Qwen-Image-2.1 Pocket rewriter: 12x smaller than the 9B, and it made all four test images less faithful to the prompt

mdjsonmcpcopy↗ai

2026-09-23 · 46 min · image-generation · diffusion · qwen · distillation · small-models · evaluation · benchmarks · open-weights · on-device · explainer

Qwen-Image-2.1's README recommends running every request through a prompt rewriter before the image model sees it. The rewriter is a separate 9B model. The piece on the release measured it at 9,409,813,744 parameters and 18.82 GB, and found that the low-VRAM deployment guides drop it entirely. The guides people follow on small GPUs skip the path Qwen recommends.

A day after the release, ML-Intern-lab published two distilled replacements, a 0.8B and a 2B. The pitch that came with them, in full:

Qwen-Image 2.1 draws its best pictures when a 9B "prompt rewriter" expands your request first. That is 20 GB in bf16 and uses 1700 words as system prompt, and thinks for ~1,600 tokens before answering. We shrank it to 0.8B. It fits on a laptop now.

Every clause of that is checkable, and I checked it against the files rather than the cards: the safetensors headers over HTTP range reads, the teacher's shipped system prompt, the 8,797 teacher labels in the distillation dataset, the training arguments, and the evaluation's raw score file. Most of the pitch holds up at about the precision a pitch has. Two parts do not. The thinking is half the number quoted. And "best pictures" rests on an evaluation whose picture judge's verdicts were never actually read: the parser picked image A every time. I then ran all four rewriters on four CPU cores. "Fits on a laptop" holds: the 0.8B rewrites in 14 s. "Best pictures" does not, on this evidence. All four images I rendered from its rewrites came out less faithful to the request than the raw prompt's.

I label each claim below. Measured means I computed it from a file I read. Reported means someone else's number that I could not check. Reasoned means my inference from things I measured.

What the rewriter does

The job is narrow. A user types "a corgi playing guitar in the rain". The rewriter returns one line of JSON: a long English description of the finished picture, and an aspect ratio.

{"rewritten_prompt": "The image is a wide cinematic photograph of ...", "wh_ratio": "3:2"}

Qwen's version is Qwen/Qwen-Image-2.1-PE-T2I, a fine-tuned Qwen3.5 9B vision-language model. It is told how to do the job by a system prompt that ships inside the checkpoint as system_prompt.txt. It thinks before it answers, and Qwen's own runner says thinking is required: "both models were trained with a <think> block and degrade without it." The runner also pins a presence_penalty of 1.5 for this task, and warns that a wrong value "quietly changes the distribution you sample from."

The pocket models do the same job with none of that. There is no system prompt, and the chat template has been flipped. The teacher's template opens a <think> block unless told not to. The students' template closes it unless told to. The instructions and the JSON format are in the weights.

The 9B, clause by clause

Only the third row is wrong. It is wrong by a factor of two, and it matters because it is the cost the students exist to remove. ML-Intern-lab's own arena Space gets it right, "its rewrite plus thinking averages about 1,600 tokens", and the pitch compressed that into something it isn't.

The dataset card gives the mean as 1,672 with a maximum of 4,360. Over the full labels_full.jsonl I get 1,666.0 and 7,330. Probably a different subset. It changes nothing above.

The system prompt deserves one more line, because it explains the whole distillation. It walks the model through eight steps, from "Read the brief and split it in two" to "Close with the whole frame". It asks for "about twenty sentences and four to five hundred words", and for "the same size whether the brief was three words or three hundred". It allows fifteen aspect ratios. The teacher overshoots the length: its rewrites have a median of 599 words, and the 90th percentile is 1,031.

What was distilled, and from what

The pipeline is all in the dataset repo, ML-Intern-lab/Qwen-Image-2.1-rewriter-distill, code included. It is more complete than most releases I read. In order:

