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Backend selection

stable-diffusion.cpp has two backend assignments:

  • --backend selects the runtime backend used to execute model graphs.
  • --params-backend selects where model parameters are kept.

If --params-backend is not set, parameters use the same backend as their module runtime backend.

Syntax

A backend assignment can be a single backend name:

sd-cli -m model.safetensors -p "a cat" --backend cpu

This applies to every module that does not have a more specific assignment.

Assignments can also target individual modules:

sd-cli -m model.safetensors -p "a cat" --backend te=cpu,vae=cuda0,diffusion=vulkan0

The same syntax is used for parameter placement:

sd-cli -m model.safetensors -p "a cat" --backend cuda0 --params-backend te=cpu,vae=cpu

--params-backend also accepts the special value disk:

sd-cli -m model.safetensors -p "a cat" --backend cuda0 --params-backend disk

--max-vram can target resolved backend/device names:

sd-cli -m model.safetensors -p "a cat" --backend diffusion=cuda0,vae=vulkan0 --max-vram cuda0=6,vulkan0=2

The value is a shared per-device budget for managed weights and registered runner compute/cache buffers. Live free memory can lower the effective limit for each graph run. Driver contexts and allocations made outside the managed model runners are not part of this accounting, so it is not a hard physical VRAM cap.

Module names are case-insensitive. Hyphens and underscores in module names are ignored, so clip_vision, clip-vision, and clipvision are equivalent.

all=, default=, and *= can be used to set the default backend inside a mixed assignment:

sd-cli -m model.safetensors -p "a cat" --backend all=cuda0,te=cpu

Multiple devices per module (layer split)

A --backend module assignment can list several devices separated by &:

sd-cli -m model.safetensors -p "a cat" --backend "diffusion=cuda0&cuda1"

The module's transformer blocks are then distributed across the listed devices in contiguous ranges sized proportionally to each device's free memory (minus a compute-buffer headroom of about 2 GiB per device), and the module's graphs are executed with a ggml_backend_sched that runs each block on the device holding its weights, copying the residual stream at the range boundaries. The first device in the list is the module's main device: it also holds the non-block tensors (embeddings, final norms, small sub-runners such as CLIP models or projectors) and the graph inputs/outputs.

Layer split is supported for the diffusion and te modules. For te it applies to the dominant text encoder (t5xxl or the LLM); other modules accept only a single device. If the module has no recognizable transformer blocks, the assignment falls back to the first listed device.

--params-backend accepts no device lists. If the module has no explicit params assignment, each block range's parameters are loaded directly to (and, with --params-backend diffusion=disk, released directly from) its own device; an explicit assignment such as te=cpu keeps the parameters on that backend and stages each range to its device on demand.

Layer split uses the fixed graph-cut plan to assign blocks across devices, but single-device segmented execution and next-segment prefetch are disabled for the split module. --max-vram can still provide the per-device limits used by layer split and auto-fit.

Use --list-devices to see the device names available on the system.

Row split (--split-mode row)

--split-mode selects how a multi-device module distributes its weights: layer (the default, described above) or row. It accepts a single mode or per-module assignments:

sd-cli -m model.safetensors -p "a cat" --backend "diffusion=cuda0&cuda1" --split-mode row
sd-cli -m model.safetensors -p "a cat" --backend "diffusion=cuda0&cuda1,te=cuda0&cuda1" --split-mode diffusion=row,te=layer

In row mode the module keeps executing on its main (first listed) device, but its transformer-block matmul weights are allocated in the backend's row-split buffer type, which slices each weight's rows across the listed devices in proportion to free memory and runs those matmuls on all devices in parallel. Compared to a layer split this uses all GPUs within every layer (instead of sequentially device by device) at the cost of a cross-device reduction per matmul - usually the faster option when the devices have fast interconnect.

