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Dive into the intricacies of ComfyUI nodes, from Checkpoint Loader Simple to KSampler Advance, and unravel the complexities of text-to-image and image-to-image workflows.
Hello Everyone, in this video, I explained the fundamental built-in nodes of ComfyUI, their functionalities and technical nuances. Starting with the Checkpoint Loader Simple, the tutorial explains how they interact and contribute to text-to-image and image-to-image workflows.
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[TIMESTAMPS]
00:00:00 Introduction
00:00:36 Checkpoint Loader Simple
00:02:01 Primitive Node
00:03:39 CLIP and Clip Text Encode
00:05:25 Model or UNet
00:06:04 safetensors and CKPT checkpoints
00:06:43 Variational autoencoder or VAE
00:08:30 Latent Space
00:10:02 VAE decode
00:10:11 Empty latent image
00:11:37 KSampler
00:14:03 Seed
00:15:28 Step or inference step
00:16:10 CFG or classifier-free guidance scale
00:16:39 KSampler and Noise scheduler
00:17:51 Euler
00:18:39 Euler Ancestral
00:20:11 Positive and Negative Conditioning
00:20:16 Latent image
00:20:27 Noise scheduler
00:21:14 Denoise
00:22:25 Utils
00:22:28 Note
00:23:07 Primitive
00:24:24 Reroute
00:25:00 custom samplers, schedulers, samplers
00:25:21 KSampler advance
00:26:09 SDXL base workflows
00:26:25 Upscale latent
00:27:05 Conclusion
00:27:17 like
00:27:24 Subscribe to the channel and I will see you in the next one
[TAGS]
comfyui, Code Crafters Corner, CodeCraftersCorner, ComfyUI, Stable Diffusion, Text-to-Image, Image-to-Image, Machine Learning, Tutorial, Checkpoint Loader, KSampler, Clip Text Encode, VAE Decode, Noise Scheduler, Workflow Optimization.
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#StableDiffusion #ComfyUI #CodeCraftersCorner #ComfyUI #TextToImage #ImageToImage #MachineLearning #StableDiffusion #Tutorial