CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

DGX-Pixels is an AI-powered pixel art generation stack optimized for the NVIDIA DGX-Spark hardware, designed to generate game sprites and assets for Bevy game engine projects.

Current Status: Documentation Phase Complete ✅ - Implementation not yet started

The project is in its research and planning phase. All documentation is complete, but no code has been written yet. This is intentional - the comprehensive research and architecture proposals must be reviewed and a specific architecture path selected before implementation begins.

Core Constraints

These are non-negotiable requirements that must be respected in all implementations:

Hardware: Must run on NVIDIA DGX-Spark (GB10 Grace Blackwell Superchip, single GPU, 128GB unified memory, 1000 TOPS, ARM CPU)
- VERIFIED: See
```
docs/hardware.md
```
  for actual hardware specifications
- IMPORTANT: This is NOT a multi-GPU DGX B200 system (see
```
docs/adr/0001-dgx-spark-not-b200.md
```
  )
Open Source Only: All tools, libraries, and models must be open source (no proprietary APIs or closed models)
Bevy Integration: Primary target is Bevy game engine with MCP server integration
Technology Stack: Rust TUI (ratatui) + Python AI Backend + Stable Diffusion XL + LoRA fine-tuning + ComfyUI + ZeroMQ IPC

Documentation Structure

The

docs/

directory contains comprehensive research and planning:

Core Documentation

01-research-findings.md: Deep research on AI models, DGX-Spark capabilities, Bevy integration, and tools
02-architecture-proposals.md: Four complete architecture proposals (Rapid/Balanced/Rust+Python/Advanced) with timelines and trade-offs
03-technology-deep-dive.md: Technical details on SDXL, LoRA training, ComfyUI, PyTorch optimizations
04-bevy-integration.md: Complete integration guide for Bevy asset pipeline and MCP
05-training-roadmap.md: 12-week training strategy for custom LoRA models
06-implementation-plan.md: Step-by-step implementation guides for architecture paths

Rust + Python Stack Documentation

07-rust-python-architecture.md: Hybrid Rust TUI + Python backend design with ZeroMQ IPC patterns
08-tui-design.md: Complete TUI mockups, workflows, and side-by-side model comparison feature
11-playbook-contribution.md: Proposal for contributing to dgx-spark-playbooks repository

Operations & Project Management (NEW)

hardware.md: Verified DGX-Spark GB10 hardware specifications, topology, and performance characteristics
metrics.md: Performance, quality, and observability metrics framework (adapted for single-GPU)
adr/0001-dgx-spark-not-b200.md: Architecture Decision Record explaining hardware differences
docs/ROADMAP.md: Milestone-based development roadmap (M0-M5)
docs/rfds/gpt5-dgx-pixels.md: External review feedback (note: assumes DGX B200, not applicable to our GB10)

Critical: Read relevant documentation before implementing any component. The research phase identified best practices, pitfalls, and optimal approaches.

Hardware Context: The system runs on DGX-Spark GB10 (single GPU, unified memory), NOT a multi-GPU DGX B200. This changes many architectural decisions. Always consult

docs/hardware.md

and

docs/adr/0001-dgx-spark-not-b200.md

when making hardware-related decisions.

Architecture Decision Required

Before writing any code, one of four architecture proposals must be selected:

Proposal 1: Rapid (1-2 weeks) - Automatic1111 + Simple CLI + Manual Bevy integration
- Use for: Quick prototypes, validation, solo developers
- Trade-offs: No training, manual workflows, limited scalability
Proposal 2: Balanced (4-6 weeks) - ComfyUI + FastAPI + MCP + LoRA Training
- Use for: Small studios (2-10 devs), production projects
- Trade-offs: Medium complexity, requires setup investment

2B. Proposal 2B: Rust TUI + Python (5-6 weeks) - ratatui TUI + ZeroMQ + Python Worker + ComfyUI [NEW RECOMMENDED]

Use for: Developers wanting fast, responsive UI with side-by-side model comparison
Key features: 60+ FPS TUI, <1ms IPC, Sixel image preview, compare pre-trained vs custom models
Trade-offs: Requires Rust knowledge, slightly longer initial setup than Proposal 2

Proposal 3: Advanced (8-12 weeks) - Full microservices + Kubernetes + Web UI + MLOps
- Use for: Large studios (50+ devs), multiple projects
- Trade-offs: High complexity, significant maintenance overhead

Recommendation: Proposal 2B (Rust TUI + Python) offers the best balance of performance, developer experience, and unique features like side-by-side model comparison. This architecture leverages dgx-spark-playbooks and provides a foundation for contributing back to the ecosystem.