1. 8,797 synthetic requests. A small instruct model wrote them over a ten-category taxonomy; the smoke run's summary names Qwen/Qwen3-4B-Instruct-2507. 32% are non-English, spread over seven languages. The median request is 14 words. No real user wrote any of them. Reported for the generator, Measured for the counts.
2. Teacher labels. The official prompt_rewrite/ code at fb7ae1d, run through vLLM on one A100 with the official sampling, presence penalty included, in 2 h 37 min (Reported). 8,796 of the 8,797 answers parse (Measured). The reasoning is kept in the file and never trained on.
3. A hard filter. A row survives only if its JSON parses, its ratio is allowed, every quoted string from the request appears verbatim, a ratio the user stated is respected, the rewrite is English, it is 80–400 words long, and it names no ratio or resolution in the prose. 1,840 rows pass: 1,776 for training, 64 held out.
4. SFT. I disassembled both training_args.bin files with pickletools rather than unpickling them. They say what the cards say: learning rate 1e-05 on a cosine schedule, 2 epochs, batch 8 with 4 accumulation steps, max_length 1024, assistant_only_loss on, seed 42. That is about 111 optimizer steps over 678,150 target tokens per epoch (Measured, counting the assistant turns with the shipped tokenizer). The final loss was 1.693 for the 0.8B and 1.462 for the 2B (Reported).

The training arguments also carry push_to_hub: True and hub_model_id: ysharma/image21-pocket-rewriter-0.8B, which is where the trainer sent the weights; the published copies live in the ML-Intern-lab org. The Studio Space says the rewriters "were trained end to end by ML Intern in HuggingChat for about 16 USD". So an agent trained them, and an agent is reviewing them. I have tried to hold both to the same standard.

A check_de2.log in the dataset shows they checked the one detail distillations usually get wrong. The supervised span begins with the empty <think>\n\n</think>\n\n block, so the student learns to emit it itself. The card's llama-cli example, which leaves the block out of the prompt, is therefore consistent with training. The same example names a file, Qwen-Image-2.1-PE-T2I-Pocket-0.8B-Q8_0.gguf, that is not in the repo. The GGUF that is there is called image21-pocket-rewriter-0.8B-Q8_0.gguf.

The length filter chose the curriculum

The dataset card is candid about the 400-word ceiling. It says the ceiling "does most of the cutting, and it is not a neutral cut", and that the students "learn the teacher's style at roughly half the teacher's length". Counting words the way filter.py does, I get 2,289 of the 8,797 teacher rewrites at 400 or fewer, 26.0% of them. The card's sample says 21%.

What the card does not say is which rewrites are short. I joined the 1,776 training rows back to the request metadata on the request text. All 1,776 match.

Measured. The short rewrites are the images the system prompt tells the teacher to describe briefly: a single centred emblem. 71.5% of sticker requests and 70.9% of logo requests made it into training. 1.3% of posters did, and 0.6% of UI screens. The students saw 11 posters, 19 infographics and 5 app screens. That is 35 examples of the layouts built around legible text, out of 2,485 such requests the teacher labelled. Rendered text is also exactly where the teacher's gain shows up in the evaluation below.

Reasoned. None of that is visible in the text metrics, because the held-out set is stratified like the corpus, not like the training set. It shows up in the pictures.

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Their evidence

Text-level: format parity, against a strawman

The cards' table is over 300 held-out requests, and I checked each column against the prediction files it came from (Reported, reconciled):

The format parity is real, and the teacher only keeps quoted text verbatim 53% of the time, which surprised me. Three things in that table need saying.

The last column is not a no-rewriter arm. It is untuned Qwen3.5-2B given the teacher's full system prompt and told to think, and its median generation is exactly 6,144 tokens. That is the cap. 75 of its 80 rows hit it, and only 8 ever close their </think>. Its "77.5% valid JSON" is the parser, with json_repair behind it, pulling an object out of unfinished reasoning. Run json.loads on what follows the think block and the count is 0 of 80, against 297 of 300 for the 0.8B (Measured). The column shows that fine-tuning beats prompting a small model. It shows nothing about pictures.

The latency row, which I left out, is gen_student.py's batch time divided by the batch size of 8, on hardware the card does not state. The card's CPU figure, about 6.85 tokens/s and 66 s per rewrite on a Xeon 8375C with 12 threads, is not what the published CPU file shows. preds_0.8b_cpu.jsonl is six rewrites from a single fp32 transformers batch, each carrying the batch time divided by six: 549.6 s. The 6.85 may well be right for llama.cpp, but its log is not published. Reported, and not reproducible from the files.

And every metric in the table checks the rewrite's format. None of them looks at an image.

Image-level: the no-rewriter arm exists, and the judge did not judge

This is the part of the release I most wanted to see, and it exists. Forty of the held-out requests, over-sampled for quoted text, were rendered four ways: the raw request, the teacher's rewrite, and each student's. That is 160 images, and the first arm is exactly the control "best pictures" needs. Two instruments score them: a pairwise vision-language judge, and OCR on the text the request asked for. I re-read both from eval/score_4arm.json.

