清程爱画
Product information, use cases, and access for 清程爱画.
Pricing information
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What is 清程爱画?
Qingcheng AI Draw is an image-generation model community for people who want to create visuals online, explore models, and reuse production workflows. Its features are organized around image generation, workflow sharing, model use, and personal work management, supporting both quick experiments and more controlled creative work.
Users can browse, publish, and run workflows from the community, or build and adjust workflows online with ComfyUI. The platform also combines quick image generation, prompt refinement, private model storage, and portfolio management, making it suitable for beginners, visual creators, and teams that need a shared place to organize models and creative output.
Key features of 清程爱画
Online Workflow Community
Users can browse and publish image-generation workflows in the community and run available workflows online. The built-in ComfyUI workspace also supports building new workflows, adjusting parameters, and continuing work on existing ones, which suits both workflow reuse and gradual exploration of node-based creation.
Quick Generation and Prompt Refinement
A quick-generation mode lets users start creating images with fewer configuration steps, lowering the entry barrier for complex workflows. The platform can also refine a simple prompt into a more developed English prompt, helping users clarify an idea before moving into a more detailed workflow.
Model and Portfolio Spaces
The platform supports managing private and public Checkpoint and LoRA models, alongside personal spaces for a private model library, portfolios, work collections, and saved items. This organization is useful for creators who repeatedly use specific models, keep creative assets together, or present generated work.
LoRA Model Training
Users can train LoRA models for SD1.5, SDXL, and FLUX by configuring training parameters, uploading and preparing image data, previewing sample outputs with prompts, and starting a training job. Completed models can remain available in the platform library, be shared with the community, or be downloaded for local use.