mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-10-14 21:40:47 +02:00
Victor Kuznetsov
16bc9879e9
Extract TwoStageZImagePipeline shared base from QwenZImagePipeline so all three profiles are siblings, not a chain. Add ChromaZImagePipeline on that base with diffusers ChromaImg2ImgPipeline bf16, neutral calibrated prompt, guidance 5.0, four effective steps with Diffusers truncation compensation, no Canny. Flat vendor floors from the 2026-08-29/30 oracle calibration across OpenAI (0.09), Microsoft InvisMark (0.125), Google (0.40), Meta Content Seal (0.17), seed-stability verified on Meta, full-path face-stage interaction clean through the Gemini oracle. Research record in docs/chroma1-engine-research.md with the matched-strength addendum showing Chroma1 regenerates better per unit strength but its boundaries scatter wider than qwen's. Fix text-manifest guard to reject chroma-zimage at the boundary, update all profile references from 'both' to 'all three' across src, docs, README, and python-api.
115 lines
3.7 KiB
Python
Executable File
115 lines
3.7 KiB
Python
Executable File
# /// script
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# requires-python = ">=3.11"
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# dependencies = ["modal>=1.5"]
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# ///
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"""Extra high Chroma1 rungs (0.25-0.40) for the Google cohort fixtures.
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The 0.08-0.20 ladder came back detected end-to-end on the first fixture, so
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this appends the high rungs for every Google fixture without touching the
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already-oracled lower outputs.
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"""
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from __future__ import annotations
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import json
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import math
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from pathlib import Path
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import modal
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_ROOT = Path(__file__).resolve().parent.parent
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app = modal.App("chroma-google-high-rungs")
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image = (
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modal.Image.debian_slim(python_version="3.12")
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.pip_install(
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"torch",
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"diffusers>=0.38,<1",
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"transformers>=4.53",
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"accelerate",
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"sentencepiece",
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"protobuf",
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"pillow",
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"numpy",
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)
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.env({"HF_HOME": "/cache"})
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)
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cache = modal.Volume.from_name("chroma-hf-cache", create_if_missing=True)
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CHROMA_MODEL_ID = "lodestones/Chroma1-HD"
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RUNGS = (0.25, 0.30, 0.35, 0.40)
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EFFECTIVE_STEPS = 4
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_PROMPT = "high quality, sharp, detailed, faithful to the original"
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_NEGATIVE = "blurry, lowres, distorted text, garbled text, artifacts"
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FIXTURES = (
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"Gemini_Generated_Image_633uuy633uuy633u.png",
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"Gemini_Generated_Image_akdbeiakdbeiakdb.png",
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"Gemini_Generated_Image_y48j3cy48j3cy48j.png",
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"Gemini_Generated_Image_3mc4t93mc4t93mc4.png",
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)
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MAX_SIDE = 1536
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@app.function(image=image, gpu="H100", volumes={"/cache": cache}, timeout=2400)
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def run(payload: dict[str, bytes]) -> dict:
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import io
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import time
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import torch
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from diffusers import ChromaImg2ImgPipeline
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from PIL import Image
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chroma = ChromaImg2ImgPipeline.from_pretrained(CHROMA_MODEL_ID, torch_dtype=torch.bfloat16).to("cuda")
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out: dict = {}
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for name, data in payload.items():
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source = Image.open(io.BytesIO(data)).convert("RGB")
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if MAX_SIDE and max(source.size) > MAX_SIDE:
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scale = MAX_SIDE / max(source.size)
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source = source.resize(
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(round(source.width * scale), round(source.height * scale)),
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Image.Resampling.LANCZOS,
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)
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width = max(16, (source.width // 16) * 16)
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height = max(16, (source.height // 16) * 16)
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for strength in RUNGS:
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started = time.perf_counter()
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generator = torch.Generator(device="cpu").manual_seed(0)
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image = chroma(
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prompt=_PROMPT,
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negative_prompt=_NEGATIVE,
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image=source,
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width=width,
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height=height,
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strength=strength,
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num_inference_steps=max(1, math.ceil(EFFECTIVE_STEPS / strength)),
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guidance_scale=5.0,
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generator=generator,
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).images[0]
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if image.size != source.size:
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image = image.resize(source.size, Image.Resampling.LANCZOS)
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buffer = io.BytesIO()
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image.save(buffer, format="PNG")
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out[f"{Path(name).stem}/chroma_s{strength:.2f}"] = {
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"png": buffer.getvalue(),
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"seconds": round(time.perf_counter() - started, 2),
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}
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print(f"{name} s={strength} done", flush=True)
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cache.commit()
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return out
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@app.local_entrypoint()
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def main(out: str = "out/cohort-calibration/google") -> None:
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payload = {name: (_ROOT / "data" / "synthid" / "originals" / name).read_bytes() for name in FIXTURES}
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results = run.remote(payload)
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timing = {}
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for key, item in results.items():
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destination = Path(out) / f"{key}.png"
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destination.parent.mkdir(parents=True, exist_ok=True)
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destination.write_bytes(item["png"])
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timing[key] = item["seconds"]
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print(json.dumps(timing, indent=2))
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