Qwen3.5: Text Generation
Use the Qwen3.5 model to analyze an input image and generate descriptive text prompts. This workflow performs image captioning and reverse prompt engineering.
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关于此工作流
This workflow demonstrates how to use Qwen3.5 inside ComfyUI to analyze an image and generate descriptive text that doubles as ready-to-use prompts. It performs both image captioning and reverse prompt engineering: you provide an image via LoadImage, and the TextGenerate node, powered by Qwen3.5, returns structured descriptions or prompt candidates you can paste into your image-generation pipelines.
Technically, the CLIPLoader node is pointed at the Qwen3.5 weights (qwen3.5_4b_bf16.safetensors) stored under models/text_encoders/. That model handle feeds into the TextGenerate node, which accepts the loaded image and an instruction prompt (for example, "Produce 3 concise, diffusion-ready prompts"). The node then runs inference and returns text, which you can view with PreviewAny. A MarkdownNote in the graph provides inline guidance and prompt tips, making it easy to iterate on instruction wording, temperature, and token length to dial in results.
常见问答
Beyond plain descriptions, the workflow can generate structured, diffusion-ready prompts (subject, medium, lens, lighting, style, and optional negatives) when you specify that format in the TextGenerate instruction.
No. Once you download qwen3.5_4b_bf16.safetensors into ComfyUI/models/text_encoders/, the workflow runs locally in ComfyUI.
Use clear, high-quality images; provide a precise instruction template (e.g., ask for 3 concise prompts with subject, medium, lens, lighting, style, and negatives); and tune generation parameters like temperature (for more or less creativity) and max tokens (for longer outputs).
Yes, as long as it’s packaged for use with the TextGenerate node and placed where CLIPLoader can load it (models/text_encoders/). Update the CLIPLoader selection to the desired variant and keep the instruction format consistent.