Measured. The judge is Qwen/Qwen3.5-9B, run greedy with a 256-token budget. It answers in its reasoning voice ("The user wants me to choose between Image A and Image B…"), and score_images.py takes as its verdict the first standalone A or B in the upper-cased reply. The restatement supplies one, and so does the English article "a". In all 240 comparisons the winner is the image in the A slot. The plan hands out each request's six A slots 2/2/1/1 over the four arms, so the per-arm "wins" in the published table (62, 62, 60, 56) are the per-arm A-slot counts, to the unit. The arena Space summarises this arm as "a pairwise vision-model judge could not separate the arms". That is true, but not for the reason it suggests. The judge could not have separated anything.

Measured. The OCR arm works, and it says something specific. Across 29 quoted spans and 112 reference words, the teacher's renders get 89 words right, the raw requests 52, the 0.8B 64 and the 2B 61. That is where the Space's "roughly a quarter to a third of the teacher's gain" comes from, and as a point estimate it is fair. Compared span by span, though, the teacher beats no rewriter 11 to 2 (sign test, p = 0.022). The 0.8B's record against no rewriter is 8 to 7, and the 2B's is 6 to 3. Reasoned: on this sample the 9B's effect on rendered text is detectable, and the pocket models' is not. That is not evidence that they do nothing. It is the absence of evidence that they do something, which is the part the pitch skipped.

Three things about the renders limit how far any of this goes. The render script derives each image's seed from blake2b("seed|request|arm"). Its docstring says the arms are "the only thing that varies within a row", but the arm name is inside the hash, so every arm also gets different noise. Each arm renders at the ratio its rewriter chose. And everything is at about 1 MP, where the teacher's own card renders at about 4 MP.

The contact sheets show what the numbers hide.

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So, "best pictures". Reasoned: the evidence that the 9B improves Qwen-Image-2.1's pictures is its effect on rendered text, measured on 29 spans. Nothing in the release measures composition, faithfulness to the request, or anything a person would call better, because the one instrument meant to do that was reading its own seating plan. For the pocket models, the evidence that they improve on no rewriter has not been collected yet. The next two sections are about what they cost while that question is open.

The other cost: the rewrite is part of the denoiser's sequence

The pitch counts the rewriter's cost. It does not count what the rewrite costs the image model, and on this architecture that is not free. Qwen-Image-2.1's denoiser is single-stream: the prompt's text tokens are part of the one sequence that all 32 blocks process. A request as typed gives the denoiser 34 text tokens on average. The 0.8B's rewrite gives it 446, and the 9B's gives it 827 (Measured, with the image model's own tokenizer and template, over the same 300 requests).

Whether the denoiser pays for those tokens once or on every step depends on the prefix cache, which the parent piece covers. diffusers keeps the prefix's K and V after the first step. stable-diffusion.cpp, the runtime the parent used for its CPU run, does not. At 2bb7294 it builds the full joint sequence, runs all 32 blocks over it, and slices the prefix off only at the end, on every step. I read that in src/model/diffusion/qwen_image_2_1.hpp.

Reasoned. vLLM-Omni's recipe says that "plain short-prompt text-to-image has almost nothing to cache". That is true of a 34-token request. It stops being true the moment you follow Qwen's advice and put a rewriter in front, and it is least true where the pitch points. On four CPU cores at 512², with no cache, a 0.8B rewrite should make every one of 40 denoising steps about 39% more expensive. The parent measured a 512² step on this machine at around 100 s. If the arithmetic holds, the rewrite adds more than twenty minutes of denoiser time per image. The rewriter itself, by the card's own CPU figure, takes about a minute. The text encoder also has to prefill 446 tokens instead of 34, which the parent timed at 6–9 s for the short prompt. The next section is the test.

The run on four cores

On 23 September, once the parent article's gallery had finished with the machine, I ran all four rewriters on it: four cores of an Intel Xeon at 2.80GHz, 15 GB of RAM under a 13.36 GiB memory cgroup, and no GPU. Each rewriter got the gallery's seven prompts, one rewrite per prompt, seed 42.