See

docs/02-architecture-proposals.md

and

docs/07-rust-python-architecture.md

for detailed comparison matrices and decision criteria.

Key Technical Decisions

These decisions were made after extensive research and should not be changed without strong justification:

Model Architecture

Base Model: Stable Diffusion XL 1.0 (NOT SD 1.5 - SDXL offers 3x larger UNet and better quality)
Fine-tuning Method: LoRA (NOT full fine-tuning - LoRA is faster, uses less memory, produces smaller files)
Training Framework: Kohya_ss or Diffusers (both support DGX-Spark optimizations)

Inference Engine

Balanced/Advanced: ComfyUI (2x faster than A1111, better for automation)
Rapid: Automatic1111 (faster setup, good for prototyping)

Integration Layer

Protocol: Model Context Protocol (MCP) for Bevy communication
Bevy Library: bevy_brp_mcp (enables AI assistants to control Bevy apps)
API Framework: FastAPI (modern, async, auto-docs) for Proposal 2
IPC: ZeroMQ (REQ-REP + PUB-SUB patterns, <1ms latency) for Proposal 2B

Rust + Python Architecture (Proposal 2B)

Frontend: Rust with ratatui TUI framework (60+ FPS rendering, Sixel image preview)
Backend: Python worker with ZeroMQ server for job management
Communication: ZeroMQ with MsgPack serialization
Side-by-Side Comparison: Unique feature allowing comparison of pre-trained vs custom LoRA models simultaneously
Playbook Integration: Leverages dgx-spark-playbooks ComfyUI setup

Hardware Optimization

Enable mixed precision training (FP16/FP4)
Use xformers memory-efficient attention
Leverage Tensor Cores for matrix operations
Load multiple models in 128GB unified memory
Unified Memory: Exploit zero-copy CPU↔GPU transfers (no cudaMemcpy overhead)
Single GPU Focus: No multi-GPU scaling complexity, simpler deployment
ARM Compatibility: Ensure all dependencies support ARM64 architecture

Implementation Guidelines

When Starting Implementation

Select architecture proposal - Don't mix approaches, commit to one path
Follow implementation plan - Use the step-by-step guide in
```
docs/06-implementation-plan.md
```
Read technology deep-dive - Understand SDXL, LoRA, ComfyUI before coding (see
```
docs/03-technology-deep-dive.md
```
)
Respect training roadmap - Custom models are essential for quality, follow the 12-week plan

Project Structure (To Be Created)

For Proposal 2B (Rust + Python):

dgx-pixels/
├── rust/             # Rust TUI application
│   ├── src/
│   │   ├── main.rs        # TUI entry point
│   │   ├── ui/            # ratatui UI components
│   │   ├── zmq_client.rs  # ZeroMQ communication
│   │   └── image_preview.rs # Sixel rendering
│   └── Cargo.toml
├── python/           # Python backend worker
│   ├── workers/
│   │   ├── generation_worker.py  # ZMQ server + ComfyUI client
│   │   └── zmq_server.py         # Job queue management
│   ├── requirements.txt
│   └── pyproject.toml
├── workflows/        # ComfyUI workflow JSON templates
├── models/           # Model storage (use Git LFS)
│   ├── checkpoints/  # Base SDXL models
│   ├── loras/        # Trained LoRAs
│   └── configs/      # Model metadata
└── examples/         # Example Bevy integrations

For Proposal 2 (Python only):

dgx-pixels/
├── src/
│   ├── api/          # FastAPI orchestration layer
│   ├── cli/          # Command-line tools
│   ├── training/     # LoRA training scripts
│   └── processing/   # Post-processing pipeline
├── workflows/        # ComfyUI workflow JSON templates
├── models/           # Model storage
└── examples/         # Example Bevy integrations

Critical Implementation Notes

LoRA Training:

Dataset: 50-100 images minimum for style training
Resolution: 1024x1024 for SDXL (not 512x512)
Training time: 2-4 hours on DGX-Spark
Don't skip training - pre-trained models won't match game art style

ComfyUI Workflows:

