Install ComfyUI and Build Your First Stable Diffusion Workflow

ComfyUI v0.33.1, released August 2026, can be installed locally to run Stable Diffusion 1.5 workflows, according to a tutorial by Mariana Souza on Sourcefeed. The guide walks through setting up ComfyUI, downloading the fp16 SD 1.5 checkpoint, building a text-to-image graph, adding a LoRA for style control, and integrating ESRGAN upscaling, requiring an NVIDIA GPU with 6 GB+ VRAM and about 8 GB free disk space.

Install ComfyUI and Build Your First Stable Diffusion Workflow Get ComfyUI running locally, then wire a txt2img graph with a LoRA and ESRGAN upscaling. Mariana Souza https://sourcefeed.dev/u/mariana souza What you'll build A local ComfyUI https://www.comfy.org/ 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 https://www.python.org/ 3.12 or 3.13 3.13 is the best-supported; 3.14 works but some custom nodes break and Git https://git-scm.com/ - 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 https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive 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: php 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 https://civitai.com/models/25995 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 — you got CPU-only wheels usually by running AssertionError: Torch not compiled with CUDA enabled 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.— the model file is corrupt, usually an HTML login page saved as safetensors rust.SafetensorError: Error while deserializing header: HeaderTooLarge .safetensors . Check ls -lh ; if it's kilobytes, re-download using the /resolve/ URL not /blob/ or after logging in to Civitai.— your GPU ran out of VRAM mid-sample. Relaunch with torch.OutOfMemoryError: CUDA out of memory python main.py --lowvram , or drop Empty Latent Image back to 512×512. Checkpoint dropdown shows — the file is in the wrong folder or was added while the server was running. Confirm it's in null models/checkpoints not a subfolder of your home dir and press R to refresh. Next steps Install ComfyUI-Manager https://github.com/Comfy-Org/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 https://docs.comfy.org/tutorials/basic/lora , and remember any PNG ComfyUI generates embeds its full workflow — drag one onto the canvas to reload it. Sources & further reading - Manual Installation - Local Self-Hosted https://docs.comfy.org/installation/manual install — docs.comfy.org - ComfyUI First Image Generation https://docs.comfy.org/get started/first generation — docs.comfy.org - ComfyUI LoRA Example https://docs.comfy.org/tutorials/basic/lora — docs.comfy.org - ComfyUI Image Upscale Example https://docs.comfy.org/tutorials/basic/upscale — docs.comfy.org - ComfyUI README https://github.com/comfyanonymous/ComfyUI — github.com - ComfyUI-Manager https://github.com/Comfy-Org/ComfyUI-Manager — github.com Mariana Souza https://sourcefeed.dev/u/mariana souza · Senior Editor Mariana covers the fast-moving world of machine learning and generative AI, with a particular focus on how these technologies are reshaping development workflows. When she isn't stress-testing the latest foundation models, she's usually at a local hackathon. Discussion 0 No comments yet Be the first to weigh in.