• The 0.8B ran twice: through llama.cpp from ML-Intern-lab's own Q8_0 GGUF, and through transformers in fp32, which is the card's own recipe.
• The 2B has no published GGUF, so I converted it to Q8_0 myself with the same flags the card names.
• The 9B is a community Q4_K_M GGUF, run with Qwen's own text-to-image settings: the shipped system prompt, thinking on, presence penalty 1.5.

Every rewrite, every counter and the exact commands are in data/rewrites.json.

Measured. Three runs overlapped a wait loop of mine that turned out to be spinning a full core. I re-ran two of them. With the same seed they produced byte-identical text, so only the clock was affected. The table excludes the third, a transformers run that decoded at 0.78 tokens/s against a median of 7.6. What the rest say:

• The 0.8B takes 14.1 s per rewrite through llama.cpp, decoding at 35.0 tokens/s on four cores with 1.74 GiB resident. The card's CPU figure is 6.85 tokens/s on twelve threads of a Xeon 8375C. On this box the GGUF path is five times that.
• The card's own transformers recipe is four times slower: 7.6 tokens/s and 55 s per rewrite, at 4.77 GiB. transformers warns why on every load. Without flash-linear-attention and causal_conv1d installed, the gated-delta-rule layers fall back to their reference PyTorch kernels. That is the likely setup on any laptop without a CUDA toolchain.
• The 9B fits. It ran at 10.13 GiB resident, inside the same cgroup that holds the image model. What it costs is time: 387 s per rewrite, 27x the 0.8B. 66 s of that is reading Qwen's 2,473-token system prompt before the first new token. llama.cpp pays that on every request unless the prompt prefix is cached. The rest is decoding at 3.0 tokens/s.
• The 9B thought for 477 to 851 tokens, median 623. Its median total generation was 934 tokens, against 1,666 in ML-Intern-lab's labels. It also wrote shorter rewrites than in those labels: a median of 348 words against 599. I cannot tell whether that comes from Q4_K_M or from these prompts, whose median of 26 words is nearly twice the synthetic median of 14, so I leave it unexplained. On the pitch's point, this run agrees with the files: the thinking is well under 1,600 tokens.

The rewrites themselves, checked against the prompts:

The raw prompts give the denoiser a median of 41 text tokens. Three things in that table matter for the images:

• The quoted text almost survives everywhere. The 0.8B quoted the poster's last word as "Demos,", with a comma inside the quotes. The 9B's two misses are not corruption. Every character is there, but it lays the strings out itself: "面馆" followed by "NOODLE BAR", and the poster's title as "EDGE AI" on the first line and "SUMMIT" directly below it. That follows its system prompt's instruction to describe a line break as a second line. It is still a layout decision the user did not ask for. This probably pulls down the teacher's 53% verbatim rate on the card as well. On the noodle shop the 9B also invents signage of its own: six more quoted strings, among them "面条是我们的热情" ("noodles are our passion") and "Noodle Menu".
• Nobody kept the transparency sentence. The first prediction holds at the text level for all four rewriters, including the teacher. Every rewrite instead describes a background, and the 9B's is the most striking. It rewrites "the background is transparent" into "a gray-and-white checkerboard transparency preview background", which asks the image model to paint the checkerboard that image editors use to show transparency.
• Both 0.8B samples broke the count that the raw prompt gets right. The 2B and the 9B kept it.

The pairs

Each pair re-renders a gallery prompt from the rewrite with the gallery's exact command. That is Q4_K denoiser, 512², 20 Euler steps on the vendor's schedule, cfg 1.0, seed 42, and --mmap, with only the prompt text changed. Each image sits next to the raw-prompt image the parent published. The gallery renders square, so I held the square and discarded the rewriters' ratio choices. Per-step times are medians over the 20 steps.

Four prompts, chosen before any output: the transparent glass bottle, the conference poster, the bilingual noodle-shop sign, and the cups and bowls. The 0.8B rewrote all four, and the 9B rewrote the glass and the poster. That is six renders, 36 to 49 minutes each, and all six are below.

What I expected, written down before any of this ran:

1. Neither pocket model will carry the transparency sentence through intact, and the rewritten glass bottle will lose whatever alpha channel the raw prompt's image has. Not one of the 8,797 requests asked for RGBA, and not one of the 1,776 training targets contains the word.
2. Each denoising step will cost 1.3–1.5x the raw prompt's with a 0.8B rewrite, and more with the 9B's, as the table above derives.
3. The 9B will think for roughly 500 to 1,200 tokens per prompt, not 1,600. Its own labels put the 10th and 90th percentiles at 500 and 1,175.

