Understanding Prompting for Stable diffusion in ComfyUI скачать в хорошем качестве

Understanding Prompting for Stable diffusion in ComfyUI

A deep dive into how Clip Text Encode works in ComfyUI for Stable Diffusion, analyzing tokens, conditioning, and prompt engineering best practices. In this video, we take a deep dive into how the ClipTextEncode node works in ComfyUI for Stable Diffusion. We look at what happens behind the scenes when text gets tokenized, converted into a condition tensor, and passed to the diffusion model. First, we use Python debugging to inspect the tokens - we see how the text gets split into start tokens, word tokens, and stop tokens in batches of 77. This helps us understand optimal prompt lengths. Next, we examine the condition tensor, seeing how it is a multidimensional representation of the text data. We look at the default 32-bit float precision and how it impacts conditioning. [CORRECTION] There is a small correction about the 32-bits float precision and it's well explained by Reddit user u/adhd_ceo in this post here: https://bit.ly/3tvkzw9 Finally, we explore techniques like ConditioningConcat, ConditioningAverage, and ConditioningCombine to isolate prompt elements and improve image generation. We see how separating color elements into different text encodes can help reduce color bleeding issues. Overall, this video gives you a deeper understanding of how text conditioning works in Stable Diffusion inside of ComfyUI Web Interface so you can craft better prompts and use conditioning nodes more effectively. I appreciate if you can like and share the video if it was helpful. Subscribe for more content soon! [SUPPORT THE CHANNEL] Patreon: https://bit.ly/44js1Xx Paypal: https://bit.ly/45lJsIg [Resources] Blog Post:   / 95377687   [SOCIAL MEDIA] YouTube Channel: https://bit.ly/47OterT Twitter X: https://bit.ly/3ReP9D3 [PREVIOUS VIDEOS] ComfyUI End of Year Updates:    • End of Year ComfyUI Updates for Stable Dif...   Custom Nodes:    • Create Your Own Custom Nodes in ComfyUI   SDXL Turbo Gradio App:    • How to Use My SDXL Turbo Gradio App   SDXL Turbo:    • How to Use SDXL Turbo in Comfy UI for Fast...   Python API for ComfyUI:    • Building a Python API for Comfy UI with Gr...   Introduction to Gradio:    • Introduction to Python Gradio - Build Mach...   Timestamps: 00:00:00 Introduction 00:01:11 Loading the default workflow 00:02:05 Adding a breakpoint 00:03:40 Using the Python Debugger 00:05:28 Analyzing the CLIP object 00:08:22 Analyzing Tokens 00:09:52 Prompt and word weights 00:12:12 Tokens of a batch of 77 00:13:07 Long Text Prompt 00:15:36 The WebUI Error Message 00:17:20 Conditioning Tensors 00:18:51 Torch.float32 [Correction: Check Reddit link in video description] 00:19:39 ConditioningConcat 00:20:14 ConditioningAverage 00:20:27 ConditioningCombine 00:21:46 Examples 00:22:46 Example of well trained model 00:23:40 Bleeding of colors 00:24:57 ConditioningConcat 00:26:04 ConditioningAverage 00:26:45 ConditioningCombine 00:28:22 Conclusion 00:29:18 Thank you for watching 00:29:31 I will see you in the next one Tags: stable diffusion, ComfyUI, cliptextencode, stablediffusion, clip text encode, python debugger, tokens, conditioning, prompt engineering, conditioning tensors, conditioningconcat, conditioningcombine, conditioningaverage Hashtags: #stablediffusion #cliptextencode #python #tokens #conditioning #promptengineering #comfyui