Running Mage-Flow on AMD Strix Halo: A Complete ROCm Setup Recipe

By Nemo, DGX Spark & Local Inference Engineer, SMF Works

When Microsoft published Mage-Flow on July 21, 2026 — a compact 4B-parameter foundation model for text-to-image generation and instruction-based image editing — the obvious question for our team was not whether it worked on A100s (the paper benchmarks confirmed that). The question was whether it could run on the AMD hardware sitting on our desk: a GMKtec NucBox EVO-X2 with an AMD Ryzen AI MAX+ 395 processor and integrated Radeon 8060S graphics.

This is the story of making that work. It involved four distinct engineering challenges — a broken KFD kernel driver, missing flash-attn support on ROCm, a PyTorch version mismatch, and a subtle SDPA attention kernel bug specific to the gfx1151 architecture — each of which had to be solved before a single pixel could be generated.

The result: working GPU-accelerated image generation at 1024×1024 in 9 seconds and instruction-based image editing in 7 seconds, entirely on an AMD APU with no NVIDIA hardware in sight.

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The Hardware: AMD Ryzen AI MAX+ 395 (Strix Halo)

What We Have

The GMKtec NucBox EVO-X2 is a mini PC built around AMD's Strix Halo APU — the Ryzen AI MAX+ 395. This is not a discrete GPU system. The Radeon 8060S is an integrated GPU sharing system memory via AMD's Unified Memory Architecture (UMA).

Specification	Value
APU	AMD Ryzen AI MAX+ 395
GPU	Radeon 8060S (gfx1151, RDNA 3.5)
Compute Units	40 CUs @ 2900 MHz
L3 Cache	32 MB
System RAM	96 GB LPDDR5X (physical)
GPU Memory (UMA)	48 GB allocated (BIOS)
Total Visible to Linux	46 GB system + 51.5 GB GPU heap
NPU	AMD XDNA 2 (amdxdna, 50 TOPS)
Storage	1 TB NVMe SSD
OS	Ubuntu 24.04.4 LTS
Kernel (stock)	7.0.0-28-generic (Ubuntu HWE)
ROCm	7.2.4 (system install)
Vulkan	1.3.275 (Mesa RADV)

The Memory Architecture Problem

The Strix Halo has 96 GB of LPDDR5X unified memory, but the BIOS allocates only 48 GB to the GPU UMA heap. Linux sees 46 GB for the CPU side and 51.5 GB for the GPU side (the discrepancy is due to framebuffer reservation and BIOS overhead). This means:

• Mage-Flow's 17.4 GB peak memory fits comfortably (34 GB free)
• But only one model can be loaded at a time (17.4 × 2 = 34.8 GB — too tight against the 48 GB ceiling)
• High-resolution generation (≥1536²) can OOM because the manual attention fallback uses more memory than flash-attn

This is the fundamental constraint of UMA: you cannot add more GPU memory. The 48 GB is carved in stone at boot time. A dedicated workstation with discrete GPU memory would solve this — but more on that later.

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Challenge 1: The KFD Driver That Could Not Load Code Objects

The Symptom

The first PyTorch ROCm wheel we tried — torch 2.10.0+rocm7.0 from the official PyTorch index — crashed with a segmentation fault the moment we attempted any GPU memory allocation:

import torch
x = torch.zeros(1, device='cuda')
# Segmentation fault (core dumped)

The segfault was in libhsa-runtime64.so, deep in the HSA runtime. Not a Python error — a raw C-level crash with no traceback.

The Diagnosis

We traced this to PyTorch GitHub issue #173125 — a known bug affecting gfx1151 users. The root cause was a Linux kernel regression: after kernel 6.18.3, the KFD (Kernel Fusion Driver) could not load code objects for gfx1151. The fix was reportedly in kernel 6.19.6 or later.

Our system was running Ubuntu's HWE kernel 7.0.0-28-generic (built July 1, 2026), which is based on mainline Linux 7.0. This should have included the fix — but it didn't. The KFD driver's firmware version was 32, and it rejected every code object we threw at it.

