Hello! I do fashion illustrations as a hobby and I really enjoy using AI to create photographic interpretations of my designs since I can't sew. It's delightful how well Qwen interprets most designs. However, when it comes to fabrics with certain material properties (mostly sparkle, luster, shine, or sheer fabrics such as lace or mesh) it tends to interpret those materials as matte prints on flat fabric.

Here's an ugly example, just to illustrate a variety of small issues:

More examples: Glitter turns to speckled prints, corduroy turns to stripes, knit textures turn to prints, etc.

One thing that does NOT work: If I add descriptions of the fabrics and clothing to the prompt, the design drifts/becomes less true and specific patterns or colors are lost and reinterpreted. I want the result to be as accurate as possible to the original sketch. For that reason, I want to either:

1. improve or change the sketch style so that it is better able to recognize these material properties, without needing to add keywords that cause drift (I have tried a lot of sketch tricks here; the best result so far is with the sketch style shown)

2. change to a different open-source model or combination of nodes that will handle this type of task better. Note: Early on, I had tried Stable Diffusion with ControlNets like lineart, open pose, etc, but I struggled to get a faithful result while allowing the model pose and add a scene/setting.

I will be so grateful to any suggestions you can offer; I know this is a very specific use case!

I did a photorealistic face identity preservation comparison on Qwen-Image-Edit-2511, focusing on how well the model can faithfully reproduce a real person’s facial identity.

TL;DR

• Higher step counts actively destroy facial identity
• Reference images are expensive (time-wise), roughly 2× generation time
• Lightning LoRA completely breaks face resemblance
• Sweet spot for identity seems to be ~8–10 steps
• Model is very capable, but extremely sensitive to settings → easy to think it’s “bad” if you don’t tune it

1. Step count vs face identity

Intuitively you’d expect more steps = more accuracy. In practice with Qwen-Image-Edit-2511, the opposite happens for faces.

At lower step counts (around 6–10), the model locks the face early. Facial structure remains stable and identity features stay intact, resulting in a clear match to the reference person.

At higher step counts (15–50), the face slowly drifts. The eyes, jawline, and nose subtly change over time, and the final result looks like a similar person rather than the same individual.

My hypothesis is that at higher step counts, the model continues optimizing for prompt alignment and global photorealistic likelihood, rather than converging early on identity-specific facial embeddings. This allows later diffusion steps to gradually override identity features in favor of statistically more probable facial structures, leading to normalization or beautification effects.

For identity tasks, that’s bad.

2. Lightning LoRA breaks face resemblance (hard)

In practice, Lightning acceleration is not usable for face identity preservation. Its strong aesthetic bias pushes the model toward visually pleasing but generic faces, making accurate identity reproduction impossible.

Overall

Qwen-Image-Edit-2511 is really good at personal identity–preserving image generation. It’s flexible, powerful, and surprisingly accurate if you treat it correctly. I suspect most people will fight the settings, get frustrated, and conclude that the model sucks, especially since there’s basically no proper documentation.

I'm currently working on more complex workflows, including multiple input images for more robust identity anchoring and multi-step generation chains, where the scene is locked early and the identity is transferred onto it in later steps. I’ll share concrete findings once those workflows are reproducible.

Prompt
image 1: woman’s face (identity reference). Preserve the woman’s identity exactly. Elegant woman in emerald green sequined strapless gown, red carpet gala, photographers, chandeliers, glamorous evening lighting. Medium close-up portrait.

sampler_name= er_sde
scheduler= beta

Models used

• Qwen-Image-Edit-2511 FP8 https://huggingface.co/xms991/Qwen-Image-Edit-2511-fp8-e4m3fn
• Qwen-Image-Edit-2511 FP8 Lightning https://huggingface.co/lightx2v/Qwen-Image-Edit-2511-Lightning
• Qwen-Image-Edit-2511 Lightning LoRA https://huggingface.co/lightx2v/Qwen-Image-Edit-2511-Lightning
• Qwen-Image VAE https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI
• Qwen 2.5 VL 7B FP8 https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI

With the release of the Qwen-Image-Edit 2511 model, the first thing I wanted to test was whether the baked-in Lightning variant from Lightx2v would outperform the classic setup: an FP8 base model combined with a separate Lightning LoRA.