Save workflows as JSON templates with placeholder prompts
Create reusable workflows for: single sprite, animation frames, tile sets, batch generation
Version control workflows alongside code

MCP Integration:

Use FastMCP library for Python MCP server
bevy_brp_mcp for Bevy side
Test MCP connection before building higher-level features

Performance Targets (from research):

Inference: 3-5 seconds per 1024x1024 sprite
Batch generation: 20-30 sprites per minute
LoRA training: 2-4 hours per model (50 images, 3000 steps)
TUI rendering: 60+ FPS (Proposal 2B)
ZeroMQ IPC latency: <1ms (Proposal 2B)

Side-by-Side Model Comparison (Proposal 2B):

Generate with multiple models (pre-trained + custom LoRAs) simultaneously
Display results side-by-side in TUI for visual comparison
Track user preferences (which model produced better results)
Use comparison data to inform training improvements
Essential for validating that custom LoRA training improves quality

Bevy Integration Patterns

Two integration approaches are documented:

Manual: Generate → Review → Copy to
```
assets/
```
→ Reference in code
Automated (MCP): Generate → Auto-deploy via MCP → Hot reload in game

For MCP integration:

Bevy must have
```
bevy_brp_mcp
```
plugin enabled
Assets must follow Bevy's
```
assets/
```
directory structure

Use relative paths:

asset_server.load("sprites/character.png")

Enable hot reloading for development:

AssetPlugin { watch_for_changes_override: Some(true) }

See

docs/04-bevy-integration.md

for complete patterns and code examples.

Common Pitfalls (From Research)

Don't use SD 1.5 - SDXL is significantly better for pixel art
Don't skip LoRA training - Pre-trained models lack style consistency
Don't use blur/smooth upscaling - Use nearest-neighbor for pixel-perfect scaling
Don't ignore color quantization - Reduce to optimal palette in post-processing
Don't load models with FP32 - Use FP16 or FP4 to leverage Tensor Cores
Don't create absolute asset paths in Bevy - Use relative to
```
assets/
```
directory
Don't assume multi-GPU scaling - This is a single-GPU system, focus on batch optimization instead
Don't ignore ARM compatibility - Verify all dependencies support ARM64 architecture
Don't waste unified memory - Exploit zero-copy transfers, avoid unnecessary cudaMemcpy calls

Development Workflow

Agent Workflow (Automated)

When agents implement workstreams, they follow this workflow:

Create Branch:

just branch WS-XX

gh-create-branch "wsXX-name"

Implement Changes: Follow TDD (tests first!)
Run Quality Checks:
```
just ci
```
(fmt, lint, test)
Create PR:
```
just pr "Title"
```
or
```
gh-create-pr "Title"
```
Rebase onto Main:
```
gh-rebase-main
```
(before merge)
Auto-merge:
```
gh-auto-merge --merge-method squash
```
(after CI passes)

Manual Workflow (Human Developers)

See

CONTRIBUTING.md

for detailed manual workflow guidelines.

Project Commands (justfile)

The project uses

just

for task automation. Key commands:

# Setup
just init              # Initialize project (first time)
just validate-gpu      # Verify DGX-Spark hardware

# Development
just tui               # Run Rust TUI (debug)
just backend           # Start Python backend worker
just comfyui           # Start ComfyUI server

# Testing
just test              # Run all tests
just test-coverage     # Run tests with coverage
just ci                # Run all CI checks (fmt, lint, test)

# Code Quality
just fmt               # Format Rust code
just lint              # Run Rust clippy
just fmt-python        # Format Python code

# Models
just models-list       # List available models
just download-model    # Download SDXL base model
just train-lora DATASET  # Train LoRA on dataset

# Monitoring
just gpu-status        # Show GPU stats
just gpu-watch         # Monitor GPU (live)
just hw-info           # Show all hardware info

# Git
just status            # Show git status
just branch WS-XX      # Create branch for workstream
just pr "Title"        # Create pull request
just rebase            # Rebase onto main

# Documentation
just docs              # Generate and open Rust docs
just docs-serve        # Serve docs locally

# Full list
just --list            # Show all available commands

Nushell Scripts

The project uses nushell for automation scripts:

Location:

scripts/nu/

Modules:

```
config.nu
```
- Project config, logging, utilities
```
modules/comfyui.nu
```
- ComfyUI API wrapper
```
modules/dgx.nu
```
- DGX-Spark hardware utilities
```
modules/github.nu
```
- GitHub automation (PR, branch, merge)

