ausboss/Qwen-Image-2.1-Outpaint-LoRA

Qwen Image 2.1 Outpaint LoRA

Extends a picture in any direction: one side, two sides, a corner, three sides, all four, or a small picture on a big empty canvas. Pad the picture with flat gray #808080, give the padded canvas to Qwen Image 2.1 as the reference, and the LoRA fills the gray with a continuation of the scene while keeping the original picture where it is.

Ready-made ComfyUI workflows: Qwen Image 2.1 Outpaint and, for a tilted picture, Qwen Image 2.1 Rotate + Outpaint, both on Civitai. This LoRA on Civitai: Qwen Image 2.1 Outpaint LoRA. Videos on YouTube @ausbossdev.

Pictures neither LoRA was trained on, rendered in ComfyUI with the INT8 Qwen Image 2.1 model: 25 steps, CFG 1, seed 42, the same auto-caption prompt in every column, original pixels pasted back with Stitch Inpaint. Each row: padded input | no LoRA | v1 | v2. The amber box marks what goes wrong without the LoRA: visible seams where the original is pasted back, and a duplicated person.

Files

file	notes
qwen-image-2.1-outpaint.safetensors	v1, step 1500: any extension size, trained at ~1 MP
qwen-image-2.1-outpaint-v2.safetensors	v2, step 2000: everyday extensions at 1-2 MP, people and outfits
checkpoints/qwen21_outpaint_v1_000001250.safetensors	step 1250, practically identical
checkpoints/qwen21_outpaint_v1_000000500.safetensors	step 500, already fixes framing; plainer fills

Both are rank 32, ComfyUI key format (diffusion_model.transformer_blocks.*), all 384 tensors load onto the Comfy-Org Qwen Image 2.1 weights.

v1 or v2

Both fix the same thing (the picture stays in place, so the stitch is clean) and measure almost the same. v2 is a little better on small and medium extensions and was trained at 1-2 MP on a people- and fashion-heavy set; v1 saw more extreme zoom-outs.

unseen pictures	no LoRA	v1	v2
subtle crops (10 pictures, ~75 % kept): picture stays in place	16.4 dB	34.6 dB	34.7 dB
subtle crops: fill error vs the real photo (lower is better)	16.3	10.5	10.2
big crops (8 pictures, 36-50 % kept): picture stays in place	25.5 dB	33.9 dB	34.0 dB
big crops: fill error	29.8	27.0	27.2

Prompt

The instruction is the trigger. Put it first:

Outpaint the image: replace the solid gray areas with a seamless continuation of the scene, keeping the existing picture unchanged.

Optionally follow it with Scene: <description>. The training captions described the whole picture, so a description of the source (what is visible) works well; in ComfyUI, Text Generate with the Qwen3-VL 8B encoder you already load for Qwen 2.1 can write it automatically from the picture.

ComfyUI

The quickest start is the ready-made workflow on Civitai. To build it yourself:

1. Pad the picture with flat gray #808080 (AusBoss Load Image + Pad: fill color, #808080, canvas multiple 32, target 1.0 MP for v1, 1-2 MP for v2). For a tilted picture, use Image Crop + Rotate + Pad with feather 0 instead (see Tilted pictures).
2. Text Encode Qwen Image 2.1: the padded canvas as image_1, resolution 0 (reference and output share the canvas size), plus the prompt.
3. KSampler on the encoder's latent output: 25 steps, CFG 1, euler / simple, denoise 1.
4. VAE Decode -> Split Image with Alpha (the Qwen 2.1 VAE decodes RGBA) -> AusBoss Stitch Inpaint to paste the exact original pixels back over a 32 px feather.

Do not pin the known area with Set Latent Noise Mask: on Qwen 2.1 that draws a visible rectangle at the seam. The LoRA keeps the picture in place on its own.

If the LoRA file is missing, the Civitai workflow stops at a red LoRA Loader. Download the file, or switch its row off to run plain Qwen 2.1 on purpose. If your results show a lighter box or doubled edges where the original was pasted back, check that the LoRA row is on.

