ROCm-Research-Archive/Danis ROCm Kernel Patch Research/ComfyUI_ZImage_Documentation.md

ComfyUI + Z-Image Turbo on AMD BC-250 — Complete Setup Guide¶

Hardware: AMD BC-250 (Cyan Skillfish, gfx1013→gfx1010, 24 CUs, shared RAM)
Backend: ROCm 7.2.0 / PyTorch 2.5.1+rocm6.2
OS: CachyOS, kernel 6.18.8-3-cachyos
ComfyUI Version: 0.15.1
Date: 2026-03-02

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Table of Contents¶

1. Overview
2. Architecture
3. Prerequisites
4. Installation — Step by Step
5. Model Setup
6. Launch Script
7. ComfyUI Workflow — Z-Image Turbo
8. BC-250 Specific Tuning
9. Troubleshooting
10. File Inventory
11. Performance Notes

---

1. Overview¶

ComfyUI is a node-based Stable Diffusion GUI that runs Z-Image Turbo (a Lumina2-architecture model) via PyTorch with ROCm/HIP on the AMD BC-250 GPU. The model uses a GGUF-quantized diffusion model (Q5_K_S) loaded via the ComfyUI-GGUF custom node, with a Gemma 2 2B text encoder and a Flux-compatible VAE.

What's Running¶

Component	File	Size	Format
Diffusion Model	z_image_turbo-Q5_K_S.gguf	5.2 GB	GGUF Q5_K_S
Text Encoder	gemma2_2b_lumina2.safetensors	9.8 GB	Safetensors (f32)
VAE	ae.safetensors	335 MB	Safetensors (f32)

Pipeline¶

Text Only

[ComfyUI WebUI :8188] → [PyTorch] → [ROCm/HIP] → [AMD BC-250 GPU]
                                                      ↓
[Gemma 2 2B Text Encoder] → CLIP Encode → [Z-Image Turbo Diffusion] → [VAE Decode] → Image

---

2. Architecture¶

Z-Image Turbo Details¶

• Architecture: Lumina2 (Lumina-Image 2.0 family)
• Base: Z-Image by Freepik, turbo-distilled variant
• Text Encoder: Gemma 2 2B (Google, 2304-dim embeddings)
• VAE: Flux-compatible autoencoder (ae.safetensors)
• Sampler: Euler with SGM Uniform scheduler, 8 steps (turbo)
• CFG Scale: 3.0 (turbo models use low CFG)
• Latent Format: Flux-style latent space

Why GGUF?¶

The BC-250 has ~14.7 GB shared system RAM. The full FP16 diffusion model would be too large. GGUF Q5_K_S quantization reduces the model from ~12+ GB to 5.2 GB, making it feasible alongside the text encoder and VAE.

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3. Prerequisites¶

Before starting, you need ROCm working on the BC-250. See ROCm_BC250_Documentation.md for the full ROCm setup.

Required¶

• ROCm 7.2.0 installed and working (rocminfo detects BC-250)
• Python 3.11 (/usr/bin/python3.11)
• Git
• ~30 GB free disk space

Verify ROCm¶

Bash

rocminfo | grep "Name:"
# Should show: gfx1010 and AMD BC-250

---

4. Installation — Step by Step¶

4.1 Clone ComfyUI¶

Bash

cd ~
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI

4.2 Create Python 3.11 Virtual Environment¶

Python 3.11 is required — Python 3.14 (system default) is too new for PyTorch ROCm wheels.

Bash

python3.11 -m venv venv
source venv/bin/activate

4.3 Install PyTorch with ROCm Support¶

Bash

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2

This downloads ~4 GB. The ROCm 6.2 PyTorch wheel is compatible with the ROCm 7.2 runtime.

Verify installation:

Bash

python -c "import torch; print(torch.version.cuda); print(torch.cuda.is_available())"
# Should print: 6.2 and True

4.4 Install ComfyUI Dependencies¶

Bash

pip install -r requirements.txt

4.5 Install ComfyUI-GGUF Custom Node¶

This enables loading GGUF-quantized models in ComfyUI.