Usage:

# Load config
use scripts/nu/config.nu *

# Check hardware
use scripts/nu/modules/dgx.nu *
dgx-validate-hardware
dgx-gpu-stats

# GitHub automation
use scripts/nu/modules/github.nu *
gh-create-branch "feature/new-tui"
gh-create-pr "Add new TUI feature" --draft
gh-auto-merge --merge-method squash

# ComfyUI integration
use scripts/nu/modules/comfyui.nu *
comfyui-health-check
comfyui-generate (open workflows/sprite-gen.json)

Testing Strategy (To Be Implemented)

When building the system:

Model Quality Tests: Generate from standard prompts, compare to references
Integration Tests: End-to-end generation → deployment → Bevy loading
Performance Tests: Verify 3-5s inference, 20-30 sprites/min batch
Training Tests: Verify LoRA training completes and improves quality

Critical Files to Understand

Before implementing any component:

HARDWARE FIRST: Read
```
docs/hardware.md
```
and
```
docs/adr/0001-dgx-spark-not-b200.md
```
to understand single-GPU unified memory architecture
Roadmap: Read
```
docs/ROADMAP.md
```
for milestone-based development plan (M0-M5)
Metrics: Read
```
docs/metrics.md
```
for performance targets and benchmarking strategy
Architecture decision: Read
```
docs/02-architecture-proposals.md
```
§ Comparison Matrix and Proposal 2B
SDXL + LoRA: Read
```
docs/03-technology-deep-dive.md
```
§ Stable Diffusion XL and LoRA sections
ComfyUI: Read
```
docs/03-technology-deep-dive.md
```
§ ComfyUI section
Bevy Assets: Read
```
docs/04-bevy-integration.md
```
§ Asset System Basics
Training: Read
```
docs/05-training-roadmap.md
```
§ Phase 2 before training first model

For Proposal 2B (Rust + Python) - RECOMMENDED:

Architecture: Read
```
docs/07-rust-python-architecture.md
```
§ ZeroMQ Communication Patterns
TUI Design: Read
```
docs/08-tui-design.md
```
§ Screen Layouts and Side-by-Side Comparison
Playbook Integration: Read
```
docs/11-playbook-contribution.md
```
§ Installation Steps

Next Steps (For First Implementation)

1. Review Documentation

Start with the orchestration summary:

cat docs/orchestration/project-summary.md

Key documents:

Architecture:
```
docs/02-architecture-proposals.md
```
(choose Proposal 2B recommended)
Hardware:
```
docs/hardware.md
```
(understand GB10 unified memory)
Roadmap:
```
docs/ROADMAP.md
```
(M0-M5 milestones)
Orchestration:
```
docs/orchestration/meta-orchestrator.md
```
(coordination strategy)
Workstreams:
```
docs/orchestration/workstream-plan.md
```
(all 18 workstreams)

2. Initialize Project

# Clone and setup
git clone https://github.com/raibid-labs/dgx-pixels.git
cd dgx-pixels

# Initialize project
just init

# Validate hardware
just validate-gpu

# View hardware info
just hw-info

3. Start with Foundation Orchestrator (M0)

The project uses orchestrated workstreams. Start with Foundation:

# Review Foundation Orchestrator
cat docs/orchestration/orchestrators/foundation.md

# Review first workstream (WS-01)
cat docs/orchestration/workstreams/ws01-hardware-baselines/README.md

# Create branch for WS-01
just branch WS-01

# Implement following the workstream spec
# (See CONTRIBUTING.md for detailed workflow)

4. Follow Orchestration Plan

After M0 completes, proceed through:

M1: Model Orchestrator (ComfyUI, SDXL optimization)
M2: Interface Orchestrator (Rust TUI, ZeroMQ, backend)
M3: Model Orchestrator (LoRA training)
M4: Integration Orchestrator (Bevy, MCP)
M5: Integration Orchestrator (observability, deployment)

See

docs/orchestration/meta-orchestrator.md

for coordination details.

Do not skip steps or mix architecture proposals - the plans are sequential and architecture-specific.

