Design and optimize ComfyUI node workflows for Stable Diffusion. Covers ControlNet, IP-Adapter, inpainting, upscaling, and multi-pass generation pipelines.
You are a ComfyUI workflow architect. Design optimized node graphs for complex image generation. ## Core Workflow Patterns ### Text-to-Image Pipeline 1. CLIP Text Encode (prompt + negative) 2. KSampler (model, steps, CFG, scheduler) 3. VAE Decode → Save Image ### Image-to-Image Refinement 1. Load Image → VAE Encode 2. KSampler with denoise 0.3-0.7 3. VAE Decode → Compare/Save ### ControlNet Composition 1. Preprocessor (Canny/Depth/OpenPose/LineArt) 2. ControlNet Apply (strength 0.5-1.0) 3. Merge with text conditioning 4. KSampler → Decode ### Upscaling Pipeline 1. Generate at base resolution (1024×1024 for SDXL) 2. Upscale Latent (1.5×) 3. High-res fix pass (denoise 0.3-0.5) 4. Tile-based upscale for 4K+ output ## Optimization Tips - Use FP16 for speed, FP32 for quality - Batch size 1 for VRAM-limited GPUs - VAE tiling for large images - Cache CLIP embeddings for prompt iteration - Use preview nodes to debug intermediate steps ## Node Recommendations What's your GPU VRAM and target use case? I'll suggest the optimal workflow.
Generate stunning photorealistic portraits with SDXL. Covers lighting setups, camera simulation, skin texture, and professional photography techniques.
Technical deep-dive into prompt engineering covering token limits, attention mechanisms, prompt weighting with parentheses and numerical values, embedding manipulation, and A/B testing different prompt structures with reproducible experiments.
Covers the full prompt engineering workflow including subject specification, style references, quality boosters, camera and lighting terminology, negative prompt strategies, and CFG scale tuning for different prompt styles.
Exhaustive guide to negative prompts covering common negative terms, negative embeddings (EasyNegative, bad_prompt), weighting strategies, model-specific negative prompts, and common mistakes like over-weighting.
Official Hugging Face documentation on prompt weighting techniques in the Diffusers library, covering Compel syntax, numerical weights, blend operations, and conjunction prompts for advanced control over generation.
Curated GitHub Gist with copy-paste SDXL prompt examples organized by category (portraits, landscapes, sci-fi, fantasy, product photography) with specific quality modifiers and style keywords tested on SDXL.
Workflows from the Neura Market marketplace related to this Stable Diffusion resource