs":[{"name":"MODEL","type":"MODEL","links":[1],"slot_index":0},{"name":"CLIP","type":"CLIP","links":[],"slot_index":1},{"name":"VAE","type":"VAE","links":[6],"slot_index":2}],"properties":{"Node name for S&R":"CheckpointLoaderSimple"},"widgets_values":["sd3.5_large.safetensors"]},{"id":3,"type":"TripleCLIPLoader","pos":[50,200],"size":{"0":315,"1":150},"flags":{},"order":2,"mode":0,"outputs":[{"name":"CLIP","type":"CLIP","links":[7,8],"slot_index":0}],"properties":{"Node name for S&R":"TripleCLIPLoader"},"widgets_values":["clip_l.safetensors","clip_g.safetensors","t5xxl_fp16.safetensors"]},{"id":4,"type":"CLIPTextEncode","pos":[450,100],"size":{"0":425,"1":180},"flags":{},"order":3,"mode":0,"inputs":[{"name":"clip","type":"CLIP","link":7}],"outputs":[{"name":"CONDITIONING","type":"CONDITIONING","links":[2],"slot_index":0}],"properties":{"Node name for S&R":"CLIPTextEncode"},"widgets_values":["a highly detailed cyberpunk city, neon lights, 8k resolution, cinematic lighting, masterpiece, sharp focus, ray tracing, nvidia blackwell style"],"color":"#233","bgcolor":"#353"},{"id":5,"type":"CLIPTextEncode","pos":[450,350],"size":{"0":425,"1":180},"flags":{},"order":4,"mode":0,"inputs":[{"name":"clip","type":"CLIP","link":8}],"outputs":[{"name":"CONDITIONING","type":"CONDITIONING","links":[3],"slot_index":0}],"properties":{"Node name for S&R":"CLIPTextEncode"},"widgets_values":["low quality, blurry, ugly, bad anatomy, watermark, text, jpeg artifacts"],"color":"#332","bgcolor":"#533"},{"id":6,"type":"EmptyLatentImage","pos":[50,450],"size":{"0":315,"1":106},"flags":{},"order":5,"mode":0,"outputs":[{"name":"LATENT","type":"LATENT","links":[4],"slot_index":0}],"properties":{"Node name for S&R":"EmptyLatentImage"},"widgets_values":[1024,1024,1]},{"id":7,"type":"VAEDecode","pos":[1300,190],"size":{"0":210,"1":46},"flags":{},"order":6,"mode":0,"inputs":[{"name":"samples","type":"LATENT","link":5},{"name":"vae","type":"VAE","link":6}],"outputs":[{"name":"IMAGE","type":"IMAGE","links":[9],"slot_index":0}],"properties":{"Node name for S&R":"VAEDecode"}},{"id":8,"type":"SaveImage","pos":[1550,190],"size":{"0":500,"1":600},"flags":{},"order":7,"mode":0,"inputs":[{"name":"images","type":"IMAGE","link":9}],"properties":{"Node name for S&R":"SaveImage"},"widgets_values":["ComfyUI"]}],"links":[[1,2,0,1,0,"MODEL"],[2,4,0,1,1,"CONDITIONING"],[3,5,0,1,2,"CONDITIONING"],[4,6,0,1,3,"LATENT"],[5,1,0,7,0,"LATENT"],[6,2,2,7,1,"VAE"],[7,3,0,4,0,"CLIP"],[8,3,0,5,0,"CLIP"],[9,7,0,8,0,"IMAGE"]],"groups":[],"config":{},"extra":{},"version":0.4}

流程逻辑如下：

1. TripleCLIPLoader: 这是核心。我们明确指定了 clip_l, clip_g 和 t5xxl_fp16。如果这三个没选对，生成的图片将无法理解你的 Prompt。
2. Scheduler: sgm_uniform: Stable Diffusion 3 必须使用 SGM Uniform 调度器，否则画面会充满噪点。
3. Resolution: 默认设为 1024x1024。在 Blackwell 上，你可以尝试将其改为 2048x2048 进行原生 4K 生成。

性能总结

在 DGX Spark 上完成上述部署后，性能表现如下：

• 模型加载: 首次启动后，模型将永久驻留显存 (High VRAM Mode)。
• 生成速度: 1024x1024 分辨率下，实现秒级出图。
• 并发能力: 得益于 128GB 显存，你可以同时开启 Batch Size = 4 甚至更高，或者在后台同时运行一个 70B 的大语言模型而不发生 OOM。