Repository Context

Hardware: This will run on a specific NVIDIA DGX-Spark - not generic cloud GPUs
Target Users: Game developers using Bevy engine (Rust-based)
Use Case: Rapid pixel art sprite generation for game prototyping and production
Unique Value: Open-source, optimized for specific hardware, direct game engine integration

The research phase identified that no existing solution combines all these requirements, which is why this project exists.

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

DGX-Pixels is an AI-powered pixel art generation stack optimized for the NVIDIA DGX-Spark hardware, designed to generate game sprites and assets for Bevy game engine projects.

Current Status: Documentation Phase Complete ✅ - Implementation not yet started

Core Constraints

These are non-negotiable requirements that must be respected in all implementations:

Hardware: Must run on NVIDIA DGX-Spark (GB10 Grace Blackwell Superchip, single GPU, 128GB unified memory, 1000 TOPS, ARM CPU)
- VERIFIED: See
```
docs/hardware.md
```
  for actual hardware specifications
- IMPORTANT: This is NOT a multi-GPU DGX B200 system (see
```
docs/adr/0001-dgx-spark-not-b200.md
```
  )
Open Source Only: All tools, libraries, and models must be open source (no proprietary APIs or closed models)
Bevy Integration: Primary target is Bevy game engine with MCP server integration
Technology Stack: Rust TUI (ratatui) + Python AI Backend + Stable Diffusion XL + LoRA fine-tuning + ComfyUI + ZeroMQ IPC

Documentation Structure

The

docs/

directory contains comprehensive research and planning:

Core Documentation

01-research-findings.md: Deep research on AI models, DGX-Spark capabilities, Bevy integration, and tools
02-architecture-proposals.md: Four complete architecture proposals (Rapid/Balanced/Rust+Python/Advanced) with timelines and trade-offs
03-technology-deep-dive.md: Technical details on SDXL, LoRA training, ComfyUI, PyTorch optimizations
04-bevy-integration.md: Complete integration guide for Bevy asset pipeline and MCP
05-training-roadmap.md: 12-week training strategy for custom LoRA models
06-implementation-plan.md: Step-by-step implementation guides for architecture paths

Rust + Python Stack Documentation

07-rust-python-architecture.md: Hybrid Rust TUI + Python backend design with ZeroMQ IPC patterns
08-tui-design.md: Complete TUI mockups, workflows, and side-by-side model comparison feature
11-playbook-contribution.md: Proposal for contributing to dgx-spark-playbooks repository

Operations & Project Management (NEW)

hardware.md: Verified DGX-Spark GB10 hardware specifications, topology, and performance characteristics
metrics.md: Performance, quality, and observability metrics framework (adapted for single-GPU)
adr/0001-dgx-spark-not-b200.md: Architecture Decision Record explaining hardware differences
docs/ROADMAP.md: Milestone-based development roadmap (M0-M5)
docs/rfds/gpt5-dgx-pixels.md: External review feedback (note: assumes DGX B200, not applicable to our GB10)

Critical: Read relevant documentation before implementing any component. The research phase identified best practices, pitfalls, and optimal approaches.

Hardware Context: The system runs on DGX-Spark GB10 (single GPU, unified memory), NOT a multi-GPU DGX B200. This changes many architectural decisions. Always consult

docs/hardware.md

and

docs/adr/0001-dgx-spark-not-b200.md

when making hardware-related decisions.

Architecture Decision Required

Before writing any code, one of four architecture proposals must be selected:

Proposal 1: Rapid (1-2 weeks) - Automatic1111 + Simple CLI + Manual Bevy integration
- Use for: Quick prototypes, validation, solo developers
- Trade-offs: No training, manual workflows, limited scalability
Proposal 2: Balanced (4-6 weeks) - ComfyUI + FastAPI + MCP + LoRA Training
- Use for: Small studios (2-10 devs), production projects
- Trade-offs: Medium complexity, requires setup investment

2B. Proposal 2B: Rust TUI + Python (5-6 weeks) - ratatui TUI + ZeroMQ + Python Worker + ComfyUI [NEW RECOMMENDED]

Use for: Developers wanting fast, responsive UI with side-by-side model comparison
Key features: 60+ FPS TUI, <1ms IPC, Sixel image preview, compare pre-trained vs custom models
Trade-offs: Requires Rust knowledge, slightly longer initial setup than Proposal 2

Proposal 3: Advanced (8-12 weeks) - Full microservices + Kubernetes + Web UI + MLOps
- Use for: Large studios (50+ devs), multiple projects
- Trade-offs: High complexity, significant maintenance overhead

See

docs/02-architecture-proposals.md

and

docs/07-rust-python-architecture.md

for detailed comparison matrices and decision criteria.