Bash

cd ~/ComfyUI/custom_nodes
git clone https://github.com/city96/ComfyUI-GGUF.git
source ~/ComfyUI/venv/bin/activate
pip install gguf

4.6 Install huggingface-hub (for model downloads)¶

Bash

pip install huggingface-hub

---

5. Model Setup¶

5.1 Directory Structure¶

ComfyUI looks for models in ~/ComfyUI/models/. Our models live in ~/sd-models/ and are symlinked.

Text Only

~/ComfyUI/models/
├── unet/
│   └── z_image_turbo-Q5_K_S.gguf → ~/sd-models/diffusion_models/z_image_turbo-Q5_K_S.gguf
├── text_encoders/
│   └── gemma2_2b_lumina2.safetensors   (merged from 3 shards, 9.8 GB)
├── vae/
│   └── ae.safetensors → ~/sd-models/vae/ae.safetensors
└── ...

5.2 Symlink Diffusion Model (GGUF)¶

The Z-Image Turbo GGUF model must go in models/unet/ (ComfyUI-GGUF's UnetLoaderGGUF node reads from there):

Bash

ln -sf /home/dars/sd-models/diffusion_models/z_image_turbo-Q5_K_S.gguf \
       ~/ComfyUI/models/unet/z_image_turbo-Q5_K_S.gguf

5.3 Text Encoder — Gemma 2 2B¶

Z-Image Turbo uses the Gemma 2 2B text encoder from the Lumina-Image-2.0 family. The original model is sharded into 3 safetensors files. We merge them into a single file for ComfyUI.

Download from Alpha-VLLM (not gated, no login required):

Bash

source ~/ComfyUI/venv/bin/activate
python3 -c "
from huggingface_hub import hf_hub_download
import os

repo = 'Alpha-VLLM/Lumina-Image-2.0'
dest = os.path.expanduser('~/sd-models/text_encoders/lumina2_gemma2_2b')
os.makedirs(dest, exist_ok=True)

files = [
    'text_encoder/config.json',
    'text_encoder/model.safetensors.index.json',
    'text_encoder/model-00001-of-00003.safetensors',
    'text_encoder/model-00002-of-00003.safetensors',
    'text_encoder/model-00003-of-00003.safetensors',
]
for f in files:
    print(f'Downloading {f}...')
    hf_hub_download(repo, f, local_dir=dest)
print('Done!')
"

Merge shards into single file:

Bash

source ~/ComfyUI/venv/bin/activate
python3 << 'EOF'
import safetensors.torch
import torch
import os, json

base_dir = os.path.expanduser("~/sd-models/text_encoders/lumina2_gemma2_2b/text_encoder")
output = os.path.expanduser("~/ComfyUI/models/text_encoders/gemma2_2b_lumina2.safetensors")
os.makedirs(os.path.dirname(output), exist_ok=True)

with open(os.path.join(base_dir, "model.safetensors.index.json")) as f:
    index = json.load(f)

all_tensors = {}
shards = set(index["weight_map"].values())
print(f"Loading {len(shards)} shards with {len(index['weight_map'])} tensors...")
for shard in sorted(shards):
    path = os.path.join(base_dir, shard)
    print(f"  Loading {shard}...")
    tensors = safetensors.torch.load_file(path, device="cpu")
    all_tensors.update(tensors)

print(f"Total tensors: {len(all_tensors)}")
print(f"Saving merged file...")
safetensors.torch.save_file(all_tensors, output)
print(f"Done! Size: {os.path.getsize(output)/1e9:.2f} GB")
EOF

Clean up shards (optional):

Bash

rm -rf ~/sd-models/text_encoders/lumina2_gemma2_2b/

5.4 VAE¶

Bash

ln -sf /home/dars/sd-models/vae/ae.safetensors \
       ~/ComfyUI/models/vae/ae.safetensors

5.5 Verify All Models in Place¶

Bash

ls -lh ~/ComfyUI/models/unet/*.gguf \
       ~/ComfyUI/models/text_encoders/*.safetensors \
       ~/ComfyUI/models/vae/*.safetensors

Expected output:

Text Only

9.8G  ~/ComfyUI/models/text_encoders/gemma2_2b_lumina2.safetensors
5.2G  ~/ComfyUI/models/unet/z_image_turbo-Q5_K_S.gguf  (symlink)
335M  ~/ComfyUI/models/vae/ae.safetensors  (symlink)

