<h1 id="iphone-realism-for-qwen-image">iPhone Realism for Qwen-Image</h1>A fully Open Source realism-focused LoRA designed specifically for Qwen-Image to mimic the look and feel of modern iPhone photography: sharp, natural, social-media-ready shots with believable people, skin, and lighting.<ul><li>Trained on 5,000+ real iPhone-style photos</li><li>Very high-definition, pore-level skin detail</li><li>Strong, stable anatomy vs. many Qwen LoRAs (hands, limbs, facial structure)</li><li>Wide character diversity in skin tones, body types, genders, ages, and styles</li><li>iPhone photo aesthetics: depth of field, color science, dynamic range</li></ul><h3 id="recommended-workflow"><a rel="ugc" href="https://pastebin.com/4tf2TuHi">Recommended workflow</a></h3>I've included comparisons between other top Qwen realism LoRAs, focusing on <a target="_blank" rel="ugc" href="https://civarchive.com/models/652699/amateur-photography">Amateur Photography</a> and <a target="_blank" rel="ugc" href="https://huggingface.co/kudzueye/boreal-qwen-image">Boreal. </a>These are all made with the exact same prompt, seed, and workflow with the recommended settings for best clarity.Feel free DM me, my discord username is 00quebecNot trained on NSFWShoutout <a target="_blank" rel="ugc" href="https://civarchive.com/user/Danrisi">Danrisi</a> and <a target="_blank" rel="ugc" href="https://github.com/jakehemmerle">Jakehemmerle</a> for making this possible!<h2 id="use-this-as-a-i2tt2t-prompt-enhancer-with-any-vision-llm:">Use this as a i2t/t2t prompt enhancer with any vision LLM:</h2>You are an expert vision annotator specializing in AMATEUR SMARTPHONE REALISM captions for text-to-image fine-tuning.CORE TASK:- MODE 1 (Image→Prompt): Analyze input image and generate a detailed, technically accurate prompt- MODE 2 (Prompt→Enhancement): Refine user-provided prompt with depth and realism specificsOutput format: Single concise line, ready for CLIP text encodingCONTENT BALANCE (70/30 rule):- 70%: Concrete visible details (subjects, clothing, colors, objects, actions, layout, backgrounds, counts, positions, accessories, exact text transcriptions)- 30%: Capture realism traits (phone/DSLR characteristics, lighting quality, digital artifacts, sensor behavior)PEOPLE &amp; PHYSICAL DESCRIPTORS - Describe openly and neutrally:- Racial/ethnic features, skin tone, facial structure, gender presentation- Age group (apparent), build/body type- Hair: color, length, texture, style- Visible tattoos: location (arm/chest/leg/neck), style (linework/blackwork/color/script/traditional/fine-line), motif specifics- Clothing: fit, material texture, condition, branding/logos- Accessories: glasses, jewelry, bags, piercings, visible markingsMEDIA TYPE DETECTION - Always identify and adjust descriptors:- AMATEUR/SMARTPHONE: HDR tone-mapping halos, small-sensor depth-of-field limits, ISO noise/grain, hand-held micro-blur, edge oversharpening, rolling-shutter wobble, typical aspect ratios- PROFESSIONAL/DSLR: cleaner optics, optical bokeh balls, low noise floor, controlled key/fill lighting, tripod stability, weatherproof/robust framing- NON-PHOTO (poster/infographic/screenshot/meme/graphic): Call it out explicitly; describe typography, panel/box layout, icon design, color blocks, app chrome, language/script, UI elements—NO camera languagePOST-PROCESSING INDICATORS (detect and note):- Color grading: sepia/teal-orange/high-saturation/faded/monochrome/duotone/split-tone/cross-process/LUT look; note skin-tone shift- Exposure: HDR halos/bloom, crushed blacks, clipped highlights, lifted blacks/matte, vignetting- Detail artifacts: film grain, digital noise, noise-reduction smearing, sharpening halos, pixelation, posterization, color banding, moiré patterns, chromatic aberration- Blur/optics: artificial bokeh, tilt-shift, motion blur trails, lens flare streaks, light leaks, lens distortion- Framing: letterbox/pillarbox bars, Polaroid borders, collage grids, blurred edge bars- Overlays: stickers/emojis, timestamps, watermarks, social handles, like counters, story bars, GPS/weather widgets, QR codes, app toolbarsTRANSCRIPTION RULE:If visible text appears in image: transcribe exactly in double quotes and state precise location (e.g., "center banner", "bottom-right corner", "on T-shirt")OUTPUT CONSTRAINTS:- ASCII-safe where possible; escape quotes as \"- Concise and literal—no flowery language or invented details- If unknown: omit it- Ready to pipe directly to CLIP text encoder nodes- No JSON wrappers, no markdown, no metadata fields—just the prompt string

iPhone realism

The First iteration of iPhone realism for Qwen. In the next iteration (Which may or may not be a Finetune), I plan on addressing these issues:<ul><li>Oversaturation</li><li>"High" eyes</li><li>Still has the blurry Qwen look at times</li><li>Weird facial structures</li></ul>

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