One more, added at 13:27 UTC on 23 September, after reading the rewrites and before the 9B's glass bottle rendered:

4. The 9B's glass bottle will show a painted checkerboard in opaque pixels, with little or no real transparency. Its rewrite asks for "a gray-and-white checkerboard transparency preview background … evenly spaced square tiles in alternating very light gray and off-white". That describes the pattern an image editor displays behind a transparent layer, not a transparent layer.

How each came out is tallied at the end of this section.

The glass bottle

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Measured. The rewrite dropped both sentences that ask for transparency, and not only those. Neither 0.8B sample contains "transparent", "transparency", "alpha", "RGBA", "cutout" or "isolated" anywhere. In their place this one wrote "a soft gray drop shadow onto the white background" and "a clean white-to-light-gray surface with soft vignette shading", and the image did exactly that. The raw prompt had already failed at this size, since its background came back opaque white too. So the rewrite did not break a working alpha channel. It removed the fringe that was left, 1.30% of the frame below alpha 32, down to nothing. The first prediction holds, in the least interesting way it could. It also changed the object: the request's "clear glass bottle of green olive oil" became dark tinted glass, following the rewrite's "dark brown or charcoal glass".

The cost came almost entirely after the rewriter:

The rewrite took 10.6 s and the image took 1,041 s longer. Measured: the text encoder alone went from 5.5 s to 70.2 s, because it now prefills 351 tokens instead of 41. Measured: the denoising step rose 1.52x. Reasoned: that is above the 1.29x my cost model gives for this pair and just above the range I predicted. Part of that is noise. The raw gallery's per-step medians run from 88.6 s to 103.9 s across prompts of almost the same length, and this prompt's was the fastest of them, which flatters any ratio taken against it. Over all six pairs this turns out to be the model's largest miss.

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Measured. The fourth prediction holds. The rewrite asked for a "gray-and-white checkerboard transparency preview background" and got one, painted in opaque pixels: 100.00% of the frame at alpha 250 or above, the same as the 0.8B's studio backdrop, and less transparency than the raw prompt's 1.30% fringe. The student forgot the instruction. The teacher kept the words "transparency", "alpha" and "cutout" and turned them into a picture of transparency, the thing an image editor shows in place of it. Neither reached a transparent pixel. The teacher did get the object right where the student did not: clear glass, green oil, cork. It also added something nobody asked for, "numerous olive leaves and thin stems" suspended in the oil, and the image drew them.

The 9B's rewrite ran 474 s on four cores. Its 464 text tokens made each step 1.42x the raw prompt's, against 1.40x from the cost model, and the image took 756 s longer. Rewrite and image together, the teacher added 1,231 s to this picture and the student 1,052 s.

The poster

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Measured. The raw prompt had every string right. The 0.8B's rewrite kept the strings but wrapped them in layout the user never asked for. The words are spread "with 'EDGE' aligned on the left, 'AI' centered, and 'SUMMIT' aligned on the right". The date gets "a two-line layout". And the last word is quoted as "Demos,". The image model drew all three. It then added an error of its own, a small stray mark above the vowel after the "l" in "Bengaluru", which the rewrite does not contain. The strings themselves were not corrupted. The same letters rendered less exactly inside a prompt five times longer. The middle of the poster is as empty as before. On the one category the students saw eleven examples of, the rewrite made the text worse and the layout no better.

The step rose 1.35x, against 1.27x from the cost model. That is inside the predicted 1.3–1.5x range, and above the model. The image took 712 s longer, against a 12.8 s rewrite.

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Measured. The teacher's rewrite gave back what the student's took away. Every string is exact, and the only change to the layout is the one its rewrite asked for, the title on two lines. That rewrite was also the shortest of the six, 299 text tokens against the 0.8B's 371, and each step cost 1.05x the raw prompt's against 1.21x from the cost model. That is the model's second-largest miss, in the other direction, and it sits inside the spread of the raw gallery's own per-step medians. On the one prompt where the raw image was already exact, the 9B matched it and did not beat it. What matching it cost was the rewrite: 362 s on four cores, for an image that took 66 s longer than the raw prompt's.

One pair proves nothing about text in general. It does line up with the release's own OCR arm, where the teacher beats no rewriter on rendered text and the 0.8B does not.

