(extremely detailed cute and beautiful face:2

Flux 📌 Face + Upscale by  🚩

2.0

# Flux 📌 Face + Upscale by 🚩 Introduction to Workflow Flux 📌 Face + Upscale by 🚩 It is a consistent magnification workflow developed by Soil Sight Studio, mainly used for image processing and generation, particularly performing well in facial extraction and image super-resolution. ##1、 Model correlation 1. Rich types of models: Workflow involves multiple models, including Unet model, Clip encoder, VAE, etc. These models each undertake different functions, such as the Unet model for core image processing, Clip encoder for text image association, and VAE for image encoding and decoding. 2. Model source and placement: The model is mainly sourced from platforms such as Civitai and Hugging Face, and has clear placement path guidance. For example, the Flux Dev Q5 GGUF model is placed in the models/unet/directory, while the clip_1 model is placed in the models/clip/directory. At the same time, detailed download links are provided to facilitate users in obtaining and configuring the required models. ##2、 Workflow Process 1. * * Image Input and Preprocessing * *: Firstly, the image is loaded through the LoadImage node, and then the AutoDropFaces node may be used for face extraction, automatically detecting and cropping facial regions in the image to prepare for subsequent processing. Image scaling operations can also be performed, such as the ImageScaleToMegapixels node, which can scale the image at a specified magnification. 2. Model loading and setting: Load Unet model, VAE model, Clip model, etc. separately, and set and obtain the model and related parameters through SetNode and GetNode nodes. For example, loading the Unet model through the UNETLoader node, loading the VAE model through the VAELoader node, and loading the Clip model through the DualCLIPLOADer node. 3. Image Generation and Processing: Using the KSampler node for image sampling, generate new images based on the input model, positive conditions, negative conditions, and Latent images. The ControlNetApply Advanced node applies the ControlNet model to further control and optimize the image, adjusting the generation effect of the image by setting different parameters. 4. * * Post processing and output * *: The generated image can be previewed through the PreviewiImage node or saved locally using the SaveImage node. During this process, some post-processing operations can also be performed, such as the PlaySound | pysssss node playing specified sound prompts when image generation is complete. ##3、 Copyright Notice This workflow is independently developed by SoilSighStudio and is not for sale. Copyright✦ SoilSighStudio ✦
SoilSighStudio
Juliane Brazilian

5.0

The presentation of the model in this image is done in a tasteful and elegant manner. The woman is shown in a close-up portrait, allowing the viewer to focus on her beautiful facial features and natural, radiant beauty.Her pose is relaxed and natural, with her shoulders slightly , creating a sense of openness and approachability. The lighting is soft and flattering, highlighting the contours of her face and the warmth of her skin tone.The woman's expression is warm and inviting, with a genuine, captivating smile that draws the viewer in. Her gaze is direct and engaging, establishing a connection with the audience.The overall presentation emphasizes the model's inherent charm, grace, and natural allure, without relying on overly provocative or objectifying elements. The image celebrates the woman's beauty in a tasteful, respectful manner, allowing her inner confidence and charisma to shine through.To further enhance the presentation, the prompt could suggest:"A beautifully composed, high-resolution portrait that showcases the model's natural, radiant beauty and captivating charm. The lighting is soft and flattering, accentuating the delicate features of her face and the warmth of her skin tone. Her relaxed, open posture and genuine, inviting smile create a sense of warmth and approachability, drawing the viewer in and highlighting her inherent grace and elegance."This prompt emphasizes the thoughtful, tasteful approach to the model's presentation, focusing on her natural allure and the image's overall artistic merit.
novelist987
Flux Kontext Fast + Upscale Workflow [WIP]

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Flux Kontext Fast + Upscale Workflow Status: Work in Progress - Still refining output quality This workflow combines the power of FLUX.1 Kontext with the Alimama Turbo LoRA to achieve fast, high-quality image generation and upscaling in just 8 steps. Key Features Fast Generation: Utilizes the alimama-creative/FLUX.1-Turbo-Alpha LoRA for rapid image generation with only 8 inference steps Context-Aware Editing: Leverages FLUX.1 Kontext capabilities for intelligent image editing and manipulation Two-Stage Process: Initial generation followed by upscaling for enhanced detail and resolution Efficient Workflow: Optimized for speed while maintaining quality output Workflow Components Stage 1: Fast Generation Model: FLUX.1 Kontext with Turbo Alpha LoRA Steps: 8 (significantly reduced from standard 20-50 steps) Guidance Scale: 2.5 (optimized for Turbo LoRA) Sampler: Euler with simple scheduler Stage 2: Upscaling Method: Ultimate SD Upscale with custom sampling Upscale Model: 4x-ClearRealityV1 Tile Processing: Intelligent slicing for memory efficiency Denoising: 0.2 strength for detail enhancement Technical Setup Required Models FLUX.1 Kontext DEV (FP8 quantized) Alimama FLUX.1-Turbo-Alpha LoRA 4x upscale model Standard FLUX text encoders (CLIP-L + T5) Memory Optimization FP8 model quantization for reduced VRAM usage Tile-based upscaling to handle large images Optional caching nodes for faster iterations Prompt Guidelines The workflow works best with detailed, descriptive prompts. The Turbo LoRA has been trained on high-aesthetic images (6.3+ rating) with resolution >800px, so it excels at: Detailed scene descriptions Lighting and atmosphere specifications Style and aesthetic directions Object replacement and background changes Current Limitations ⚠️ Work in Progress: Output quality is still being refined Some inconsistencies in fine details Occasional artifacts in upscaled regions This workflow represents an efficient approach to context-aware image editing with FLUX, trading some quality for significant speed improvements. Perfect for rapid prototyping and iterative design work.
DenRakEiw