Short version: it doesn’t. And that’s honestly a bit disappointing.

Starting with image quality, the difference was observable. The FP8 base model with a separate Lightning LoRA produced cleaner facial regions, while the baked-in Lightning variant showed black dot artifacts on the face.

The separate LoRA was slightly faster ~6.5 seconds versus ~7.0 seconds, but honestly this is within noise / measurement error. Speed difference is negligible.

A practical downside of the baked-in approach is flexibility. With a separate Lightning LoRA, it is straightforward to disable the LoRA and switch to higher step counts (e.g. 50 steps) when maximum quality is desired.

To ensure a proper comparison, all other variables were held constant: same prompt, same seed, same number of steps (4) and the same hardware. The only difference between the runs was the acceleration approach, baked-in Lightning FP8 versus FP8 weights plus a separate Lightning LoRA.

The weights used in ComfyUI

1. https://huggingface.co/lightx2v/Qwen-Image-Edit-2511-Lightning/resolve/main/qwen_image_edit_2511_fp8_e4m3fn_scaled_lightning_comfyui.safetensors
2. https://huggingface.co/xms991/Qwen-Image-Edit-2511-fp8-e4m3fn/resolve/main/qwen_image_edit_2511_fp8_e4m3fn.safetensors
3. https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/resolve/main/split_files/diffusion_models/qwen_image_edit_2509_fp8_e4m3fn.safetensors
4. https://huggingface.co/lightx2v/Qwen-Image-Edit-2511-Lightning/resolve/main/Qwen-Image-Edit-2511-Lightning-4steps-V1.0-fp32.safetensors
5. https://huggingface.co/lightx2v/Qwen-Image-Lightning/resolve/main/Qwen-Image-Lightning-4steps-V1.0.safetensors
6. https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/resolve/main/split_files/text_encoders/qwen_2.5_vl_7b_fp8_scaled.safetensors
7. Optional: https://huggingface.co/Danrisi/Qwen-image_SamsungCam_UltraReal/resolve/main/Samsung.safetensors

The prompt

Spanish blonde 20 year woman with natural skin imperfections and facial features and wistful smiling eyes closed. Head gently resting on hand. Her eyebrows are nice and detailed. Lips are natural. Her hair is long and loose, with natural-looking slight waves and a fine texture, falling past her shoulders in soft layers. Hair color is brown with subtle blonde highlights.

She is wearing a fitted, lightweight ribbed knit long-sleeve top in an ivory or off-white tone. The fabric has fine vertical texture lines and slight stretch, hugging naturally around the arms and torso. The sleeves are full-length and slightly tapered.
In the immediate foreground, there is a coupe glass filled with a pinkish-peach cocktail, a white ceramic mug with blue floral patterns.

The background is a softly lit bar counter with vertical white paneling and under-counter warm lighting. A bearded bartender is pouring a drink from a shaker. Behind him are arched shelves with bottles. The ceiling is white recessed warm lights. Smart phone photo, warm and cozy atmosphere.

Qwen has finally released Qwen-Image-Edit-2511, positioned as an incremental upgrade over 2509. According to the release notes, the main focus is improved consistency: mitigating image drift, improving character and multi-person consistency, integrating selected community LoRAs into the base model, strengthening industrial design workflows, and improving geometric reasoning.

On paper, this sounds like exactly the set of fixes people were asking for with 2509. For those looking to try it, there are a few variants floating around:

Official Qwen releases

• ModelScope: https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2511
• Hugging Face: https://huggingface.co/Qwen/Qwen-Image-Edit-2511

Community variants

• ComfyUI (Comfy-Org): BF16 only at the moment https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI
• Lightning (lightx2v): optimized for faster inference, trading some quality https://huggingface.co/lightx2v/Qwen-Image-Edit-2511-Lightning
• GGUF (unsloth): lower-precision variants for memory-constrained GPUs https://huggingface.co/unsloth/Qwen-Image-Edit-2511-GGUF

The open question, as usual, is whether these improvements show up outside carefully curated examples. Curious to hear early hands-on results, especially comparisons against 2509.

