{"assessments":[],"deployments":[],"fuzz":[],"identity":{"adapter":"0xde152afb7db5373f34876e1499fbd893a82dd336","chainId":1,"collection":"0x0000ec93127baa929e58e97dd0095a2bfb38ec1d","registry":"0x8004a169fb4a3325136eb29fa0ceb6d2e539a432"},"interpretation":"Records acceptance and evidence. Neither completion nor an AI assessment establishes correctness, safety, or independent review.","jobId":"d54561e4-57be-4acf-8d7e-8dbe4e1964c3","kind":"skill:scaffold-project","nodes":[{"acceptedSubmissionHash":"f3988e5150f4d63e7be367663ef1e5dc432fa98eb28b73f2bcdb56401e4887f6","dependsOn":[],"execution":{"network":true,"profile":"none","requires":["network"],"skillHash":"7ae2f33d07dd65f04780071437d0c74200f8f58a323bc9324fa8524f587719c6","skillId":"scaffold-project","tools":[]},"key":"scaffold_project","kind":"code","role":"implement","skillHash":"7ae2f33d07dd65f04780071437d0c74200f8f58a323bc9324fa8524f587719c6","skillId":"scaffold-project","state":"accepted"}],"objective":"Render ONE Pepe the Frog character illustration with Stable Diffusion 1.5 + three public LoRAs, on CPU, and deliver the image together with proof of how it was made.\n\nWHY: we want to know how good an image the swarm itself can produce with an open diffusion model and public LoRAs (not a commercial image API). The provenance matters as much as the picture.\n\nHARD RULES\n- Use ONLY the files listed below. Do NOT use any other image generator, hosted API or your own image tool (no OpenAI / DALL-E / gpt-image, no Midjourney, no Replicate, no other model). Do not edit the image afterwards (no painting, no filters, no upscaling).\n- Verify every downloaded file against its sha256 before use. If a hash does not match, stop and report it in run.log.\n- If you cannot finish (no memory, too slow, download blocked), still deliver run.log and params.json explaining exactly where it stopped. A failed but honest run is a valid result. A substituted image is not.\n- CPU is expected. No GPU is required. 512x512 at 25 steps should take minutes on a modern CPU.\n\nFILES\n1. Base model: Stable Diffusion 1.5, single file v1-5-pruned-emaonly.safetensors from the Hugging Face repo stable-diffusion-v1-5/stable-diffusion-v1-5 (about 4.0 GB, CreativeML OpenRAIL-M).\n   sha256 6ce0161689b3853acaa03779ec93eafe75a02f4ced659bee03f50797806fa2fa\n2. LoRA \"pepe-frog\" (Hugging Face): https://huggingface.co/stablediffusionapi/load_lora_embeddings/resolve/main/pepe-frog.safetensors (151 MB)\n   sha256 b777ec0c7b40ea6f5c7fc324c497042df41b89581223ca23354d3f217622cd04\n   weight 0.6\n3. LoRA \"Line Art + Flat Colors v2\" (Civitai model 121745, version 147989, no login needed): https://civitai.com/api/download/models/147989?fileId=109723 (144 MB)\n   sha256 9a51a3d5f74740cec30599fdc695e7dd7ee96e43940b1e0637d69c02165fd2be\n   weight 0.7\n4. LoRA \"Retro - Style\" (Civitai model 65262, version 69897, no login needed): https://civitai.com/api/download/models/69897?fileId=48789 (9 MB)\n   sha256 c07d26d0a564b6214783dbe78ee34063ece5d8d95159a01b18e90097364ed201\n   weight 0.6\nLoad all three LoRAs at the same time (diffusers: load_lora_weights with adapter_name for each, then set_adapters with the weights above).\n\nSETTINGS (diffusers StableDiffusionPipeline.from_single_file + load_lora_weights is fine)\n- size 512x512, steps 25, guidance (cfg) 7, scheduler Euler Ancestral, seed 314, torch.Generator(\"cpu\").manual_seed(314), float32\n- prompt: pepe_frog, pepe the frog, cartoon character, 1970s cartoon, old printed comic, faded colors, flat poster colors, heavy sleepy half closed eyelids, small black pupils, wide red lips, two eyes with white eyeballs and small round black pupils, lineart, flat colors\n- negative: photo, realistic, 3d render, plush, knitted, felt, toy, huge pupils, big black eyes, wide open eyes, extra eyes, many eyes, deformed, melting, blurry, text, watermark, signature, blank white eyes, empty eyes, pupilless eyes, solid black eyes, closed eyes, one eye, sunglasses\n\nDELIVER (exactly these files)\n- artifacts/pepe.png: the raw output, 512x512 PNG. Write the settings into the PNG text chunk \"parameters\" (prompt, negative, seed, steps, cfg, scheduler, each LoRA name + weight + sha256).\n- artifacts/render.py: the exact script you ran, runnable as-is on another CPU machine.\n- artifacts/params.json: {model, model_sha256, loras:[{name,url,sha256,weight}], prompt, negative, seed, steps, cfg, scheduler, width, height, torch_version, diffusers_version, cpu_model, ram_gb, seconds_total, seconds_sampling}\n- artifacts/run.log: full console log including the sha256 checks and timings.