What v1 fixes (12 held-out pictures, ComfyUI renders)

	base Qwen 2.1	+ LoRA 500	+ LoRA 1250	+ LoRA 1500
gray left unfilled	9.1 %	1.1 %	1.0 %	0.9 %
kept-area PSNR (picture stays in place)	23.6 dB	33.6 dB	33.6 dB	33.5 dB
mean colour step at the seam	+2.2	-0.8	-0.7	-0.6

Without the LoRA, Qwen 2.1 often reframes or rescales the picture (a fisheye kitchen shrank inside its frame, a street scene was recomposed) or returns a gray frame untouched. With it, the known picture stays pixel-registered, which is what makes a clean stitch possible. (The "gray" metric counts any flat mid-gray, so gray pavement or sky can read as a few percent.)

Tilted pictures

Straightening or tilting a picture before outpainting leaves gray wedges in the corners instead of straight borders. Neither LoRA was trained on diagonal borders, and both handle them.

36 ComfyUI renders: a sports photo, a flat lawn and the pier above, each rotated 17° with AusBoss Image Crop + Rotate + Pad, 2 seeds, feather 24 and 0, the same prompt and seed in every column, original pasted back with Stitch Inpaint.

rotated 17°	no LoRA	v1	v2
renders where the picture did not stay in place near the tilted edges (> 2 px)	12 / 12	0 / 12	0 / 12
largest shift (sports photo, pier)	95 px	0 px	0 px

Without the LoRA, Qwen 2.1 moves the scene while it fills the wedges, so the pasted-back original no longer lines up: a lighter tilted box, cut or doubled edges and ghosted objects along the tilt. With either LoRA the picture stays exactly in place.

Set feather to 0 on Image Crop + Rotate + Pad (AusBoss nodes 2.2.0). Its feather also fades the picture itself into the gray fill (Load Image + Pad only feathers the mask). The model paints that ramp as a darker band along the tilt, and Stitch Inpaint's color match reads the faded edge and pulls flat areas grayer. Stitch Inpaint still blends the paste-back over 32 px at feather 0.

Training

v1

• ostris/ai-toolkit, arch: qwen_image_2, Comfy-Org INT8 convrot base (the same weights ComfyUI runs), text encoder embeddings cached with the reference image.
• 924 pairs from 231 pictures, 4 layouts each: all-sides frame, one side, opposite sides, corner, three sides, small window (12-30 % kept). Target = the picture at <= 1 MP on the /32 grid (never upscaled), source = same canvas with everything outside the kept rectangle painted #808080.
• Captions: the instruction (70 %) or one of five paraphrases (30 %), and for 75 % of pairs Scene: + a Qwen3-VL-8B description of the whole picture. Caption dropout 0 (in this trainer a dropped caption also drops the reference).
• Rank 32 / alpha 32, AdamW8bit, LR 1e-4 constant, batch 1, shift timesteps, 1500 steps. Converged by about step 1250; step 500 already works.
• Pictures: a hand-picked set plus openly licensed images: Flickr photos from CommonCatalog CC-BY, museum art from PD12M (CC0 / public domain), anime from anime-with-caption-cc0.

v2

• 68 hand-picked pictures: 47 of people and outfits (Unsplash photos under the Unsplash License via unsplash-lite, plus the author's own), 21 of scenes, interiors, anime and paintings. One random layout per picture, 68 pairs.
• Smaller extensions than v1: the kept picture covers 45-93 % of the canvas (median ~74 %), about one layout in seven bolder; no small-window zoom-outs.
• Targets at 1-2 MP (never upscaled), ai-toolkit resolution: 1408.
• Same captions recipe, rank 32 / alpha 32, AdamW8bit, LR 1e-4, 2000 steps. Works from step 500; step 2000 had the lowest fill error.

Limits

• Very large extensions (the kept picture under ~15 % of the canvas) invent a lot; results vary more by seed.
• v1 was trained at about 1 MP, v2 at 1-2 MP; much larger canvases are untested.
• Text in the new area is plausible, not legible.
• On shallow-focus photos, tilt wedges come out a little softer than an in-focus subject: the model continues the blur it sees. Qwen 2.1 without the LoRA, Krea 2 and FLUX.2 Klein did the same in the same test.

License

A LoRA for Qwen Image 2.1, which is released under the Qwen Research License; use of the base model, and of this LoRA with it, follows that license.