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Saving a local image-generation model preserves its learned weights, not the complete recipe that produced a particular image. To make reruns as repeatable as possible, save the model identity, prompt, seed, pipeline settings, software versions and hardware details—and recreate the same execution environment. A seed alone cannot guarantee identical pixels, especially across machines or software versions.
Why the same seed can produce a different image
Diffusion pipelines use random noise during generation. A seed helps initialize that randomness, but it is only one input to a larger process. The output can also depend on the exact model and pipeline components, prompt and negative prompt, sampler or scheduler, step count, image dimensions, numerical precision, software implementation and hardware backend.
There is another easy-to-miss detail: a random-number generator advances as it is used. Reusing an already-consumed generator object does not necessarily restart it at the original state. For a fresh reproduction attempt, create or reseed the generator rather than assuming it is still at its starting point. Diffusers recommends a CPU generator when reproducibility matters. Diffusers: Reusing seeds and reproducibility
What to save with each image
Keep a run record alongside the image and model files. The following is a practical checklist synthesized from the documented sources of variation; it is not a universal official manifest.
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- Model identity: exact model name and revision, or a checksum where available. Include relevant pipeline components and add-ons such as adapters.
- Prompts: the full prompt and negative prompt, preserving wording and settings as used.
- Randomness: the seed, and the generator type or state if the interface exposes it. Recreate or reseed the generator for each run.
- Generation settings: sampler or scheduler, number of steps, image width and height, and other settings that affect inference.
- Numerical settings: precision and any relevant performance or determinism options.
- Execution environment: interface or pipeline version, framework and library versions, device, backend and relevant hardware.
- Workflow: export the complete workflow or configuration if the local interface supports it, and keep a plain-text run record as a fallback.
Keep the original weight files and their exact identity together with this record. Do not assume a saved PNG contains a complete recipe: the reviewed documentation does not establish that every interface writes all required settings into image metadata.
How to improve repeatability in a pinned environment
- Record the baseline. Note the model revision, complete generation settings, software versions and hardware before changing anything.
- Reset randomness for the run. Set a fixed PyTorch seed and, if the application uses them, seed other random-number generators such as Python’s
randomor NumPy. In Diffusers, pass a CPUGeneratorwhen appropriate and instantiate or reseed it for each attempt. PyTorch: Reproducibility notes - Enable deterministic controls where supported. PyTorch offers deterministic algorithms and documents disabling cuDNN benchmarking; Diffusers’ reproducibility helper also configures CUDA-related settings and disables TF32. Follow the guidance for your exact software stack rather than assuming one setting covers every source of variation. Diffusers: Reusing seeds and reproducibility
torch.use_deterministic_algorithmsAPI - Rerun without changing the environment. Compare results on the same machine with the same pinned software and settings before attempting to reproduce them elsewhere.
Deterministic controls can reduce nondeterminism, but they have costs and limits. Deterministic operations may be slower, and an operation without a deterministic implementation can raise an error. PyTorch cautions that the deterministic-algorithm setting by itself may not make an application reproducible. torch.use_deterministic_algorithms API
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What to expect on another computer or software version
A rerun on the same machine with the same pinned environment has a better chance of matching than a run on a different device or software stack. Exact cross-machine reproduction is a stronger goal: different platforms, framework releases, numeric precision and backend algorithms can change results even when the seed and visible settings match. PyTorch explicitly warns that completely reproducible results are not guaranteed across releases or platforms, and Diffusers notes that full reproducibility for some GPU pipelines requires the same hardware and PyTorch version.
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For that reason, treat a seed as a useful part of a reproducible record—not a promise of pixel-identical output. Keep the environment details with the workflow so you can distinguish a changed recipe from a changed execution platform.
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