\n\nAcceptance: pepe.png exists and is the unedited result of render.py with the settings above; params.json and run.log match it. We will re-run render.py with the same seed to compare.","parentJobId":null,"planHash":"53da5fa5a22c98dcab385e18343b046a70dc1387befdc1816cc9c777857ada50","previousHash":"0000000000000000000000000000000000000000000000000000000000000000","projectId":"d54561e4-57be-4acf-8d7e-8dbe4e1964c3","publication":{"commit":null,"deliveredAt":null,"repoUrl":null},"receiptIdentity":{"adapter":"0xde152afb7db5373f34876e1499fbd893a82dd336","chainId":1,"collection":"0x0000ec93127baa929e58e97dd0095a2bfb38ec1d","registry":"0x8004a169fb4a3325136eb29fa0ceb6d2e539a432"},"registry":"0xb6d0a187b050fa5bb0b87033a203f37becf4a775","research":[],"schema":"identitymd-work-v1","signals":[{"agentId":"52230","feedbackHash":"3b0f0c26fcc0aa00836f0b35cf5f4ada12e667c78838e563f5d6867e838b4945","nodeKey":"scaffold_project","submissionHash":"f3988e5150f4d63e7be367663ef1e5dc432fa98eb28b73f2bcdb56401e4887f6","tag1":"verification:structural","tag2":"acceptance-v2","value":1}],"site":null,"snapshotHash":"bf8e5046d8bc5b024b648b7944633d96a99e696cb27733950b78141f37e307ec","state":"completed","submissions":[{"artifacts":[{"bytes":278117,"hash":"c213272ba260ef4294632e6fde293f5939028e40f46447afb55d4741b7ce962f","mediaType":"image/png","name":"pepe","path":"artifacts/pepe.png"},{"bytes":12421,"hash":"55d8212b0a77241b1ddc38d507055c9b8419e349492b83f71778e6db3a1bedfc","mediaType":"text/x-python","name":"render","path":"artifacts/render.py"},{"bytes":2140,"hash":"73ba050f09ef6d367f386cee38e6707ed9e7d215d4da946f7fbbabcf9e37afae","mediaType":"application/json","name":"params","path":"artifacts/params.json"},{"bytes":4559,"hash":"220f7e061d784f191c33c82c90904c21c94ef6b6e58faa284978e679598c9f43","mediaType":"text/plain","name":"log","path":"artifacts/run.log"}],"attempt":1,"bundleHash":"ca34ad1e6159d64f00536bae1c27ccc46bf407db8a7e4eb3bd5ceaddd4f3429d","device":"04f946173ab09bec","findings":[],"hash":"f3988e5150f4d63e7be367663ef1e5dc432fa98eb28b73f2bcdb56401e4887f6","nodeId":"afbdce12-74a9-405d-9a90-7cab28ab515f","outcome":"completed","summary":"I rendered the image with Stable Diffusion 1.5 and all three LoRAs on CPU. A second run with the same settings produced a byte-identical PNG (sha256 `c213272b…962f`).\n\nThe picture is a flat-colour line-art Pepe: green head, red lips, heavy half-closed eyelids, on a pink poster background. The left eye has a small round black pupil; the right eye is a narrow white slit with no visible pupil.\n\n**How it was made:**\n- **Hashes:** all four downloaded files matched their sha256 before use, and each check is in `run.log`.\n- **LoRAs:** loaded together as adapters with weights 0.6, 0.7 and 0.6. No warnings about unmatched keys.\n- **Settings:** exactly as specified (512×512, 25 steps, cfg 7, Euler Ancestral, seed 314 on a CPU generator, float32). The PNG's `parameters` text chunk holds the prompt, negative, settings and each LoRA's name, weight and sha256.\n- **Timing:** 875 s in total, 828 s of it sampling, at about 33 s per step. That's slower than \"minutes\" because the machine had only 2 cores and 3.8 GB of RAM.\n- **Swap file:** the float32 model doesn't fit in 3.8 GB, so I added an 8 GB swap file at `/swapfile`. This is outside the repo, but `run.log` doesn't mention it; the README does, with the command.\n- **Versions:** torch 2.5.1+cpu, diffusers 0.31.0. I ran it with the Hugging Face offline flag set, and model loading worked from local files only.\n\n**Delivered, untracked:** `artifacts/pepe.png`, `artifacts/render.py`, `artifacts/params.json` and `artifacts/run.log`. The script copies itself into `artifacts/`, so the copy is exactly what ran. `params.json` also has a few extra fields beyond your list: status, PNG hash, other library versions and core count.\n\n**Committed** (one commit on `main`):\n- `render.py`: downloads any missing weights, checks each hash and stops on a mismatch, renders and writes the four outputs. If it fails, it still writes `run.log` and a `params.json` saying where it stopped.\n- `sd15-config/`: the small config and tokenizer files from the SD 1.5 repo (no weights), so loading needs no network.\n- `requirements.txt` with pinned versions, `README.md` explaining what this is and how to run it, and `.gitignore`.\n\n**What isn't in the commit:** the 4.6 GB of weights in `models/` and the Python packages themselves (torch alone is far too big). A fully offline re-run therefore needs those weights placed in `models/` and the packages already installed. The script re-checks the hashes either way.\n\n**Possible pixel differences:** a re-run on a different CPU or torch build may differ slightly in float32. With the same versions it should give the same image, as my second run did.\n\nI made the commit with your name and email set only for that command, because no git identity was configured and I'm not allowed to edit `.git/`.","treeHash":"99499598e5d81ef14e62463e9497c251b9316154","usage":{"cachedInputTokens":921301,"inputTokens":48,"model":"claude-opus-5-5","outputTokens":17577,"runtime":"claude","turns":25,"wallClockMs":2096082}}],"verification":[{"checks":[],"detail":"paths and tree verified; no suite was run for this kind of work","evaluation":"structural","profile":"none","status":"accepted","submissionHash":"f3988e5150f4d63e7be367663ef1e5dc432fa98eb28b73f2bcdb56401e4887f6","verifiedTreeHash":"99499598e5d81ef14e62463e9497c251b9316154","verifierVersion":"0.1.0+c4d32abc"}]}