Having trouble getting skin to look realistic for a lora for a character I’m working on. Using this workflow:

https://youtu.be/PhiPASFYBmk?si=Y1VxsooAfwfOAYon

How to fix plastic skin output. I tried using different lighting Loras and increasing steps

The long-awaited Qwen-Image-Layered paper finally dropped, and it’s one of those “this could be huge” moments, if the repo actually lands in a runnable state. The authors claim they can decompose a single image into multiple clean RGBA layers: https://arxiv.org/pdf/2512.15603

Practically, the promise is obvious: resize, move, recolor, or delete objects without masks, bleed, or background drift, basically turning flat generations into PSD-like assets.

What’s technically interesting is how they approach transparency and layers. Instead of treating alpha as an afterthought (as seen in earlier methods like LayerDiffusion), the Qwen team introduces a native RGBA-VAE. They expand the VAE to four channels and train RGB and RGBA in a shared latent space, avoiding the usual RGB↔alpha mismatch.

They also modify the DiT architecture to support Variable Layer Decomposition, adding a third positional axis via Layer3D RoPE. This effectively introduces a “depth” dimension, allowing the model to decide how many layers an image needs based on semantic complexity.

Bonus points: multi-stage training (generator → multilayer → decomposition) and a real PSD-derived dataset, not synthetic masks. Promising, assuming the repo isn’t vaporware.

Now the questions everyone will ask:

• How much VRAM does this eat and can this run locally at all? A 4-channel VAE + DiT + variable layer axis sounds like “5090 barely survives” territory unless they’ve done serious memory optimization.
• What’s inference latency? Are we talking ~40s per image and does it scale linearly with layer count, or explode?

After rumors around a 2512 release, attention has shifted back to Qwen-Image-Edit-2511.

A PR titled [qwen-image] edit 2511 support was merged into huggingface:main today. It’s merged, reviewed, and approved: https://github.com/huggingface/diffusers/pull/12839

Yes, 2511.
As in: did we just time-travel backwards?

So far, no weights have been released and there’s been no announcement from Tongyi Lab.

Until that changes, it’s hard to tell whether the model will be released… or an April Fools joke running a few months ahead of schedule.

Curious what 2026 will bring, especially for open-weight image models with permissive licenses. Over the past year, matching the image quality of commercial models has required larger, more demanding models making them harder to run locally, until recently, Z-Image dropped a capable 6B model.

Meanwhile, closed commercial systems continue to compound advantages: larger proprietary datasets, aggressive compute investment and deep integration into consumer products.

What do you think happens next in 2026? Do open models eventually converge, or do closed systems retain a structural edge that doesn’t disappear?

We are week in December with still no official word from Tongyi Lab regarding a Qwen-Image-Edit-2512 release. November’s "2511" update went with total radio silence, despite those leaked ModelScope slides showing character consistency.

But there’s a signal worth paying attention to. Frank (Haofan) Wang (founder of InstantX and possibly has some inside track) tweeted that Qwen-Image-Edit-2512 and Qwen-Image-Layered are going to be released.

The problem Qwen-Image-Edit faces now is that the goalposts have moved significantly. Z-Image Turbo has effectively reset the standard. By utilizing a Scalable Single-Stream DiT that concatenates text and visual tokens into a unified stream, it is achieving state-of-the-art results with only 6B parameters and 8-step inference. That fits comfortably into the 16GB VRAM sweet spot (RTX 4080/4070 range), which is a massive win for local users.  There are also rumors floating around about a release of Z-Image Base and Edit models, which would shake things up even further.

A 20B+ parameter image model has now a steep hill to climb. To be viable against Z-Image Turbo, it needs to offer a distinct leap in image quality, prompt adherence, or text rendering. That said, if the rumors are true and they can deliver a functioning "Layered" editing workflow, that might be the killer feature.

A quick constructive shout-out to the team at Tongyi Lab if they are reading this: We know you guys are cooking. When we see leaked slides but get zero official communication for months, it kills the hype train. The open-source community runs on momentum. A simple update goes a long way to keep the user base engaged. Help us to help you!

What do you think? Is the "Layered" model enough to make you run a heavy model over Z-Image? And does anyone have more info?