---

6. Launch Script¶

Location: ~/start-comfyui.sh¶

Bash

#!/bin/bash
# =============================================================
# ComfyUI Launch Script for AMD BC-250 (ROCm / gfx1013)
# =============================================================

set -euo pipefail

echo "=========================================="
echo " ComfyUI — BC-250 ROCm Launcher"
echo "=========================================="

# --- GPU Health Check ---
if dmesg 2>/dev/null | tail -50 | grep -qi "KIQ fence timeout"; then
    echo "[ABORT] KIQ fence timeout detected in dmesg — reboot required!"
    exit 1
fi
echo "[OK] GPU health check passed"

# --- ROCm Environment for BC-250 (gfx1013 → gfx1010 spoof) ---
export HSA_OVERRIDE_GFX_VERSION=10.1.0
export HSA_ENABLE_SDMA=0
export HIP_VISIBLE_DEVICES=0
export ROCM_PATH=/opt/rocm
export HSA_TOOLS_LIB=""
export HSA_TOOLS_REPORT_LOAD_FAILURE=0
export PATH="/opt/rocm/bin:$PATH"
export LD_LIBRARY_PATH="/opt/rocm/lib"

# --- Unset old workaround variables that destroy performance ---
unset GPU_MAX_HW_QUEUES 2>/dev/null || true
unset HIP_LAUNCH_BLOCKING 2>/dev/null || true
unset GGML_CUDA_ENABLE_UNIFIED_MEMORY 2>/dev/null || true
unset GGML_HIP_HOST_ALLOC 2>/dev/null || true
unset GGML_CUDA_NO_PINNED 2>/dev/null || true
unset GGML_HIP_NO_COARSE_GRAIN 2>/dev/null || true
unset HSA_DISABLE_FRAGMENT_ALLOCATOR 2>/dev/null || true

# --- PyTorch ROCm tuning ---
export PYTORCH_HIP_ALLOC_CONF="expandable_segments:False"

echo "[OK] ROCm environment configured"

# --- Activate venv ---
cd ~/ComfyUI
source venv/bin/activate

# --- Launch ComfyUI ---
echo "[START] Launching ComfyUI on http://0.0.0.0:8188"
echo "=========================================="
python main.py \
    --listen 0.0.0.0 \
    --port 8188 \
    --force-fp32 \
    --lowvram \
    "$@"

Usage¶

Bash

# Foreground (see logs):
bash ~/start-comfyui.sh

# Background with logging:
nohup bash ~/start-comfyui.sh > /tmp/comfyui.log 2>&1 &

# Check if running:
curl -s http://localhost:8188/system_stats | python3 -m json.tool

CLI Flags Explained¶

Flag	Why
--listen 0.0.0.0	Accept connections from any interface (access from other machines)
--port 8188	Default ComfyUI port
--force-fp32	BC-250 gfx1010 has limited FP16 support in PyTorch ROCm; FP32 prevents crashes
--lowvram	Enables aggressive model offloading — essential for 14.7 GB shared RAM

---

7. ComfyUI Workflow — Z-Image Turbo¶

Access the WebUI¶

Open in browser: http://localhost:8188 (or http://<machine-ip>:8188 from another machine)

Pre-made Workflow¶

A ready-to-use workflow is saved at:

Text Only

~/ComfyUI/workflows/z_image_turbo_bc250.json

Load it via: Menu → Load → select z_image_turbo_bc250.json

Manual Node Setup¶

If building the workflow from scratch, create these nodes:

Node 1: UnetLoaderGGUF¶

• Type: UnetLoaderGGUF (from ComfyUI-GGUF custom node, category: bootleg)
• unet_name: z_image_turbo-Q5_K_S.gguf
• Output: MODEL → connect to KSampler's "model" input

Node 2: CLIPLoader¶

• Type: CLIPLoader (built-in, category: advanced/loaders)
• clip_name: gemma2_2b_lumina2.safetensors
• type: lumina2 ← CRITICAL: must be set to lumina2
• Output: CLIP → connect to both CLIP Text Encode nodes

Node 3: CLIP Text Encode (Positive)¶

• Type: CLIPTextEncode
• text: Your prompt (e.g., "a beautiful sunset over the ocean")
• Input: clip ← from CLIPLoader
• Output: CONDITIONING → connect to KSampler's "positive" input