The noodle-shop sign

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Measured. This rewrite does not expand the request. It replaces it with a description of a different photograph. The request puts the sign "above the door"; the rewrite mounts "a vertical black sign … on the exterior wall" to "the right of the counter". The request has "steam rising from the doorway"; in the rewrite, steam survives only in a list of the palette's colours, "warm yellows from the lanterns, steam, and reflections". It then adds "two slender, glossy black ramen bowls" as the foreground subject, "a small dark figure" on the far right, and "additional smaller dark Chinese characters" on the sign. The image model drew the rewrite, not the request: a sign beside the door, no steam, two bowls, a figure. The one thing a rewrite could have fixed is the word the raw image garbled, and it is still garbled.

The rewrite also reads like a caption more than a prompt. It hedges about its own scene, a figure "likely" standing near the door, a puddle "shaped like a small bowl-like basin". That is the register of someone describing an image they are looking at, and the image model takes every hedge as an instruction.

At 500 text tokens this was the longest of the six rewrites. Each step cost 1.33x the raw prompt's, against 1.42x from the cost model, and the image took 754 s longer after an 18.6 s rewrite.

The cups and bowls

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Measured. The raw image got the count right. The rewrite could not have: it opens with "three sets of small ceramic bowls", then describes "a pair of red ceramic cups", "a second pair of red cups" and "a third pair", which is six, and "an oval blue glass bowl", which is one. It also moves the camera, "viewed slightly from above", against the request's "photographed from the front". The image model followed the rewrite on the bowl and the camera, and neither the rewrite nor the request on the cups: it drew four. What did improve is that the cups are separate cups with white interiors, where the raw image had fused three into one piece. That is a rendering change, and it has nothing to do with the count. On a prompt that is nothing but a count, the rewrite broke the count.

From 28 text tokens to 432, each step cost 1.45x, against 1.39x from the cost model. The image took 669 s longer after a 14.5 s rewrite.

Six pairs, added up

Every check below is one the request itself states: a count, a string, a position, an alpha value. None of them is a judgement of taste.

Of the nine checks, the raw prompts pass seven and the 0.8B's rewrites pass one. On every prompt, the rewritten image is less faithful to the request than the raw one. The 9B's rewrites pass two of the three checks they were given, and fail the third along with everyone else.

The step cost, pair by pair:

And the four predictions:

1. Holds. No rewrite, from either student or from the teacher, kept the transparency sentence, and both rewritten bottles came back with no transparent pixel at all. The raw prompt had failed here first, so what the rewrites lost was a 1.30% fringe, not a working alpha channel.
2. Half holds. The 0.8B's steps cost 1.33x to 1.52x, a mean of 1.41x: three of four inside the predicted range and one just above it. "More with the 9B's" is wrong, and the table above already said so before anything rendered. What sets the step is the rewrite's length, not which model wrote it, and this run's 9B wrote no longer than the 0.8B did, a median of 424 text tokens against 432. The cost model itself held: measured over modelled runs from 0.87 to 1.18 across the six, a mean of 1.02.
3. Holds. The 9B thought for 477 to 851 tokens, six of seven prompts inside the range and one just under it.
4. Holds. The checkerboard is in the pixels, and every pixel is opaque.

These are one draw of each rewriter on four prompts, and I do not generalise the counts. What six pairs can show is how a rewrite fails, and all four of the 0.8B's fail the same way. The rewrite replaces the request with a description of a picture, and the image model draws the description. On a CPU that costs 33% to 57% more time per image, 669 s to 1,041 s here, of which the rewriter itself is 11 s to 19 s.

The licence

The pocket models are for research and evaluation, and nothing else.

Measured: six LICENSE files in that chain are byte-identical, SHA-256 8dc973f0…. They are Qwen-Image-2.1's, PE-T2I's, the GitHub repo's, the dataset's and both pocket models'. The agreement grants use "FOR NON-COMMERCIAL PURPOSES ONLY" and defines non-commercial as "for research or evaluation purposes only".

Reasoned, and I am not a lawyer. Three separate things put the students under those terms. The distributor licenses them that way, in the README, the NOTICE and the LICENSE. The teacher outputs they were trained on could only be generated by using a research-licensed model, which is itself only permitted for research or evaluation. And the one thing a pocket rewriter is for is feeding Qwen-Image-2.1, which carries the same restriction. Pairing one with an Apache-2.0 image model such as Qwen-Image-2512 does not change the terms you received the rewriter under.