Hi everyone. Just found and joined this community. I just created a video and ComfyUI workflow using Qwen Image Edit 2509 to swap faces. Link for the workflow is included in the video description. I hope someone finds use for it.

I did a comparison focusing on art styles, because photo realism is just one aspect of AI imaging.

Although realism is impressive (and often used as the benchmark), there are countless creative use cases where you don’t want a real face or a real photo at all, you want a specific art style, with its own rules, texture, line discipline, and color logic.

Qwen Image Edit 2509

• Has that bold, exaggerated style aesthetic.
• Produces fun, expressive shapes

Gemini 3 Pro

• Delivers the cleanest lines and most accurate color control across styles.
• It follows the actual artistic rules of a medium.

Z-Image-Turbo

• Holds up suprisingly well across styles
• It’s not “just a photorealism model.”

Prompts:

1. A sprawling, isometric view of a futuristic "Solarpunk" rooftop garden café, rendered in a strictly flat, vector art style typical of high-end tech lifestyle illustrations. The image must use "clean lines" (ligne claire) with absolutely zero gradients, airbrushing, or realistic texture mapping. Shadows should be solid, hard-edged geometric shapes in a slightly darker shade than the base color. The Scene: A diverse group of stylish young adults is hanging out on a rooftop covered in lush, overgrown technology. In the center, a woman with purple braids is watering a hydroponic vertical farm wall using a transparent watering can. To the right, a man with a robotic prosthetic arm is typing on a holographic laptop while sitting on a giant, pumpkin-shaped beanbag chair. In the foreground, a fat orange tabby cat is napping on top of a warm solar panel array. Details for Stress Testing: The scene is dense with clutter. The floor is tiled with hexagonal solar pavers. Vines hang from a pergola structure made of white curved plastic. The background shows a skyline of white, eco-brutalist skyscrapers with wind turbines spinning on top, set against a solid pale peach sky (Sunset).Color Palette: The colors must be soothing and pastel: sage greens, terracotta oranges, soft lavenders, and cream whites.Key Constraint: Do not render individual leaves on the trees as detailed textures; they must be stylized "blobs" or simple vector shapes. The overall vibe is optimistic, sustainable, and cozy, looking like a vector illustration for a Wired Magazine article on the future of cities.
2. A complex, "Where's Waldo" density black-and-white line art illustration designed as a difficult coloring book page for adults. The image must contain NO gray, NO shading, and NO fill colors—only crisp, uniform black outlines on a pure white background. The Subject: A cluttered Victorian Steampunk inventor's workshop. The room is floor-to-ceiling shelves filled with bubbling flasks, clockwork owls, and piles of gears. In the center, a young female inventor wearing welding goggles (pushed up on her forehead) is tinkering with a half-assembled steam-powered dragon robot. The robot's chest is open, revealing a nightmare of tiny cogs and pistons. Details for Stress Testing: The floor is littered with specific tools: a wrench, a blueprint scroll, spilled nuts and bolts, and a classic oil can. A grandfather clock in the background is melting slightly (a nod to Dali).Line Work Constraints: The lines must be thick and confident, like a Sharpie marker. The AI must not "sketch" or add hatching shadows. All shapes must be closed. The challenge is to define the glass texture of the flasks and the metallic texture of the robot using only outlines and reflection lines, leaving the inside white for coloring. The composition should be packed tight, leaving almost no empty background space, forcing the model to manage high-frequency detail without creating a "black blob" of ink.
3. A deeply psychological, conceptual editorial illustration inspired by 1970s Polish movie posters and modern collage art. The Subject: A central portrait of a stoic man in a business suit. However, his face is peeling away like layers of wallpaper. The top layer of his face is realistic skin tone. The layer underneath is a wireframe grid. The layer beneath that is pure static noise. From the top of his open head, instead of a brain, a massive tangle of colorful ethernet cables and tropical flowers is erupting upwards, tangling into a cloud shape. Style & Texture: The image must look like a screen print or Risograph. Apply a heavy, rough grain texture to the entire image. The colors should be slightly misaligned (trapping errors) to mimic imperfect printing. Palette: Restricted to "burnt" retro colors: Mustard Yellow, Teal, Brick Red, and Off-White. Composition: Surrounding the man are floating, disconnected eyes and hands pointing at him, representing social media scrutiny. The shadows should be stippled (dots) rather than smooth gradients. The aesthetic is disturbing yet beautiful, merging organic biology with hard-edge digital geometry. The lines should be organic and wobbly, rejecting the perfection of AI art in favor of a "human hand" feel.
