CthomasdesignandClaude Fable 5
5fcf343a4d
_shared gains default_model()/default_lora() (env overrides KREA2_TEST_MODEL/KREA2_TEST_LORA, else <repo>/../Models discovery with clear errors) and fox_source()/ref_uint8() for the reference source image boilerplate repeated across scripts. test_img2img/test_edit/ test_edit_grounded no longer hardcode machine paths; all tools compile and resolve on a fresh checkout. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
36 lines
1.6 KiB
Python
36 lines
1.6 KiB
Python
"""Validate the img2img path: strength==1.0 regression, source round-trip, denoise sweep."""
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import mlx.core as mx
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import numpy as np
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from PIL import Image
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from _shared import PROMPT_SHORT, default_model, fox_source, load_pipeline
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pipe, _ = load_pipeline(default_model())
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src = fox_source()
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# 1) strength==1.0 must equal txt2img (same prompt/seed/size) — the hard guard
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t2i = pipe.generate(PROMPT_SHORT, width=512, height=512, steps=8, seed=0)[0]
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i2i_full = pipe.generate_img2img(PROMPT_SHORT, src, denoise=1.0, width=512, height=512,
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steps=8, seed=0)[0]
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same = np.array_equal(np.array(t2i), np.array(i2i_full))
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print(f"[guard] img2img@denoise=1.0 == txt2img (pixel-identical): {same}")
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# 2) denoise sweep: lower denoise should stay closer to the source
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print("[sweep] similarity of output to source (lower denoise => more similar):")
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sref = np.asarray(src, np.float32) / 255.0
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out = {}
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for d in (0.3, 0.5, 0.7, 0.9, 1.0):
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im = pipe.generate_img2img("the same scene, cinematic", src, denoise=d, width=512, height=512,
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steps=8, seed=0)[0]
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a = np.asarray(im, np.float32) / 255.0
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mae = float(np.abs(a - sref).mean())
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out[d] = im
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print(f" denoise={d:.1f} mean|Δ| to source = {mae:.4f}")
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# save a contact sheet for eyeballing
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sheet = [np.asarray(src.resize((256, 256)))] + [np.asarray(out[d].resize((256, 256)))
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for d in (0.3, 0.5, 0.7, 0.9, 1.0)]
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Image.fromarray(np.concatenate(sheet, axis=1)).save("reference/img2img_sweep.png")
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print("wrote reference/img2img_sweep.png (source | 0.3 | 0.5 | 0.7 | 0.9 | 1.0)")
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