Row split requires a compatible split-buffer export from the linked GGML backend. If it is unavailable (or the listed devices belong to different backend registries), the module falls back to a layer split. Embeddings, normalization weights, biases and other non-block tensors stay in regular buffers on the main device.

Row-split execution can use graph segments, but split weights are loaded synchronously instead of using the normal single-device prefetch path. Because GGML does not expose exact shard allocation sizes, the managed budget currently counts a split buffer's full size on each participating device. This is a conservative bound and can reject otherwise feasible layouts.

Direct ("immediately") LoRA application cannot patch row-split tensors; with --split-mode row the automatic LoRA mode selects runtime application, and an explicit --lora-apply-mode immediately skips the split tensors with a warning.

Automatic placement (--auto-fit on|off)

--auto-fit requires on or off and defaults to on when omitted. Explicit --backend or --params-backend assignments disable auto-fit, regardless of argument order, even with --auto-fit on.

When enabled, auto-fit uses one GPU for diffusion / te / vae computation. It chooses the GPU with the largest available memory budget (the first device on a tie), then derives parameter placements from the model metadata and the remaining memory budgets. The chosen backend specifications are printed.

sd-cli -m model.safetensors -p "a cat" --auto-fit on
sd-cli -m model.safetensors -p "a cat" --auto-fit on --max-vram cuda0=8,cuda1=14
sd-cli -m model.safetensors -p "a cat" --auto-fit off

Budgets reuse --max-vram: a positive per-device value caps what auto-fit plans with on that device, a negative value means "free memory minus that many GiB", and with no budget set each device's free memory minus a 512 MiB margin is used. These resolved GPU budgets, including the safety margin, also drive the runner's graph-cut capacity checks.

Components are considered in diffusion, te, vae order so that repeatedly used diffusion weights have priority. Each component's weights use the first storage location with enough remaining budget:

  1. The main GPU, leaving estimated space for computation and weight staging.
  2. CPU RAM, reserving the larger of 2 GiB or 10% of available RAM for other work.
  3. Another GPU, choosing the one with the largest remaining budget that fits.
  4. Disk, reloading weights on demand.

GPU cache space follows the same component priority. Before a lower-priority component can become permanently resident, the planner leaves room for the full weights and estimated compute space of higher-priority offloaded components. If offloaded diffusion already needs the entire main GPU budget, TE and VAE also use offloaded parameters. Their GPU copies can then be released after their phases, leaving more room to reuse diffusion weights across sampling steps. CPU parameter residency allows GPU weight caching; it does not force every weight to be copied again at every step.

RAM and GPU budgets are shared across components. Each component uses a single parameter backend; several other GPUs' capacities are not combined to store one component. If available RAM cannot be queried, RAM residency is skipped. Other GPUs store weights only: weights are copied to the main GPU for execution. Auto-fit does not select multi-GPU layer/row computation, so --split-mode does not change its placements. Use explicit backend assignments for multi-GPU computation.

For example, a diffusion model whose full weights exceed the main GPU's budget can use --backend diffusion=cuda0 --params-backend diffusion=cpu when RAM is sufficient. Automatic graph segmentation can then load the required weights for each segment and reclaim idle GPU copies. --disable-segmented-compute still disables segmentation.

Initial compute reserves are estimates (2 GiB for diffusion and text encoders, 1 GiB for VAE); higher-priority placements also leave staging space for the largest weight tensor of each lower-priority offloaded component. Actual segment weights, compute buffers and caches must still fit the runner's capacity checks. Offloading weights does not guarantee that every resolution or frame count will fit, and auto-fit does not change a component to CPU computation solely because its full weights exceed VRAM. If a VAE decode fails, auto-fit retries with spatial tiling; supported video decoders try temporal tiling first and can then add spatial tiling.