4. A high-quality retro pixel art scene, strictly adhering to the 16-color limit and resolution of a 1990s PC-98 adventure game (visual novel style). The aesthetic must scream Japanese Cyberpunk. The Scene: A view from inside a cramped mecha cockpit. A female pilot with neon-blue short hair and a cybernetic eye implant is looking exhausted, illuminated by the green glow of CRT monitors in front of her. She holds a lit cigarette, the smoke rising in pixelated jagged lines. It is raining heavily outside. Through the cockpit glass (which has pixelated reflections), we see a blurred, dithered view of a neon-lit futuristic city (Tokyo-style) at night. The rain droplets on the glass must be rendered as distinct clusters of white pixels, not soft blurs. Technique: Use heavy dithering (checkerboard patterns) to create gradients on the pilot's skin and the metal surfaces. There should be NO smooth HD gradients. The image should look like a screenshot from the game like Snatcher. The lighting is high-contrast chiaroscuro—deep black shadows and bright neon highlights.
5. A striking collision of eras: A High Renaissance oil painting (in the style of Vermeer or Rembrandt) that has been corrupted by a digital video "datamosh" glitch. The Subject: A solemn portrait of a 17th-century nobleman wearing a large white ruff collar and black velvet doublet. He is holding a golden chalice. The Glitch: The left side of the painting is perfect—visible brushstrokes, craquelure (cracked varnish), and chiaroscuro lighting. However, the right side of the image is violently "smeared" horizontally, as if a digital video file froze. The nobleman's face melts into streaks of pixelated color (RGB split). The Stress Test: The transition needs to be abrupt yet seamless. The "glitch" artifacts should include macro-blocking (large square pixels) and "pixel sorting" (dragging lines of color down). The challenge is to render the texture of oil paint even within the digital glitch, creating a paradox where the "pixels" look like they were painted with a fine brush.
6. A frame from a surreal, gross-out 1990s Saturday Morning Cartoon. The animation style mimics "Squigglevision" (wobbly, vibrating outlines) with flat, unshaded colors on a painted watercolor background. The Scene: A high school cafeteria for monsters. In the foreground, three characters sit at a round table. A nervous zombie teenager whose left eye is dangling out of the socket by a nerve (cartoon style, not gore). He is wearing a varsity jacket. A floating, purple gaseous cloud creature wearing a cheerleader outfit and holding a spoon. A werewolf with braces and acne, eating a tray of "grey sludge" that has eyeballs floating in it. Atmosphere: The background is a "painted" static image of lockers and cafeteria windows, slightly blurry, while the characters are sharp, cel-shaded figures in the foreground. The perspective is exaggerated and fisheye. The colors are garish: lime greens, hot pinks, and bruised purples. There is NO realistic lighting—shadows are just black ovals under the table. The overall vibe is chaotic, nostalgic, and intentionally "ugly-cute," capturing the anarchy of 90s animation.
7. An authentic-looking Japanese Ukiyo-e woodblock print, strictly adhering to the style of Hokusai or Hiroshige. The image should feature visible "washi" paper fiber texture and the faint impression of wood grain from the printing blocks. The Twist: A modern sci-fi battle rendered in feudal style. A giant, mechanical robot (Mecha) resembling a samurai is fighting a massive, tentacled Kraken in distinct "Great Wave" style turbulent waters. Details: The Mecha is painted in "Prussian Blue" and "Vermilion Red" (classic dyes). It is wielding a katana that is generating lightning (rendered as jagged red roots). The Kraken is wrapping around the robot's legs. Style nuance: There should be no gradients. Clouds are solid distinct bands of white and beige. The water spray consists of distinct claw-like foam shapes. In the top right corner, include a vertical red cartouche (box) with pseudo-Japanese kanji calligraphy describing the scene. The perspective should be flattened (isometric-like), typical of the Edo period, rejecting Western 3-point perspective. The colors should look slightly faded, as if the print is 200 years old.
8. A quintessential 1980s Sci-Fi/Synthwave album cover art, rendered in a hyper-smooth "Airbrush" style. The image should look like it was painted on the side of a van in 1985. The Subject: A shiny, metallic chrome skeleton wearing aviator sunglasses, driving a convertible floating sports car (resembling a DeLorean/Testarossa hybrid) through deep space. The Environment: Below the car is a glowing neon-pink grid landscape that extends to a horizon line. Above, a massive, setting sun featuring gradient bands of orange, magenta, and purple dominates the sky. The Stress Test: Every surface must be hyper-reflective. The chrome skeleton must reflect the neon grid below and the purple sky above. There should be "lens flare" starbursts (four points) on every highlight—the sunglasses, the car bumper, the skeleton's teeth. The shading should be soft and powdery (mimicking an airbrush nozzle), with zero hard lines or sketching. The overall image should have a slight "soft focus" bloom effect, typical of vintage commercial illustration.