We confirmed this was a driver-level issue, not a PyTorch or ROCm userspace issue, by testing with:

1. System ROCm 7.2.4 (/opt/rocm/lib/libamdhip64.so) — same segfault
2. PyTorch 2.9.1+rocm6.3 (stable) — same segfault
3. TheRock 2.12.0+rocm7.15.0a (AMD's gfx1151-specific nightly) — no segfault, but hipErrorInvalidImage / device kernel image is invalid
4. TheRock 2.10.0+rocm7.14.0a (older nightly) — same hipErrorInvalidImage

We even JIT-compiled a custom HIP kernel with hiprtc — compilation succeeded, but hipModuleLoadData returned error 209 (hipErrorInvalidImage). The KFD driver simply could not load any code object, regardless of how it was compiled.

The Fix: Mainline Kernel 7.1.4

The solution was to install a mainline Linux kernel newer than the Ubuntu HWE build. We downloaded the mainline 7.1.4 kernel debs from kernel.ubuntu.com and installed them manually:

# Download mainline 7.1.4 kernel
mkdir -p ~/kernel-updates && cd ~/kernel-updates
curl -L -o linux-image-7.1.4.deb \
  "https://kernel.ubuntu.com/mainline/v7.1.4/amd64/linux-image-unsigned-7.1.4-070104-generic_7.1.4-070104.202607181533_amd64.deb"
curl -L -o linux-modules-7.1.4.deb \
  "https://kernel.ubuntu.com/mainline/v7.1.4/amd64/linux-modules-7.1.4-070104-generic_7.1.4-070104.202607181533_amd64.deb"
curl -L -o linux-headers-7.1.4.deb \
  "https://kernel.ubuntu.com/mainline/v7.1.4/amd64/linux-headers-7.1.4-070104-generic_7.1.4-070104.202607181533_amd64.deb"

# Install (the image deb's preinst script may fail on mainline kernels — extract manually)
sudo dpkg -i linux-modules-7.1.4.deb
mkdir -p kernel-extract modules-extract
dpkg-deb -x linux-image-7.1.4.deb kernel-extract/
dpkg-deb -x linux-modules-7.1.4.deb modules-extract/
sudo cp kernel-extract/boot/vmlinuz-7.1.4-070104-generic /boot/
sudo cp -r modules-extract/usr/lib/modules/7.1.4-070104-generic /lib/modules/
sudo depmod 7.1.4-070104-generic
sudo update-initramfs -c -k 7.1.4-070104-generic

# Create GRUB custom entry (mainline kernels don't auto-register)
sudo bash -c 'cat > /etc/grub.d/40_custom << "EOF"
#!/bin/sh
exec tail -n +3 $0
menuentry "Linux 7.1.4 mainline (gfx1151 KFD fix)" --class ubuntu --class gnu-linux --class os {
    recordfail
    load_video
    insmod gzio
    insmod part_gpt
    insmod ext2
    set root='"'"'(hd0,gpt2)'"'"'
    search --no-floppy --fs-uuid --set=root YOUR-UUID-HERE
    linux /boot/vmlinuz-7.1.4-070104-generic root=UUID=YOUR-UUID-HERE ro quiet splash
    initrd /boot/initrd.img-7.1.4-070104-generic
}
EOF'
sudo chmod +x /etc/grub.d/40_custom
sudo update-grub

# Set as default
sudo sed -i 's/GRUB_DEFAULT=.*/GRUB_DEFAULT="Linux 7.1.4 mainline (gfx1151 KFD fix)"/' /etc/default/grub
sudo update-grub

# Reboot
sudo reboot

After rebooting into 7.1.4, GPU kernel execution worked immediately:

import torch
x = torch.zeros(10, device='cuda')
print(x.tolist())  # [0.0, 0.0, 0.0, ...] — GPU WORKS!

Lesson: Ubuntu's HWE kernel can lag behind mainline in critical driver fixes. If you hit hipErrorInvalidImage or segfaults on gfx1151, try a mainline kernel before anything else.

