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README.md
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@@ -59,7 +59,7 @@ pipeline_tag: text-to-image
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- Training Speed (Benchmark: 39 imgs, 0.26MP, r16):
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- OneTrainer: 910 steps, 1.12 s/it (`Local Batch Size: 3, LR: 0.0003`) → ~0.37s/img (17m 43s)
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- Fizgig(Batch-Size: Not Supported): 2730 steps, 1.25 it/s (`Gradient Accumulation: 3, LR: 0.0001`) → ~0.83s/img (36m 17s)
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- Fizgig-Advanced(Batch-Size: Supported):
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- *(Note on Speed & Formats: OneTrainer used the official sharded Diffusers format (bfloat16), while Fizgig used the ComfyUI single-file format (int8). Although int8/fp8 usually compute faster on modern GPUs, the benchmark shows Fizgig is slower here—likely due to software-level framework overheads. Fizgig also supports the original bf16 Diffusers shards.)*
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- Showcase & Card Image Parameters:
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- Model: -
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- Training Speed (Benchmark: 39 imgs, 0.26MP, r16):
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| 60 |
- OneTrainer: 910 steps, 1.12 s/it (`Local Batch Size: 3, LR: 0.0003`) → ~0.37s/img (17m 43s)
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| 61 |
- Fizgig(Batch-Size: Not Supported): 2730 steps, 1.25 it/s (`Gradient Accumulation: 3, LR: 0.0001`) → ~0.83s/img (36m 17s)
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- Fizgig-Advanced(Batch-Size: Supported): 910 steps, 2.08 s/it (`Batch Size: 3, LR: 0.0003`) → ~0.69s/img (31m 10s)
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- *(Note on Speed & Formats: OneTrainer used the official sharded Diffusers format (bfloat16), while Fizgig used the ComfyUI single-file format (int8). Although int8/fp8 usually compute faster on modern GPUs, the benchmark shows Fizgig is slower here—likely due to software-level framework overheads. Fizgig also supports the original bf16 Diffusers shards.)*
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- Showcase & Card Image Parameters:
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- Model: -
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