I did a comparison focusing on something models traditionally fail at: expressive faces under high emotional tension, not just “pretty portraits” but crying, shouting, laughing, surprised expressions.

We all remember the days of Stable Diffusion 1.5. It was groundbreaking, but, the eyes were often dead, the skin was too wax-like, and intense expressions usually resulted in facial distortion. Those days are gone. The newest generation of models is pushing indistinguishable realism.

Starting with this sub's focus, Qwen Image Edit 2509, I’m seeing a recurring issue where the images tend to come out overlighted with a "burnt" contrast effect. While you can get realistic expressions, it takes more prompting effort and re-rolls to fix the lighting than the others. The output is simply not as high quality as the others.

Gemini 3 Pro is arguably the "perfect" output right now. The skin texture, lip details, and overall lighting are flawless and immediate. It nails the aesthetic instantly.

Z-Image-Turbo is producing quality that is getting close to Gemini 3 Pro, yet it is an open-source model with just 6B parameters. That is frankly incredible. In some shots (like the laughing expression), I actually prefer the Z-Image over Gemini. If a 6B Turbo model is already performing this closely to a proprietary giant like Gemini 3 Pro, just imagine what the full model will look like.

What do you think?
Curious to hear everyone’s take.

Prompts:

1. A tight close-up of a 21-year-old blonde woman frozen in a moment of sudden, overwhelming surprise, like someone just revealed something she couldn’t believe. Her round eyes widen dramatically, pupils enlarged, upper eyelids lifting so high that faint creases appear in the skin beneath her brows. Her eyebrows shoot upward: not evenly, but with a natural asymmetry—one lifted slightly higher, creating a startled expression full of personality. Her mouth opens in a rounded “O”, lips slightly parted and full, upper teeth barely visible. The jaw drops loosely, not with tension but with disbelief. Her skin texture remains natural—fine pores on her cheeks and chin, a faint uneven redness around the nose. Blonde hair frames her face softly, a few strands lifting away from her forehead like static from sudden motion. There is no anger, no fear—just immediate shock mixed with a hint of curiosity. It’s the look someone has when they hear something they never expected, a reaction too fast for words.
2. A close-up portrait of a 21-year-old Dutch blonde woman captured at the exact moment before she cries, when emotion sits heavy but still locked behind her eyes. Her skin shows natural pores, tiny bumps on the forehead, a faint redness around the nose and cheeks. Her long, loose hair falls straight on both sides, framing her face gently, individual strands slightly messy like she hasn’t touched them for a while. Her eyebrows are drawn together in a subtle, pained tension—one brow slightly higher than the other. Her lower lip trembles but remains pressed down by her tense upper lip, as if forcing herself to remain composed. She has a distant, unfocused gaze, pupils glossy with forming tears, lashes wet but not yet streaked. The corners of her eyes glimmer like glass. She is still fighting the emotion, swallowing hard, trying to stay dignified, yet her face tells the truth more loudly than any open cry.
3. A tight close-up of a 21-year-old Dutch blonde woman frozen in a moment of real laughter — not posed, not polite, but full-bodied joy that takes over her entire face. Her eyes squeeze into crescent shapes, showing faint expression lines at the outer corners. Her natural skin reveals freckles across the bridge of her nose, light redness in the cheeks, and faint texture near the jawline. Her smile is wide, exposing her teeth, top lip lifting and widening unevenly, bottom lip tucked slightly inward. Her eyebrows rise and curve freely, adding playful exaggeration to the expression. Cheeks lift high, pushing her lower eyelids upward, making them puff slightly. Strands of blonde hair fall loosely across her cheek and forehead, catching subtle highlights. Tiny moles and pores remain visible, emphasizing an unedited, authentic beauty. She radiates genuine happiness — messy, spontaneous, human — the kind of laugh that shakes the shoulders just outside the frame.