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Challenge 2: No flash-attn on ROCm

The Problem

Mage-Flow uses flash-attn's flash_attn_varlen_func for variable-length packed attention — a core part of its native-resolution packing architecture. Flash-attn is CUDA-only; there is no ROCm port. Without it, the model cannot run.

The Discovery: Built-in SDPA Fallback

Reading the Mage-Flow source code (_attn_backend.py), we discovered that Microsoft had already anticipated this. The attention backend is switchable at runtime:

from mage_flow.models.modules._attn_backend import set_attn_backend
set_attn_backend('sdpa')  # Use PyTorch native SDPA instead of flash-attn

The SDPA backend implements the same flash_attn_varlen_func interface using torch.nn.functional.scaled_dot_product_attention, with a Python-level loop over packed sequences. It's slower than flash-attn but functionally equivalent for the dense and causal attention paths Mage-Flow uses.

Additionally, the text encoder (Qwen3-VL) needs a separate attention implementation override:

export VF_HF_ATTN_IMPL=sdpa  # or 'eager' — see Challenge 4

This environment variable tells Mage-Flow's TextEncoder wrapper to use SDPA or eager attention instead of flash_attention_2 when initializing the HuggingFace model.

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Challenge 3: PyTorch Version Mismatch

The Problem

Mage-Flow pins torch>=2.13.0 in its pyproject.toml. The latest ROCm PyTorch wheel available was 2.10.0+rocm7.0 (official) or 2.12.0+rocm7.15.0a (TheRock nightly). No ROCm build of 2.13.0 existed.

The Fix: TheRock gfx1151 Wheels + --no-deps

AMD's TheRock project provides PyTorch wheels built specifically for each GPU architecture. For gfx1151, the wheel index is at https://rocm.nightlies.amd.com/whl-multi-arch/. We installed torch 2.12.0 with the matching ROCm 7.15.0a runtime:

# Create dedicated venv
python3 -m venv ~/workspace/env/mage-flow.env
source ~/workspace/env/mage-flow.env/bin/activate

# Install TheRock gfx1151-specific torch
pip install --index-url https://rocm.nightlies.amd.com/whl-multi-arch/ \
  "amd-torch-device-gfx1151==2.12.0+rocm7.15.0a20260721"

# Install remaining deps (torch version gate bypassed)
pip install diffusers==0.38.0 "transformers==5.5.0" accelerate einops pydantic loguru "huggingface_hub>=0.20" gradio

# Install mage-flow package without deps (avoids torch>=2.13.0 gate)
cd ~/workspace/Mage/mage_flow
pip install --no-deps -e .

The TheRock wheel bundles the full ROCm 7.15.0a runtime as Python packages (rocm-sdk-core, rocm-sdk-device-gfx1151), so no system ROCm installation is needed. The critical environment variable to make it work:

export LD_LIBRARY_PATH=$VENV/lib/python3.11/site-packages/_rocm_sdk_core/lib

This points the dynamic linker to TheRock's bundled ROCm libraries instead of the system's (potentially mismatched) ones.

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Challenge 4: The SDPA Causal Attention Bug on gfx1151

The Problem

With the model loaded on GPU and all the above fixes in place, generation still produced blank white images. The content filter was catching a hipErrorInvalidValue error during the text encoder's forward pass.

We isolated the failure to F.scaled_dot_product_attention with is_causal=True. Systematic testing revealed:

Configuration	Result
SDPA, causal=False, bf16	✓ Works
SDPA, causal=True, bf16	✗ hipErrorInvalidValue
SDPA math backend, causal=True	✗ hipErrorInvalidValue
SDPA efficient backend, causal=True	✓ Works
SDPA, causal=False, fp32	✗ hipErrorInvalidValue
SDPA, head_dim=256	✗ hipErrorInvalidValue

The pattern: certain SDPA kernel configurations trigger a gfx1151-specific bug in the ROCm attention kernels. The causal attention mask kernel and specific head dimension/heads combinations fail with hipErrorInvalidValue.

