I was frustrated with laggy notebook stable diffusion demos. Plus they usually didn't have all the features I wanted (for example some of them only had inpainting and some only had img2img, so if I wanted both I had to repeatedly copy images between notebooks). So I made this desktop frontend which has much smoother performance than notebook alternatives and integrates image generation, inpainting and img2img into the same workflow. See a video demo here: <a href="https://user-ima
I was frustrated with laggy notebook stable diffusion demos. Plus they usually didn't have all the features I wanted (for example some of them only had inpainting and some only had img2img, so if I wanted both I had to repeatedly copy images between notebooks). So I made this desktop frontend which has much smoother performance than notebook alternatives and integrates image generation, inpainting and img2img into the same workflow. See a video demo here: <a href="https://user-images.githubusercontent.com/6392321/191858568-0550f52d-e89c-4b37-aa07-23df605b4807.mp4" rel="nofollow">https://user-images.githubusercontent.com/6392321/191858568-...</a><p>Features include:<p>* Can run locally or connect to a google colab server<p>* Ability to erase<p>* Ability to paint custom colors into the image. It is useful both for img2img (you can sketch a rough prototype and reimagine it into something nice) and inpainting (for example, you can paint a pixel red and it forces Stable Diffusion to put something red in there)<p>* Infinite undo/redo<p>* You can import your other images into a scratch pad and paste them into main image after erasing/cropping/scaling it<p>* Increase image size (by padding with transparent empty margins) for outpainting
Design and optimize ComfyUI node workflows for Stable Diffusion. Covers ControlNet, IP-Adapter, inpainting, upscaling, and multi-pass generation pipelines.
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.
Workflows from the Neura Market marketplace related to this Stable Diffusion resource