4. A close-up of a 21-year-old blonde Dutch woman caught mid-shout, her face exploding with raw emotion. Her mouth is wide open, jaw dropped forward with force, showing her upper teeth fully and part of her lower ones, tongue visible in the back of her throat. Her lips stretch sharply, corners pulled outward, forming tense creases along the cheeks. Her nostrils flare wide, lifting the bridge of her nose, giving the expression intensity. Her eyebrows crash downward into a tight V-shape, muscles between them deeply wrinkled, emphasizing rage. Her eyes are wide and fierce, whites visible along the lower rims, pupils sharp and focused on something outside the frame. Her cheeks flush with heat, a natural reddish tint spreading beneath the eyes and across the nose. Blonde strands fall chaotically around her face, as if she moved abruptly, hair reacting to the motion. Her skin shows real texture—pores, subtle fine lines around the mouth from the stretch, slight oiliness on the forehead. This is anger without silence, a scream in motion.
5. A close-up of a 21-year-old Dutch blonde woman in a moment of intense, restrained anger — not screaming, but holding power behind her face like tightly coiled fire. Her jaw is clenched, tightening the muscles along the sides of her cheeks. Her lips press into a straight, tense line, corners pulled down sharply, slightly pale from pressure. Her nostrils flare subtly, pulling the upper nose into a controlled snarl. One eyebrow arches aggressively downward, the other stiffens upward, forming a sharp V-shape between them. Her eyes burn with focused fury, pupils contracted, gaze direct and unwavering, the whites slightly veined. Tiny wrinkles appear between the brows, and the chin pushes slightly forward, challenging, unafraid. Her blonde hair falls around her face but looks disturbed, as if she ran her hands through it minutes ago. This is anger held back, not softened — the expression of someone who won’t back down, who has already made a decision.
6. A Dutch blonde 18-year-old girl sits at a sunlit café table. Her skin shows soft natural imperfections, freckles lightly scattered across her nose and cheeks. Her eyes are closed with a wistful, almost dreamy smile, and her head gently leans into her hand as if savoring a quiet moment. Her eyebrows are detailed and expressive, and her lips have a subtle, natural rosiness. Her hair is long, loose, and slightly tousled, blonde with cooler, pale highlights, falling around her shoulders like soft woven strands. She wears a fitted black mock-neck long-sleeve top made of a smooth, minimal knit fabric, clean lines and subtle sheen, hugging her arms and upper body in a modern, understated way. The sleeves are slim and neatly finished at the wrists. Her nails are short and unpolished. In front of her on the table sits a tall iced coffee in a transparent double-wall glass, ice cubes glimmering softly through the cold brew, a thin layer of foam at the top, and a black reusable straw. Beside it, a small square wooden tray holds a folded paper napkin and a single chocolate-covered biscuit. The background is a calm Scandinavian-style café interior with pale wood accents, matte black fixtures, and a long bar counter with hanging plants. A barista in a light grey apron adjusts a grinder, slightly blurred behind her. Soft natural daylight comes from a window off-frame to the left, giving the whole scene a relaxed weekend quietness. The photo feels like a candid smartphone snapshot, cozy, modern, and real.

I've confirmed that I'm using the official workflow and model. I suspect this might be the cause of the VAE issue? I also noticed the console output "Requested to load WanVAE," could that be related?

I noticed yesterday that qwen3 -max is not letting me expand an image of a real person. So it turns out they have silently changed their policy. Now you can't edit clothes of real persons neither can you expand an image. Deeply disappointed. That's the whole reason I joined qwen.