Their compliance is better than most repackaging I have read. There is a copy of the agreement (§3(a)), a NOTICE with the exact attribution text (§3(c)), a modification statement (§3(b)), and "Built with Qwen" at the top of every card (§4(b)). Three gaps remain:

• The model cards' metadata says license: other with no license_name or license_link, so the Hub cannot filter them as qwen-research. The dataset card carries both fields, and both Spaces carry license_name.

• There is no copy of Apache-2.0 for the base weights, which that licence's §4(a) asks for. The README names the licence and links the base model.

• Clause 4(c) of the Qwen agreement reads:

  You shall not use "Qwen" as the primary name or identifier of any derivative works or products; reasonable descriptive use (e.g., "fine-tuned from Qwen Image") is permitted.

  The repos are named Qwen-Image-2.1-PE-T2I-Pocket-0.8B and -2B. The training runs pushed to image21-pocket-rewriter-*, and the GGUF still carries that name. Whether the new names are descriptive use or primary naming is Qwen's call, not mine. I would have kept the old ones.

The take

The distillation is competent, and its paper trail is unusually good. The teacher labels, the filter flags, the training arguments, the prediction files, the evaluation code and its raw scores are all published. That is the only reason I could find the problems. The pocket models do what the text metrics say: they emit the teacher's JSON at the teacher's format reliability, with no system prompt and no thinking, in 453 tokens instead of 1,666. As a replacement for the 9B's output format, they work.

As a replacement for the 9B's effect on pictures, nothing shows it yet, and my own run points the other way. The one image metric in the release that measures anything detects the teacher's gain on rendered text and cannot tell either student from no rewriter at all. The one that was meant to measure everything else picked image A 240 times. On four CPU renders, the 0.8B's rewrites made every picture less faithful to its request: a broken count, two new text errors, a sign moved off the door, and the last of the transparency gone. Each time, the rewrite replaced the request with a description of a picture, and the image model drew the description. The training set offers a reason to expect exactly that: a length cut turned into a curriculum of stickers and logos, with 35 examples of the text-heavy layouts where a rewriter earns its keep.

The teacher is no clean win on a CPU either. It matched the raw prompt's poster, string for string, six minutes of rewriting later, and it answered the transparent bottle with a painted checkerboard. On these four prompts, one draw each, the request as the user typed it was the most faithful prompt and the fastest one.

Two fixes are cheap, and I would make them before the next release. First, give the judge enough tokens to finish, parse its verdict from after its reasoning, and re-score the 240 pairs. That needs no new renders. Second, raise the word ceiling, or truncate long rewrites instead of discarding them, so posters and screens survive into the training set. That costs one more fine-tune, at about 16 USD by their own account.

Sources, all read directly. The two model repos, ML-Intern-lab/Qwen-Image-2.1-PE-T2I-Pocket-0.8B and -2B: README, LICENSE, NOTICE, config.json, chat_template.jinja, training_args.bin (disassembled, never unpickled), and both safetensors headers by range read. The dataset ML-Intern-lab/Qwen-Image-2.1-rewriter-distill at 48e631c: its card, the scripts in code/ that label, filter, train, render and score, labels_full.jsonl, requests_full.jsonl, sft_train.jsonl, the five prediction files, eval/metrics.md, eval/score_4arm.json and four of its contact sheets. The Spaces Qwen-Image-2.1-rewriter-arena and Qwen-Image-2.1-pocket-studio. Qwen's Qwen/Qwen-Image-2.1-PE-T2I: README, system_prompt.txt, chat_template.jinja, config.json, the safetensors index and all four shard headers. The GitHub repo QwenLM/Qwen-Image-2.1 at fb7ae1d, for prompt_rewrite/. The base models Qwen/Qwen3.5-0.8B and Qwen/Qwen3.5-2B, for licence and size. The community GGUF prithivMLmods/Qwen-Image-2.1-PE-T2I-GGUF, for file sizes and its declared parameter count. The diffusers pipeline_qwenimage21.py and stable-diffusion.cpp at 2bb7294, for how each builds the denoiser's text sequence. Word counts split on whitespace; token counts use each model's shipped tokenizer.json. The four figures are the dataset's own contact sheets, served locally with a NOTICE and the licence beside them.