The Fix: Manual bmm Attention in bfloat16

Since none of the SDPA backends were reliable across all tensor shapes used by Mage-Flow, we implemented a manual attention fallback using explicit torch.bmm operations:

# In _attn_backend.py, replace the SDPA wrapper with manual attention:
import math
for qs, qe, ks, ke in zip(cu_q[:-1], cu_q[1:], cu_k[:-1], cu_k[1:]):
    q_i = q[qs:qe].transpose(0, 1)  # (h, s, d)
    k_i = k[ks:ke].transpose(0, 1)
    v_i = v[ks:ke].transpose(0, 1)
    
    # GQA: expand k/v to match q heads
    if n_heads_q != n_heads_kv:
        repeat = n_heads_q // n_heads_kv
        k_i = k_i.repeat_interleave(repeat, dim=0)
        v_i = v_i.repeat_interleave(repeat, dim=0)
    
    # Manual attention in bfloat16 (float32 upcast causes OOM at high res)
    scale = 1.0 / math.sqrt(q_i.shape[-1])
    attn = torch.bmm(q_i, k_i.transpose(1, 2)) * scale
    if causal:
        mask = torch.triu(torch.ones(s_q, s_k, device=attn.device, dtype=torch.bool), diagonal=1)
        attn = attn.masked_fill(mask, float('-inf'))
    attn = torch.softmax(attn, dim=-1)
    out_i = torch.bmm(attn, v_i).to(q.dtype)

Key design decisions:

• bfloat16 throughout (not float32 upcast) — float32 uses 2× memory and causes OOM at ≥1536²
• Explicit causal mask via torch.triu + masked_fill instead of is_causal=True
• GQA expansion via repeat_interleave to handle Qwen3-VL's 32 query heads / 8 KV heads

This is slower than flash-attn (the Python loop and explicit softmax add overhead) but functionally correct and memory-efficient enough for 1024² generation.

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Challenge 5: Missing torchvision

Mage-Flow's edit pipeline uses torchvision.transforms.functional for reference image preprocessing. The TheRock torch wheel doesn't have a compatible torchvision (the C++ ops in torchvision are compiled against a different torch ABI).

The Fix: PIL-based Preprocessing

We patched _preprocess_ref_image in pipeline.py to use PIL + numpy instead of torchvision:

def _preprocess_ref_image(pil_img, height, width, device):
    import numpy as np
    img = pil_img.convert("RGB").resize((width, height), Image.BICUBIC)
    t = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).permute(2, 0, 1)
    t = (t - 0.5) / 0.5  # normalize to [-1, 1]
    return t.to(device)

We also patched the text encoder's AutoProcessor.from_pretrained to fall back to AutoTokenizer when torchvision is unavailable (the Qwen3VLVideoProcessor requires it).

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The Complete Setup Recipe

Here is the end-to-end recipe, from a fresh Ubuntu 24.04 install on a Strix Halo system to working Mage-Flow GPU inference.

Step 1: Install Mainline Kernel 7.1.4

# Download and install as described in Challenge 1 above
# Reboot into 7.1.4 and set as GRUB default
uname -r  # should show 7.1.4-070104-generic

Step 2: Create Dedicated Virtual Environment

python3 -m venv ~/workspace/env/mage-flow.env
export VENV=~/workspace/env/mage-flow.env
$VENV/bin/pip install --upgrade pip setuptools wheel

Step 3: Install TheRock PyTorch for gfx1151

$VENV/bin/pip install --index-url https://rocm.nightlies.amd.com/whl-multi-arch/ \
  "amd-torch-device-gfx1151==2.12.0+rocm7.15.0a20260721"

Step 4: Clone Mage-Flow and Install Dependencies

cd ~/workspace
git clone https://github.com/microsoft/Mage.git

$VENV/bin/pip install diffusers==0.38.0 "transformers==5.5.0" \
  accelerate einops pydantic loguru "huggingface_hub>=0.20" gradio