Key Technical Decisions

These decisions were made after extensive research and should not be changed without strong justification:

Model Architecture

Base Model: Stable Diffusion XL 1.0 (NOT SD 1.5 - SDXL offers 3x larger UNet and better quality)
Fine-tuning Method: LoRA (NOT full fine-tuning - LoRA is faster, uses less memory, produces smaller files)
Training Framework: Kohya_ss or Diffusers (both support DGX-Spark optimizations)

Inference Engine

Balanced/Advanced: ComfyUI (2x faster than A1111, better for automation)
Rapid: Automatic1111 (faster setup, good for prototyping)

Integration Layer

Protocol: Model Context Protocol (MCP) for Bevy communication
Bevy Library: bevy_brp_mcp (enables AI assistants to control Bevy apps)
API Framework: FastAPI (modern, async, auto-docs) for Proposal 2
IPC: ZeroMQ (REQ-REP + PUB-SUB patterns, <1ms latency) for Proposal 2B

Rust + Python Architecture (Proposal 2B)

Frontend: Rust with ratatui TUI framework (60+ FPS rendering, Sixel image preview)
Backend: Python worker with ZeroMQ server for job management
Communication: ZeroMQ with MsgPack serialization
Side-by-Side Comparison: Unique feature allowing comparison of pre-trained vs custom LoRA models simultaneously
Playbook Integration: Leverages dgx-spark-playbooks ComfyUI setup

Hardware Optimization

Enable mixed precision training (FP16/FP4)
Use xformers memory-efficient attention
Leverage Tensor Cores for matrix operations
Load multiple models in 128GB unified memory
Unified Memory: Exploit zero-copy CPU↔GPU transfers (no cudaMemcpy overhead)
Single GPU Focus: No multi-GPU scaling complexity, simpler deployment
ARM Compatibility: Ensure all dependencies support ARM64 architecture

Implementation Guidelines

When Starting Implementation

Select architecture proposal - Don't mix approaches, commit to one path
Follow implementation plan - Use the step-by-step guide in
```
docs/06-implementation-plan.md
```
Read technology deep-dive - Understand SDXL, LoRA, ComfyUI before coding (see
```
docs/03-technology-deep-dive.md
```
)
Respect training roadmap - Custom models are essential for quality, follow the 12-week plan

Project Structure (To Be Created)

For Proposal 2B (Rust + Python):

dgx-pixels/
├── rust/             # Rust TUI application
│   ├── src/
│   │   ├── main.rs        # TUI entry point
│   │   ├── ui/            # ratatui UI components
│   │   ├── zmq_client.rs  # ZeroMQ communication
│   │   └── image_preview.rs # Sixel rendering
│   └── Cargo.toml
├── python/           # Python backend worker
│   ├── workers/
│   │   ├── generation_worker.py  # ZMQ server + ComfyUI client
│   │   └── zmq_server.py         # Job queue management
│   ├── requirements.txt
│   └── pyproject.toml
├── workflows/        # ComfyUI workflow JSON templates
├── models/           # Model storage (use Git LFS)
│   ├── checkpoints/  # Base SDXL models
│   ├── loras/        # Trained LoRAs
│   └── configs/      # Model metadata
└── examples/         # Example Bevy integrations

For Proposal 2 (Python only):

dgx-pixels/
├── src/
│   ├── api/          # FastAPI orchestration layer
│   ├── cli/          # Command-line tools
│   ├── training/     # LoRA training scripts
│   └── processing/   # Post-processing pipeline
├── workflows/        # ComfyUI workflow JSON templates
├── models/           # Model storage
└── examples/         # Example Bevy integrations

Critical Implementation Notes

LoRA Training:

Dataset: 50-100 images minimum for style training
Resolution: 1024x1024 for SDXL (not 512x512)
Training time: 2-4 hours on DGX-Spark
Don't skip training - pre-trained models won't match game art style

ComfyUI Workflows:

Save workflows as JSON templates with placeholder prompts
Create reusable workflows for: single sprite, animation frames, tile sets, batch generation
Version control workflows alongside code

MCP Integration:

Use FastMCP library for Python MCP server
bevy_brp_mcp for Bevy side
Test MCP connection before building higher-level features