Node 4: CLIP Text Encode (Negative)¶

• Type: CLIPTextEncode
• text: Empty string "" (turbo models work best with empty negative)
• Input: clip ← from CLIPLoader
• Output: CONDITIONING → connect to KSampler's "negative" input

Node 5: Empty Latent Image¶

• Type: EmptyLatentImage
• width: 512
• height: 512
• batch_size: 1
• Output: LATENT → connect to KSampler's "latent_image" input

Node 6: KSampler¶

• Type: KSampler
• seed: Any number (42)
• control_after_generate: fixed (or randomize for variety)
• steps: 8 (turbo — more steps won't improve quality)
• cfg: 3.0 (turbo models use low CFG guidance)
• sampler_name: euler
• scheduler: sgm_uniform
• denoise: 1.0
• Inputs: model, positive, negative, latent_image
• Output: LATENT → connect to VAEDecode

Node 7: VAELoader¶

• Type: VAELoader
• vae_name: ae.safetensors
• Output: VAE → connect to VAEDecode's "vae" input

Node 8: VAE Decode¶

• Type: VAEDecode
• Inputs: samples (from KSampler), vae (from VAELoader)
• Output: IMAGE → connect to SaveImage

Node 9: Save Image¶

• Type: SaveImage
• filename_prefix: ComfyUI
• Input: images ← from VAEDecode
• Output images saved to: ~/ComfyUI/output/

Wiring Summary¶

Text Only

UnetLoaderGGUF ───MODEL──→ KSampler
CLIPLoader ───CLIP──→ CLIPTextEncode (positive) ──CONDITIONING──→ KSampler
CLIPLoader ───CLIP──→ CLIPTextEncode (negative) ──CONDITIONING──→ KSampler
EmptyLatentImage ──LATENT──→ KSampler
KSampler ──LATENT──→ VAEDecode
VAELoader ──VAE──→ VAEDecode
VAEDecode ──IMAGE──→ SaveImage

---

8. BC-250 Specific Tuning¶

Environment Variables (set in launch script)¶

Variable	Value	Why
HSA_OVERRIDE_GFX_VERSION	10.1.0	BC-250 (gfx1013) needs gfx1010 spoof for ROCm
HSA_ENABLE_SDMA	0	SDMA engine has hardware bugs on gfx1013
HIP_VISIBLE_DEVICES	0	Select the BC-250 GPU
ROCM_PATH	/opt/rocm	ROCm installation path
HSA_TOOLS_LIB	""	Disable profiling tools (stability)
HSA_TOOLS_REPORT_LOAD_FAILURE	0	Suppress tool warnings
PYTORCH_HIP_ALLOC_CONF	expandable_segments:False	Prevent memory fragmentation

Variables to NEVER Set¶

These old workarounds destroy performance and must NOT be set:

Variable	Why it's bad
GPU_MAX_HW_QUEUES=1	Serializes all GPU ops to 1 queue
HIP_LAUNCH_BLOCKING=1	Forces synchronous kernel launches
GGML_CUDA_ENABLE_UNIFIED_MEMORY=1	Page fault overhead
GGML_HIP_HOST_ALLOC=1	Zero-copy over PCIe is slow

Memory Considerations¶

• Total available: ~14.7 GB shared system RAM
• Diffusion model (GGUF Q5_K_S): ~5.2 GB
• Text encoder (Gemma 2 2B f32): ~9.8 GB
• VAE: ~335 MB
• Total model footprint: ~15.3 GB — exceeds available RAM
• --lowvram is essential: it offloads models to CPU when not in active use
• Only one component is on GPU at a time during inference

Resolution Recommendations¶

Resolution	Latent Size	Notes
512×512	64×64	Fastest, recommended for testing
768×768	96×96	Good quality, slower
1024×1024	128×128	May OOM on BC-250

---

9. Troubleshooting¶

"KIQ fence timeout" in dmesg → Reboot¶

Bash

sudo dmesg | grep -i "KIQ fence timeout"

If this appears, the GPU is in a bad state. Reboot the machine.