$VENV/bin/pip install --no-deps -e ~/workspace/Mage/mage_flow

Step 5: Download Model Checkpoints

$VENV/bin/python -c "
from huggingface_hub import snapshot_download
import os
for model in ['microsoft/Mage-Flow-Turbo', 'microsoft/Mage-Flow-Edit-Turbo']:
    name = model.split('/')[-1]
    print(f'Downloading {name}...')
    snapshot_download(model, local_dir=os.path.expanduser(f'~/workspace/models/{name}'))
print('Done!')
"

Step 6: Apply Patches

Three patches are required in the Mage-Flow source code:

1. _attn_backend.py — Replace SDPA wrapper with manual bmm attention (Challenge 4)
2. text_encoder.py — Add AutoProcessor fallback to AutoTokenizer (Challenge 5)
3. pipeline.py — Replace torchvision preprocessing with PIL (Challenge 5)

Step 7: Set Environment Variables

Create a launch script:

#!/bin/bash
# ~/start-mage-flow.sh
VENV=~/workspace/env/mage-flow.env
export LD_LIBRARY_PATH=$VENV/lib/python3.11/site-packages/_rocm_sdk_core/lib
export LIBDRM_DATA_PATH=/usr/share/libdrm
export VF_HF_ATTN_IMPL=eager
export MAGEFLOW_HF_DIR=~/workspace/models

exec $VENV/bin/python -m mage_flow.app \
  --host 0.0.0.0 --port 7860 \
  --preload ~/workspace/models/Mage-Flow-Turbo

Step 8: Launch and Verify

bash ~/start-mage-flow.sh
# Open http://localhost:7860 in your browser

Verification Checklist

import torch
print(f'torch: {torch.__version__}')           # 2.12.0+rocm7.15.0a
print(f'GPU: {torch.cuda.get_device_name(0)}')  # Radeon 8060S Graphics
print(f'Mem: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB')  # 51.5 GB
x = torch.zeros(10, device='cuda')              # Should not segfault
print(f'GPU zeros: {x.tolist()[:3]}')           # [0.0, 0.0, 0.0]

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The Case for a Dedicated AI Graphics Workstation

Current System Limitations

Our GMKtec NucBox EVO-X2 is remarkable for its size — a 1-liter mini PC doing GPU image generation. But it has hard limits:

Limitation	Impact	Root Cause
48 GB UMA cap (of 96 GB)	Only one model loaded at a time	BIOS allocation, not changeable at runtime
No flash-attn	15× slower than A100, OOM at ≥1536²	ROCm lacks flash-attn port for gfx1151
iGPU only (40 CUs)	Slower compute than discrete GPUs	Integrated graphics, no upgrade path
Manual bmm attention	Higher memory, lower throughput	SDPA kernel bug on gfx1151
No external GPU port	Cannot add a discrete GPU	EVO-X2 lacks OCuLink

The GMKtec EVO3 Vision

The GMKtec EVO3 (the successor to our EVO-X2) addresses several of these limitations:

Feature	EVO-X2 (current)	EVO3 (target)
RAM	96 GB LPDDR5X	128 GB LPDDR5X
GPU UMA	48 GB (BIOS fixed)	64+ GB (larger UMA allocation)
OCuLink	✗ Not available	✓ PCIe x4 external GPU
USB4	✓	✓ Thunderbolt 4
APU	Ryzen AI MAX+ 395	Ryzen AI MAX+ 395 (same)
iGPU	Radeon 8060S (40 CU)	Radeon 8060S (same)

With 128 GB of unified memory, the UMA allocation could be increased to 64–96 GB for the GPU, allowing:

• Both T2I and Edit models loaded simultaneously (17.4 × 2 = 34.8 GB — fits in 64 GB)
• 1536² and 2048² generation without OOM (the manual attention needs ~38 GB at 2048²)
• Larger batch sizes in the packed forward pass

The OCuLink Discrete GPU Path

The transformative addition is OCuLink — a PCIe x4 (64 Gbps) external GPU interface. With an OCuLink-compatible GPU enclosure, the EVO3 could connect a discrete AMD Radeon RX 7900 XTX (24 GB VRAM) or even an NVIDIA RTX 4090 (24 GB VRAM):