Performance Targets (from research):

Inference: 3-5 seconds per 1024x1024 sprite
Batch generation: 20-30 sprites per minute
LoRA training: 2-4 hours per model (50 images, 3000 steps)
TUI rendering: 60+ FPS (Proposal 2B)
ZeroMQ IPC latency: <1ms (Proposal 2B)

Side-by-Side Model Comparison (Proposal 2B):

Generate with multiple models (pre-trained + custom LoRAs) simultaneously
Display results side-by-side in TUI for visual comparison
Track user preferences (which model produced better results)
Use comparison data to inform training improvements
Essential for validating that custom LoRA training improves quality

Bevy Integration Patterns

Two integration approaches are documented:

Manual: Generate → Review → Copy to
```
assets/
```
→ Reference in code
Automated (MCP): Generate → Auto-deploy via MCP → Hot reload in game

For MCP integration:

Bevy must have
```
bevy_brp_mcp
```
plugin enabled
Assets must follow Bevy's
```
assets/
```
directory structure

Use relative paths:

asset_server.load("sprites/character.png")

Enable hot reloading for development:

AssetPlugin { watch_for_changes_override: Some(true) }

See

docs/04-bevy-integration.md

for complete patterns and code examples.

Common Pitfalls (From Research)

Don't use SD 1.5 - SDXL is significantly better for pixel art
Don't skip LoRA training - Pre-trained models lack style consistency
Don't use blur/smooth upscaling - Use nearest-neighbor for pixel-perfect scaling
Don't ignore color quantization - Reduce to optimal palette in post-processing
Don't load models with FP32 - Use FP16 or FP4 to leverage Tensor Cores
Don't create absolute asset paths in Bevy - Use relative to
```
assets/
```
directory
Don't assume multi-GPU scaling - This is a single-GPU system, focus on batch optimization instead
Don't ignore ARM compatibility - Verify all dependencies support ARM64 architecture
Don't waste unified memory - Exploit zero-copy transfers, avoid unnecessary cudaMemcpy calls

Development Workflow

Agent Workflow (Automated)

When agents implement workstreams, they follow this workflow:

Create Branch:

just branch WS-XX

gh-create-branch "wsXX-name"

Implement Changes: Follow TDD (tests first!)
Run Quality Checks:
```
just ci
```
(fmt, lint, test)
Create PR:
```
just pr "Title"
```
or
```
gh-create-pr "Title"
```
Rebase onto Main:
```
gh-rebase-main
```
(before merge)
Auto-merge:
```
gh-auto-merge --merge-method squash
```
(after CI passes)

Manual Workflow (Human Developers)

See

CONTRIBUTING.md

for detailed manual workflow guidelines.

Project Commands (justfile)

The project uses

just

for task automation. Key commands:

# Setup
just init              # Initialize project (first time)
just validate-gpu      # Verify DGX-Spark hardware

# Development
just tui               # Run Rust TUI (debug)
just backend           # Start Python backend worker
just comfyui           # Start ComfyUI server

# Testing
just test              # Run all tests
just test-coverage     # Run tests with coverage
just ci                # Run all CI checks (fmt, lint, test)

# Code Quality
just fmt               # Format Rust code
just lint              # Run Rust clippy
just fmt-python        # Format Python code

# Models
just models-list       # List available models
just download-model    # Download SDXL base model
just train-lora DATASET  # Train LoRA on dataset

# Monitoring
just gpu-status        # Show GPU stats
just gpu-watch         # Monitor GPU (live)
just hw-info           # Show all hardware info

# Git
just status            # Show git status
just branch WS-XX      # Create branch for workstream
just pr "Title"        # Create pull request
just rebase            # Rebase onto main

# Documentation
just docs              # Generate and open Rust docs
just docs-serve        # Serve docs locally

# Full list
just --list            # Show all available commands

Nushell Scripts

The project uses nushell for automation scripts:

Location:

scripts/nu/

Modules:

```
config.nu
```
- Project config, logging, utilities
```
modules/comfyui.nu
```
- ComfyUI API wrapper
```
modules/dgx.nu
```
- DGX-Spark hardware utilities
```
modules/github.nu
```
- GitHub automation (PR, branch, merge)

Usage:

# Load config
use scripts/nu/config.nu *

# Check hardware
use scripts/nu/modules/dgx.nu *
dgx-validate-hardware
dgx-gpu-stats

# GitHub automation
use scripts/nu/modules/github.nu *
gh-create-branch "feature/new-tui"
gh-create-pr "Add new TUI feature" --draft
gh-auto-merge --merge-method squash

# ComfyUI integration
use scripts/nu/modules/comfyui.nu *
comfyui-health-check
comfyui-generate (open workflows/sprite-gen.json)

Testing Strategy (To Be Implemented)

When building the system:

Model Quality Tests: Generate from standard prompts, compare to references
Integration Tests: End-to-end generation → deployment → Bevy loading
Performance Tests: Verify 3-5s inference, 20-30 sprites/min batch
Training Tests: Verify LoRA training completes and improves quality

Critical Files to Understand

Before implementing any component:

HARDWARE FIRST: Read
```
docs/hardware.md
```
and
```
docs/adr/0001-dgx-spark-not-b200.md
```
to understand single-GPU unified memory architecture
Roadmap: Read
```
docs/ROADMAP.md
```
for milestone-based development plan (M0-M5)
Metrics: Read
```
docs/metrics.md
```
for performance targets and benchmarking strategy
Architecture decision: Read
```
docs/02-architecture-proposals.md
```
§ Comparison Matrix and Proposal 2B
SDXL + LoRA: Read
```
docs/03-technology-deep-dive.md
```
§ Stable Diffusion XL and LoRA sections
ComfyUI: Read
```
docs/03-technology-deep-dive.md
```
§ ComfyUI section
Bevy Assets: Read
```
docs/04-bevy-integration.md
```
§ Asset System Basics
Training: Read
```
docs/05-training-roadmap.md
```
§ Phase 2 before training first model

For Proposal 2B (Rust + Python) - RECOMMENDED:

Architecture: Read
```
docs/07-rust-python-architecture.md
```
§ ZeroMQ Communication Patterns
TUI Design: Read
```
docs/08-tui-design.md
```
§ Screen Layouts and Side-by-Side Comparison
Playbook Integration: Read
```
docs/11-playbook-contribution.md
```
§ Installation Steps

Next Steps (For First Implementation)

1. Review Documentation

Start with the orchestration summary:

cat docs/orchestration/project-summary.md

Key documents:

Architecture:
```
docs/02-architecture-proposals.md
```
(choose Proposal 2B recommended)
Hardware:
```
docs/hardware.md
```
(understand GB10 unified memory)
Roadmap:
```
docs/ROADMAP.md
```
(M0-M5 milestones)
Orchestration:
```
docs/orchestration/meta-orchestrator.md
```
(coordination strategy)
Workstreams:
```
docs/orchestration/workstream-plan.md
```
(all 18 workstreams)

2. Initialize Project

# Clone and setup
git clone https://github.com/raibid-labs/dgx-pixels.git
cd dgx-pixels

# Initialize project
just init

# Validate hardware
just validate-gpu

# View hardware info
just hw-info

3. Start with Foundation Orchestrator (M0)

The project uses orchestrated workstreams. Start with Foundation:

# Review Foundation Orchestrator
cat docs/orchestration/orchestrators/foundation.md

# Review first workstream (WS-01)
cat docs/orchestration/workstreams/ws01-hardware-baselines/README.md

# Create branch for WS-01
just branch WS-01

# Implement following the workstream spec
# (See CONTRIBUTING.md for detailed workflow)

4. Follow Orchestration Plan

After M0 completes, proceed through:

M1: Model Orchestrator (ComfyUI, SDXL optimization)
M2: Interface Orchestrator (Rust TUI, ZeroMQ, backend)
M3: Model Orchestrator (LoRA training)
M4: Integration Orchestrator (Bevy, MCP)
M5: Integration Orchestrator (observability, deployment)

See

docs/orchestration/meta-orchestrator.md

for coordination details.

Do not skip steps or mix architecture proposals - the plans are sequential and architecture-specific.

Repository Context

Hardware: This will run on a specific NVIDIA DGX-Spark - not generic cloud GPUs
Target Users: Game developers using Bevy engine (Rust-based)
Use Case: Rapid pixel art sprite generation for game prototyping and production
Unique Value: Open-source, optimized for specific hardware, direct game engine integration

The research phase identified that no existing solution combines all these requirements, which is why this project exists.

CLAUDE.md

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