| Node | Description | |---|---| | Gigachad Model Loader | Load diffusion models with Gigachad energy | | Gigachad Checkpoint Loader | Checkpoint loading, streamlined | | Gigachad CLIP Loader | Load CLIP models cleanly | | Gigachad Prompt Encoder | Encode prompts like a chad | | Gigachad Prompt File Reader | Read prompts from external text files | | Gigachad KSampler | Feature-packed sampler with ClownSampler/RES4LYF support and bongmath | | Gigachad Sampler Custom Advanced | Advanced custom sampling controls | | Gigachad Power LoRA Loader | Load multiple LoRAs with power | | Gigachad Resolution Picker | Pick standard resolutions fast, outputs WIDTH + HEIGHT + LATENT | | Gigachad LTX Resolution Picker | Resolution picker tuned for LTX Video | | Gigachad VAE Loader | Clean VAE loading node | | Gigachad VAE Encode / Decode | VAE encode and decode utilities | | Gigaresolution (RTX Super Res) | RTX-powered super resolution upscaling | | Gigachad Cache Cleanup | Free up VRAM/RAM with one node, reports timing and free VRAM | | Gigachad Show Text | Display text output inline in the graph |

🚀 Installation

Option 1 — ComfyUI Manager (Recommended)

Search for comfyui-gigachad in the ComfyUI Manager and install directly.

Option 2 — Manual Install

Navigate to your ComfyUI custom nodes folder:
```
cd ComfyUI/custom_nodes
```

Clone this repo:

git clone https://github.com/Winnougan/comfyui-gigachad.git

Restart ComfyUI.

No extra dependencies required — pure ComfyUI native.

🖥️ Requirements

ComfyUI
Python 3.10+
A GPU that doesn't fear greatness

📖 How to Use the Nodes

All Gigachad nodes live under the Gigachad category in the node menu. Right-click the canvas → Add Node → Gigachad.

🔧 Basic txt2img Workflow

A typical Gigachad pipeline looks like this:

Gigachad Checkpoint Loader
        ↓
Gigachad CLIP Loader → Gigachad Prompt Encoder (positive + negative)
        ↓
Gigachad Resolution Picker → Gigachad KSampler
        ↓
Gigachad VAE Decode → Save Image

🖼️ Gigachad Resolution Picker

Outputs WIDTH, HEIGHT, and an empty LATENT — wire all three directly into the KSampler.

| Input | Description | |---|---| | width | Image width in pixels (default 1024, step 8) | | height | Image height in pixels (default 1024, step 8) | | batch_size | Number of images to generate at once |

Tip: Use the LTX Resolution Picker instead if you're running LTX Video workflows — it uses LTX-specific aspect ratios.

⚡ Gigachad KSampler

The star of the show. Drop-in replacement for the standard KSampler with extra firepower.

| Input | Description | |---|---| | model | Connect your loaded model | | positive / negative | Conditioning from Prompt Encoder | | latent_image | Connect from Resolution Picker or VAE Encode | | seed | Generation seed | | steps | Number of sampling steps (default 20) | | cfg | Classifier-free guidance scale (default 7.0) | | sampler | Standard ComfyUI sampler list | | clown_sampler | RES4LYF/ClownSampler list — overrides sampler when not set to none | | scheduler | Noise schedule (includes beta57, linear_quadratic extras) | | denoise | Denoising strength (1.0 = full generation, lower = img2img) | | bongmath | Toggle high-precision denoising for Flux/DiT models | | bongmath_cfg_scale | CFG scale inside bongmath (independent of outer cfg) | | bongmath_scale | Bongmath noise scale / step multiplier | | sigmas (optional) | Override the scheduler with a custom sigma schedule | | options (optional) | RES4LYF OPTIONS block for advanced ClownSampler control |

Outputs: latent and denoised_output — use latent for normal workflows, denoised_output for chaining into further processing.

Tip: If you're not using RES4LYF, leave clown_sampler on none and it'll use the standard sampler list as normal. Bongmath is specifically for Flux/DiT models — leave it off for SD1.5/SDXL.

🧹 Gigachad Cache Cleanup

A passthrough node you can drop anywhere in your workflow to free VRAM between heavy operations. Accepts and returns any type — it won't break your connections.

| Input | Description | |---|---| | any_input | Connect anything (optional) — it passes straight through | | empty_cache | Toggle torch.cuda.empty_cache() | | gc_collect | Toggle Python garbage collection |

After running, it displays elapsed time in milliseconds and your current free/total VRAM in the node UI.

Tip: Place one after your KSampler and before upscaling to reclaim VRAM before the next big operation.

📄 Gigachad Prompt File Reader

Load prompts from a .txt file on disk instead of typing into the graph. Great for batch workflows and prompt libraries.

Connect its output to the Gigachad Prompt Encoder positive or negative input.

📝 Gigachad Show Text

Displays any text string directly in the graph as a read-only node. Useful for debugging prompt outputs, displaying metadata, or just labeling sections of a complex workflow.

🔍 Gigaresolution (RTX Super Res)

Upscale your generated images using RTX Super Resolution. Connect the output image from your VAE Decode into this node for a clean upscale pass.

Note: Requires an NVIDIA RTX GPU with NIS/RTX Super Resolution support.

💡 Example: Minimal Workflow

Gigachad Checkpoint Loader
    ↓ MODEL, CLIP, VAE
    
Gigachad CLIP Loader (if needed)

Gigachad Prompt Encoder
    ← CLIP
    ← positive prompt text
    ← negative prompt text
    ↓ CONDITIONING (positive + negative)

Gigachad Resolution Picker
    ↓ WIDTH, HEIGHT, LATENT

Gigachad KSampler
    ← MODEL
    ← positive CONDITIONING
    ← negative CONDITIONING  
    ← LATENT (from Resolution Picker)
    ↓ latent

Gigachad Cache Cleanup  ← (optional, drop here to free VRAM)
    ↓ passthrough latent

Gigachad VAE Decode
    ← VAE
    ← latent
    ↓ IMAGE

Save Image

❤️ Support

If these nodes save you time or spark something cool, consider supporting on Patreon — exclusive workflows, nodes, and LLM setups drop there first.

📄 License

Apache 2.0 — use it, build on it, don't be lame about it.