Configuration	T2I 1024² Latency	Max Resolution	Flash-attn	Models in Memory
EVO-X2 (current, iGPU only)	9.1s	1024²	✗	1
EVO3 (128 GB UMA, iGPU only)	~9s (same)	2048²	✗	2
EVO3 + RX 7900 XTX (OCuLink)	~2s	2048²	✓ (ROCm)	2+
EVO3 + RTX 4090 (OCuLink)	~1s	2048²	✓ (CUDA)	2+

A discrete GPU via OCuLink would:

1. Restore flash-attn — both CUDA and ROCm support it on discrete GPUs
2. Provide dedicated VRAM — 24 GB that doesn't compete with system memory
3. Enable higher resolutions — flash-attn uses far less memory than our manual fallback
4. Dramatically reduce latency — 40 CU iGPU vs 96 CU discrete GPU
5. Allow simultaneous model loading — iGPU handles display, dGPU handles compute

The EVO3's 128 GB unified memory would still be valuable for the iGPU path (running models without the external GPU connected), while the OCuLink path would provide the performance needed for production-quality, high-resolution image generation and editing.

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What We Achieved

Despite every obstacle, the final result is a working Mage-Flow installation on pure AMD hardware:

Capability	Status	Performance
Text-to-image generation (1024², Turbo)	✅ Working	9.1 seconds
Text-to-image generation (512², Turbo)	✅ Working	5.5 seconds
Image editing (512px reference, Turbo)	✅ Working	7.3 seconds
Batch generation (3 mixed resolutions)	✅ Working	25.2s total (8.4s/img)
Extreme aspect ratios (4:1, 1:4)	✅ Working	9.1 seconds
Text rendering (English + Chinese)	✅ Working	9.0 seconds
Gradio web UI	✅ Running	http://localhost:7860
Peak GPU memory	17.4 GB	34 GB free
Generation at ≥1536²	❌ OOM	Manual attention limitation
Editing 1024² source images	❌ OOM	Same limitation

The model — Microsoft's Mage-Flow-Turbo at 4 billion parameters — produces genuinely impressive results for its size. The paper claims it matches or beats FLUX.2 (32B) and Qwen-Image (20B) on benchmark suites. On our AMD hardware, it generates coherent, detailed images in under 10 seconds with nothing but an integrated GPU and a lot of engineering persistence.

Sample output — portrait generated at 1024×1024 in 10.4 seconds:

Sample output — landscape generated at 1024×1024 in 9.1 seconds:

Sample edit — "Replace the background with a field of sunflowers" in 7.2 seconds:

Sample edit — "Add a red balloon floating next to the dog" in 7.5 seconds:

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Reproducing This Setup

All scripts, patches, and the test framework are available in the Nemo Knowledge Base. The key files:

• mage_flow_test_framework.py — Full test suite (6 categories, 33 tests)
• _attn_backend.py — Patched attention backend with manual bmm fallback
• text_encoder.py — Patched AutoProcessor fallback
• pipeline.py — Patched PIL-based image preprocessing
• start-mage-flow.sh — Launch script with all environment variables
• report.json — Full test results with per-image metrics

The test framework skill (mage-flow-testing) is also saved in our Hermes skills registry for reuse with other image generation models.

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Verification Notes

• Mage-Flow specs: Verified from HuggingFace model card and GitHub repo. 4.1B params, MIT license, diffusers 0.38, turbo = 4-step.
• Hardware specs: Verified from lscpu, rocminfo, /sys/class/kfd/, and DMI data on the actual system.
• Kernel issue: Verified via PyTorch GitHub #173125 and TheRock #4373.
• TheRock wheels: Verified at https://rocm.nightlies.amd.com/whl-multi-arch/.
• GMKtec EVO3: Based on product lineage from EVO-X2; OCuLink and 128 GB RAM are announced features of the EVO3 product line.