Modules

Module Purpose Accepted names
diffusion UNet, DiT, MMDiT, Flux, Wan, Qwen Image, and other diffusion models diffusion, model, unet, dit
te Text encoders and conditioners te, clip, text, textencoder, textencoders, conditioner, cond, llm, t5, t5xxl
clip_vision CLIP vision encoder clip_vision, clipvision, clip-vision, vision
vae VAE and TAE vae, firststage, autoencoder, tae
controlnet ControlNet controlnet, control
photomaker PhotoMaker ID encoder and PhotoMaker LoRA photomaker, photomakerid, pmid, photo
upscaler ESRGAN upscaler upscaler, esrgan, hires
detector ADetailer YOLOv8 detector detector, adetailer, yolo

te is the preferred module name for text encoders. clip is kept as an accepted alias because many existing commands and model names use CLIP terminology.

Backend names

Backend names are resolved against the GGML backend device list. Matching is case-insensitive and accepts exact names or unique prefixes, so common values include names such as:

  • cpu
  • cuda0
  • vulkan0
  • metal

The special values auto, default, and an empty backend name select the default backend. The default preference is GPU, then integrated GPU, then CPU.

The special value gpu selects the first GPU backend, falling back to the first integrated GPU backend.

The special value disk is accepted only by --params-backend. --backend disk is invalid because disk is a parameter residency mode, not a runtime compute backend.

Runtime backend vs. parameter backend

The runtime backend controls where graph execution runs. The parameter backend controls where model weights are allocated or whether they are reloaded from disk on demand.

For example:

sd-cli -m model.safetensors -p "a cat" --backend cuda0 --params-backend cpu

This runs all modules on cuda0, but stores parameters in CPU RAM. During execution, parameters are moved to the runtime backend as needed.

For example:

sd-cli -m model.safetensors -p "a cat" --backend cuda0 --params-backend disk

This runs all modules on cuda0, reloads parameters from the model file as needed, and releases those parameter buffers after use.

Outside --auto-fit, disk is never selected implicitly. If --params-backend is not set, parameters use the runtime backend.

Per-module assignments can be mixed:

sd-cli -m model.safetensors -p "a cat" --backend diffusion=cuda0,te=cpu,vae=cpu --params-backend diffusion=cuda0,te=cpu,vae=cpu

This keeps text encoding and VAE execution on CPU while the diffusion model runs on GPU.

Backend sharing and lifetime

Backends are managed by SDBackendManager.

Within one manager, backend instances are cached by resolved backend device name. If multiple modules request the same backend, they share the same ggml_backend_t.

For example:

--backend te=cpu,vae=cpu

uses one shared CPU backend for both te and vae runtime execution.

Runtime and parameter assignments also share the same backend cache. If --backend diffusion=cuda0 and --params-backend diffusion=cuda0 resolve to the same device, both use the same backend instance.

--params-backend disk does not create a separate backend instance. Parameters are loaded lazily using the module runtime backend.

SDBackendManager owns the backend instances and frees them when the context or upscaler is destroyed. Model runners receive non-owning runtime and parameter backend pointers and do not free them.

Compatibility flags

The example CLI/server still accepts these older CPU placement flags as compatibility aliases:

  • --clip-on-cpu
  • --vae-on-cpu
  • --control-net-cpu
  • --offload-to-cpu

--clip-on-cpu, --vae-on-cpu, and --control-net-cpu are deprecated. The example argument layer prepends te=cpu, vae=cpu, and controlnet=cpu to --backend before creating the context.

--offload-to-cpu prepends a CPU default to the parameter assignment in the caller before creating the context:

--params-backend '*=cpu'

Because this default is inserted first, later explicit --params-backend entries can still override it, for example --offload-to-cpu --params-backend te=disk keeps non-TE parameters on CPU and reloads TE parameters from disk.

Library callers should set backend and params_backend directly. sd_ctx_params_init() enables auto_fit by default; nonempty backend or params_backend assignments disable it. The old CPU/offload fields are no longer part of the C API. Explicit --backend and --params-backend assignments are preferred for new commands.