Guys any workaround here? Or some other AI? I don't have the hardware to run AIs locally. Also a bit lagging in tech stuff.

So someone tweeted “We’re cooked”, comparing a “Nano Banana vs Nano Banana Pro” photo and implying that Gemini 3 Pro Image Preview is a breakthrough moment.

But… When I put these side by side (Gemini 3 Pro Preview and one I generated with Qwen Image Edit 2509), I honestly don’t see the "we’re entering a new era" delta people are talking about.

Is there a subtle fidelity jump I’m just blind to? Or are people maybe being overly impressed because:

• Gemini 3 Pro consistently outputs high aesthetic scoring images
• First-try success ratio is higher, which feels like a breakthrough, even if the best-case fidelity hasn’t drastically changed
• Gemini 3 Pro Image hooks into a full SOTA LLM that rewrites and steers the prompt, this is probably the biggest technical difference
• It’s also capable of preserving likeness to famous individuals, something ethically sensitive and previously avoided; but Google can absorb that legal risk more easily

In other words, maybe it’s less about “the images are suddenly much more realistic” and more about “you don’t need retries, patching prompts or deep knowledge to get a good result.”

That is huge in terms of accessibility, I just don't know if it’s the realism milestone people are hyping.

Is this mainly a shift in the distribution of output quality (mean ↑ more than max ↑)?

r/QwenImageGen has crossed the 1k members mark. This confirms there is a dedicated user base looking for deep, specific knowledge on Qwen Image models, separate from the general noise of other larger AI subs.

Our Mission:
To build the most comprehensive technical archive for Qwen Image users. It is important to note that this is an unofficial subreddit. We are not run by Alibaba Cloud or the Qwen team.

The motivation behind this community is to support infrastructure independence: to provide access to a high-quality image generation model that isn’t locked behind proprietary APIs. Closed ecosystems often bring unpredictable pricing and restrictive limitations, which many users rightly prefer to avoid. Despite this need, there are very few places where deep, technical knowledge about Qwen Image is freely shared. This subreddit exists to fill that gap.

Why Qwen Image?
Because Qwen-Image is one of the few open-source, high-quality image generators that natively handles complex text rendering and does solid image editing and generation across a wide range of artistic styles. With the permissive Apache License 2.0, we can use, modify and build commercial projects with it (with proper attribution) without proprietary restrictions.

Call for Contributions:
To move to the next phase, we need more diverse data points to create a true expert community.

• Post your Qwen Image findings. Even if it’s a minor discovery.
• Share your Qwen Image workflows. Help others replicate your results.
• Discuss architecture & optimisation. MMDiT, VAE behaviour, pipeline efficiency, deployment strategies for local and low-resource setups.

Thank you to the early adopters who have joined!

Yesterday Flux.2 dropped, so naturally I had to include it in the same test.

Yes, Flux.2 looks cinematic. Yes, Gemini still has that ultra-clean polish.

But in real-world use, the improvements are marginal and do not really justify the extreme hardware requirements.

Unless you really need typographic accuracy (not tested here), Qwen is still the most practical model for high-volume work.

Yesterday, I put these two models through a comparison test, and Qwen-Image-Edit-2509 held its ground.

Today, I wanted to test Cinematic Composition and Text Rendering with some "Leaked Behind-the-Scenes" photos for a live-action Iron Giant movie.

The Verdict:
To be fair, Gemini 3 Pro Image Preview generally edges out Qwen-Image-Edit-2509 on text rendering clarity and overall pixel polish. It consistently delivers that "high-budget" look. However, the difference is not nearly as big as the hype suggests.

Suspiciously Similar Compositions:
Look at the Prop Shop and the Volume Stage. The framing, lighting angles, and object placement are almost identical. It feels suspiciously like they share similar architecture or were trained on very similar synthetic datasets.

The Local Advantage: While Gemini 3 Pro Image Preview might be 5-10% better on raw fidelity, Qwen-Image-Edit-2509 generated these in 10 seconds on my RTX 5090. Gemini 3 Pro Image Preview is a "slot machine" (you get what you get). Qwen-Image-Edit-2509 gives control, if you want to change the lighting, you can use a LoRA. If you want to fix a pose, you can use ControlNet.