ComfyUI won't start — "No module named torch"¶

Make sure you activated the venv:

Bash

source ~/ComfyUI/venv/bin/activate
python -c "import torch; print(torch.__version__)"

"CLIP type not found" or wrong model type¶

Make sure the CLIPLoader node type is set to lumina2 — NOT stable_diffusion.

OOM (Out of Memory) during generation¶

1. Reduce resolution to 512×512
2. Ensure --lowvram is set
3. Close other programs using RAM
4. Try --use-split-cross-attention flag

Model not showing in dropdown¶

Verify symlinks are not broken:

Bash

ls -la ~/ComfyUI/models/unet/z_image_turbo-Q5_K_S.gguf
ls -la ~/ComfyUI/models/vae/ae.safetensors
ls -la ~/ComfyUI/models/text_encoders/gemma2_2b_lumina2.safetensors

"UnetLoaderGGUF" node not found¶

Ensure ComfyUI-GGUF is installed:

Bash

ls ~/ComfyUI/custom_nodes/ComfyUI-GGUF/
pip list | grep gguf

PyTorch ROCm version mismatch¶

Bash

python -c "import torch; print(torch.version.cuda)"
# Should print: 6.2

---

10. File Inventory¶

Installation Files¶

File	Purpose
~/ComfyUI/	ComfyUI installation directory
~/ComfyUI/venv/	Python 3.11 virtual environment
~/ComfyUI/custom_nodes/ComfyUI-GGUF/	GGUF model loader custom node
~/ComfyUI/workflows/z_image_turbo_bc250.json	Pre-made Z-Image Turbo workflow
~/start-comfyui.sh	Launch script with ROCm env vars

Model Files¶

File	Size	Format
~/ComfyUI/models/unet/z_image_turbo-Q5_K_S.gguf	5.2 GB	Symlink → ~/sd-models/diffusion_models/
~/ComfyUI/models/text_encoders/gemma2_2b_lumina2.safetensors	9.8 GB	Merged from Alpha-VLLM/Lumina-Image-2.0
~/ComfyUI/models/vae/ae.safetensors	335 MB	Symlink → ~/sd-models/vae/

Output¶

File	Purpose
~/ComfyUI/output/	Generated images saved here

Python Packages (key ones)¶

Package	Version
torch	2.5.1+rocm6.2
torchvision	0.20.1+rocm6.2
torchaudio	2.5.1+rocm6.2
pytorch-triton-rocm	3.1.0
transformers	5.2.0
safetensors	0.7.0
gguf	0.18.0
comfyui-frontend-package	1.39.19

---

11. Performance Notes¶

Startup Output (successful launch)¶

Text Only

Total VRAM 14750 MB, total RAM 15205 MB
pytorch version: 2.5.1+rocm6.2
AMD arch: gfx1010
ROCm version: (6, 2)
Forcing FP32
Set vram state to: LOW_VRAM
Device: cuda:0 AMD Radeon Graphics : native
ComfyUI version: 0.15.1
ComfyUI-GGUF: Partial torch compile only, consider updating pytorch

Expected Timing (BC-250, 512×512, 8 steps)¶

Phase	Estimated Time
Model Loading (first run)	30-60s
Text Encoding (Gemma 2 2B)	~2-5s
Sampling (8 steps, Euler)	~60-90s
VAE Decode	~10-15s
Total (first image)	~2-3 min
Total (subsequent)	~1-2 min

Comparison with sdcpp-restapi¶

	ComfyUI + PyTorch	sdcpp-restapi
Frontend	Full node-based GUI	REST API + simple WebUI
Model format	GGUF + safetensors	GGUF only
Memory management	PyTorch (--lowvram)	ggml manual
Flexibility	Full workflow customization	Fixed pipeline
Turbo steps	Configurable per-run	Config-based

---

Appendix: Quick Start Cheatsheet¶

Bash

# 1. Launch ComfyUI
bash ~/start-comfyui.sh

# 2. Open browser
# http://localhost:8188

# 3. Load workflow
# Menu → Load → z_image_turbo_bc250.json

# 4. Click "Queue Prompt" to generate

# 5. Images saved in ~/ComfyUI/output/

---

Document generated: 2026-03-02 | System: CachyOS + AMD BC-250 + ROCm 7.2.0