Deploy Stable Diffusion
ImageStable Diffusion by Stability AI is the most widely adopted image generation family with the largest ecosystem of fine-tunes and LoRAs. SDXL generates 1024x1024 images, while SD 3.5 offers improved text rendering.
Deploy Stable Diffusion in minutes
Starting at $0.66/hr on dedicated GPU
Available Variants (5)
| Model | GPU | VRAM | Price | Action |
|---|---|---|---|---|
Stable Diffusion XL XL | L4 | 24 GB | $0.66/hr | Deploy |
Stable Diffusion 3.5 Large Large | RTX A6000 | 48 GB | $0.72/hr | Deploy |
Stable Diffusion 3.5 Medium Medium | L4 | 24 GB | $0.66/hr | Deploy |
Stable Diffusion 3.5 Turbo Turbo | L4 | 24 GB | $0.66/hr | Deploy |
Stable Diffusion 1.5 1.5 (Legacy) | L4 | 24 GB | $0.66/hr | Deploy |
Prices include the service fee. Charges follow actual running time.
Requirements
ModelPilot assigns 24–48GB cloud GPUs across the listed variants. Actual local VRAM requirements vary with model variant, precision, quantization, resolution, and workflow settings.
On ModelPilot, deploy on a dedicated cloud GPU (up to 80GB VRAM) starting at $0.66/hr with no setup required.
Compare Stable Diffusion
Source-backed GPU, VRAM, and cost comparisons for nearby deployment choices.
FLUX.2 Dev Full (64GB) vs Stable Diffusion XL
Compare Stable Diffusion XL against FLUX.2 Dev Full (64GB) by GPU tier, VRAM, and base hourly cost.
FLUX.2 Dev Full (64GB) vs Stable Diffusion 3.5 Large
Compare Stable Diffusion 3.5 Large against FLUX.2 Dev Full (64GB) by GPU tier, VRAM, and base hourly cost.
FLUX.2 Dev Full (64GB) vs Stable Diffusion 3.5 Medium
Compare Stable Diffusion 3.5 Medium against FLUX.2 Dev Full (64GB) by GPU tier, VRAM, and base hourly cost.
FLUX.2 Dev Full (64GB) vs Stable Diffusion 3.5 Turbo
Compare Stable Diffusion 3.5 Turbo against FLUX.2 Dev Full (64GB) by GPU tier, VRAM, and base hourly cost.
FLUX.2 Dev Full (64GB) vs Stable Diffusion 1.5 (Legacy)
Compare Stable Diffusion 1.5 (Legacy) against FLUX.2 Dev Full (64GB) by GPU tier, VRAM, and base hourly cost.
Stable Diffusion 3.5 Medium vs Stable Diffusion 3.5 Turbo
Compare Stable Diffusion 3.5 Medium and Stable Diffusion 3.5 Turbo variants by GPU tier, VRAM, and base hourly cost.
Use Cases
- ✓LoRA and fine-tune deployment
- ✓Custom model training base
- ✓Established workflow integration
- ✓Community model ecosystem
Related Models
Known Issues & Fixes
Real failures from running Stable Diffusion and similar pipelines, with the mechanism behind each one.
Over-processed / double-degraded output when a speed LoRA is stacked on an already-distilled checkpoint
Cause: A distill/acceleration LoRA and a pre-distilled checkpoint are alternative acceleration paths — the rule is to disable the distill LoRA whenever the checkpoint is already pre-distilled. Stacking two conflicting few-step schedules degrades output.
Fix: Apply AT MOST ONE acceleration path. Note: base Wan DOES need the lightx2v LoRA for few-step (the correct single path); the double-distill hazard only arises with a pre-merged/FusionX-style Wan checkpoint that already bakes lightx2v/CausVid in.
Subject renders with an unexpected color cast — e.g. a white marble statue goes green-bronze/metallic
Cause: A relight LoRA above strength 0 imposes a relighting color shift matching the driving scene instead of preserving source color (shipped default is 0.0; Kijai's enabled default is 1.0).
Fix: Zero the relight/style LoRA when you want source color preserved. A tinted/off-color output means suspect an auxiliary LoRA near strength 1.0.
Pod deploys 'successfully' but comes up degraded / the custom checkpoint is missing (only the base model present), even though env vars look correct
Cause: CORRECTED: RunPod caps a single env value at ~30KB and the total env payload at ~100KB (measured empirically; practical guardrails are MAX_ENV_VAR_SIZE=30000 and MAX_TOTAL_ENV_SIZE=100000). Cram a large workflow graph into a raw WORKFLOW_JSON and you blow the per-var or total budget, so other values (e.g. MODELS_TO_DOWNLOAD) may not take effect.
Fix: Never pass large JSON as a raw value — bake the default workflow into the image, or let the deploy path gzip it (WORKFLOW_JSON_GZ) or hand it off by URL (WORKFLOW_URL / GCS). Keep per-value <30KB and total <100KB. Small inline WORKFLOW_JSON is fine (the intended SDXL-checkpoint pattern). Diagnose by inspecting the pod's on-disk /models, not startup logs.
A workflow that rendered perfectly once produces a BLACK VOID / garbage on a later redeploy — identical JSON, config, and GPU
Cause: Unpinned by default: models pull from floating HF /resolve/main (branch HEAD), custom nodes are git-clone-latest (no SHA), Docker tags are reused, and only ~95% file-size is validated, not content hash. An upstream re-upload or newer node code silently changed bytes/behavior behind unchanged names.
Fix: Pin weights to a commit/revision, nodes to a SHA, images to a digest. Verify a pinned HF URL with `curl -sI -r 0-0 -A 'Mozilla/5.0' -L '<url>'` — MUST include -L (HF /resolve/ 302-redirects to a CDN; without -L you see 302 not 206).
Workflow won't instantiate: 'the following node types were not found: BlockifyMask' despite the node repo being installed
Cause: The node was pinned to a commit predating when the required node class was added upstream. (The inverse also bites: a too-NEW pin can fail to import, forcing a rollback to an older release.)
Fix: Two-sided constraint: pin new enough that EVERY referenced node class is registered, but NOT so new it needs a ComfyUI newer than the baked image. Verify the pinned commit's NODE_CLASS_MAPPINGS registers all referenced classes AND imports cleanly against the deployed ComfyUI.
You rebuilt and pushed the image, but the pod still runs the OLD behavior — the fix 'didn't work'
Cause: A reused mutable Docker tag means the container host serves an image it already cached under that tag and skips the re-pull after a rebuild (unless pull policy forces it or a differing digest is detected) — so it runs the stale image.
Fix: Push to a fresh tag or an immutable @sha256 digest. Treat 'not taking effect after rebuild' as a pull-cache symptom first.
Hitting a specific error message? Browse ComfyUI error fixes.
Frequently Asked Questions
How much GPU memory is allocated for Stable Diffusion?
The listed ModelPilot variants use 24–48GB cloud GPUs. Local memory needs vary with the variant, precision, quantization, and workflow settings.
How much does it cost to run Stable Diffusion?
Starting at $0.66/hr on a dedicated GPU. Charges are calculated from actual running time, with auto-stop when credits run out.
How long does Stable Diffusion take to deploy?
Most deployments complete in 10–20 minutes including model download and environment setup.
Can I run Stable Diffusion on my local GPU?
It depends on the selected variant, precision, quantization, and workflow settings. Compare the variants below with your available VRAM; the table shows ModelPilot's cloud GPU allocation, not a universal local minimum.
Ready to deploy Stable Diffusion?
Pick your GPU and have it running in minutes. No infrastructure setup required.