Install ComfyUI and Build Your First Stable Diffusion Workflow

What you'll build

A local ComfyUI install running Stable Diffusion 1.5, plus a node-based text-to-image workflow you'll extend with a LoRA for style control and an ESRGAN upscaler — all copy-pasteable from a clean machine.

Prerequisites

Verified against ComfyUI v0.33.1 (August 2026) with PyTorch CUDA 13.0 wheels.

• Python 3.12 or 3.13 (3.13 is the best-supported; 3.14 works but some custom nodes break) and Git
• An NVIDIA GPU with 6 GB+ VRAM for comfortable SD 1.5 use. AMD on Linux works via ROCm (swap the torch install for --index-url https://download.pytorch.org/whl/rocm7.2); Apple silicon works via PyTorch nightly. No GPU at all? Add --cpu to the launch command — slow but functional.
• ~8 GB free disk for the code and models

Commands below are for Linux/macOS; on Windows use venv\Scripts\activate and the same pip commands (or grab the portable build from comfy.org and skip section 1).

1. Install ComfyUI

Clone the repo, create a virtual environment (ComfyUI's pinned deps will conflict with a system Python), and install PyTorch before the rest of the requirements:

git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
python3 -m venv venv
source venv/bin/activate
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130
pip install -r requirements.txt

2. Download a checkpoint

Checkpoints go in models/checkpoints. Grab the fp16 SD 1.5 base model from Comfy-Org's Hugging Face archive (~2 GB):

curl -L -o models/checkpoints/v1-5-pruned-emaonly-fp16.safetensors \
  "https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly-fp16.safetensors"

3. Run the default txt2img workflow

python main.py

Open http://127.0.0.1:8188. Load the default workflow via Workflow → Browse Templates → Image Generation (or it's already on the canvas on first launch). The graph reads left to right:

flowchart LR
    LC[Load Checkpoint] --> CT1[CLIP Text Encode<br>positive]
    LC --> CT2[CLIP Text Encode<br>negative]
    EL[Empty Latent Image] --> KS[KSampler]
    CT1 --> KS
    CT2 --> KS
    LC --> KS
    KS --> VD[VAE Decode] --> SI[Save Image]

Pick v1-5-pruned-emaonly-fp16.safetensors in Load Checkpoint, type a prompt into the positive CLIP Text Encode node, and hit Run (Ctrl+Enter). Images land in the output/ folder.

4. Wire in a LoRA

LoRAs are small adapter weights that restyle a checkpoint. They live in models/loras. The official docs use the SD 1.5-compatible blindbox LoRA from Civitai (log in on the site if the direct download 401s):

curl -L -o models/loras/blindbox_V1Mix.safetensors \
  "https://civitai.com/api/download/models/32988?type=Model&format=SafeTensor&size=full&fp=fp16"

Back in the browser, press R to refresh the model lists, then double-click empty canvas, search Load LoRA, and splice it between the checkpoint and everything downstream: Load Checkpoint's MODEL → Load LoRA model input, CLIP → clip input; then Load LoRA's outputs feed the KSampler and both CLIP Text Encode nodes. strength_model scales the LoRA's effect on the diffusion weights, strength_clip on the text encoder — 1.0 for both is fine here. Add the trigger words chibi, full body to your prompt and run again; you'll get toy-figurine style renders. Chain a second Load LoRA node after the first to stack styles.

5. Add upscaling

SD 1.5 natively generates 512×512. Model-based upscaling gets you a clean 4× without re-diffusing. Download RealESRGAN into models/upscale_models:

curl -L -o models/upscale_models/RealESRGAN_x4plus.pth \
  "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth"

Refresh again, add a Load Upscale Model node and an Upscale Image (using Model) node. Wire VAE Decode's IMAGE into the upscaler's image input, the model loader into upscale_model, and hang a second Save Image off the output so you keep both sizes.

Verify it works

On launch the terminal should show your GPU and the server address:

Total VRAM 12282 MB, total RAM 32017 MB
pytorch version: 2.8.0+cu130
Device: cuda:0 NVIDIA GeForce RTX 3060
Starting server

To see the GUI go to: http://127.0.0.1:8188

After a run, the progress bar in KSampler completes, got prompt and Prompt executed appear in the terminal, and output/ contains a 512×512 image plus a 2048×2048 upscaled one.

Troubleshooting

• AssertionError: Torch not compiled with CUDA enabled — you got CPU-only wheels (usually by running pip install -r requirements.txt first). Fix: pip uninstall torch torchvision torchaudio, then reinstall with the --extra-index-url https://download.pytorch.org/whl/cu130 command from step 1.
• safetensors_rust.SafetensorError: Error while deserializing header: HeaderTooLarge — the model file is corrupt, usually an HTML login page saved as .safetensors. Check ls -lh; if it's kilobytes, re-download using the /resolve/ URL (not /blob/) or after logging in to Civitai.
• torch.OutOfMemoryError: CUDA out of memory — your GPU ran out of VRAM mid-sample. Relaunch with python main.py --lowvram, or drop Empty Latent Image back to 512×512.
• Checkpoint dropdown shows null — the file is in the wrong folder or was added while the server was running. Confirm it's in models/checkpoints (not a subfolder of your home dir) and press R to refresh.

Next steps

Install ComfyUI-Manager (git clone https://github.com/ltdrdata/ComfyUI-Manager inside custom_nodes/, then restart) — it auto-installs missing custom nodes when you import someone else's workflow. From there, browse the built-in template library for SDXL and image-to-image graphs, work through the official examples, and remember any PNG ComfyUI generates embeds its full workflow — drag one onto the canvas to reload it.