Stable Diffusion 3.5
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Stable Diffusion 3.5 is Stability AI’s diffusion-transformer text-to-image family. It encodes prompts with three text encoders at once: CLIP-L, CLIP-G and T5. It uses a 16-channel VAE. InvokeAI supports two checkpoints:
- SD3.5 Medium: the smaller model and the one InvokeAI’s default settings are tuned for.
- SD3.5 Large: the larger model.
Both appear in InvokeAI under the SD 3.x base.
License
Section titled “License”SD 3.5 Medium and Large are released under the Stability AI Community License: free for individuals and organizations with less than $1M in annual revenue, above which an Enterprise license is needed. The downloads are gated: accept the license on the model’s HuggingFace page and add your HuggingFace token under API Keys in the Model Manager.
This is a summary, not legal advice. Read the full license on the model’s HuggingFace page before using a model commercially.
Hardware
Section titled “Hardware”The Diffusers downloads are about 16 GB for Medium and 28 GB for Large, including the text encoders and VAE. Medium is the better choice on smaller cards. See System Requirements for general guidance, and the Low VRAM guide if you run out of memory.
Installing
Section titled “Installing”Both models are starter models in the Model Manager. Search for SD3.5 Medium or SD3.5 Large. This family has no starter bundle.
| Starter model | Source | Size |
|---|---|---|
| SD3.5 Medium | stabilityai/stable-diffusion-3.5-medium | ~16 GB |
| SD3.5 Large | stabilityai/stable-diffusion-3.5-large | ~28 GB |
SD3 models install in Diffusers format only. Each Diffusers folder contains everything the model needs: the transformer, both CLIP encoders, the T5 encoder and the VAE. InvokeAI does not recognize single-file or GGUF SD3 main models.
Optional component overrides
Section titled “Optional component overrides”By default, every component comes from the main model. In the Components section next to the model, you can replace any of them with a model you have installed separately:
- T5 Encoder
- CLIP L
- CLIP G
- VAE: only SD3 VAEs are offered
The T5 encoder can also be one of the single files ComfyUI uses: t5xxl_fp16, or t5xxl_fp8_e4m3fn_scaled for
about half the VRAM. A single-file T5 run on the CPU is held in float32 (about 19 GB of RAM for
T5-XXL).
The CLIP encoders can also be the single files ComfyUI uses, clip_l and clip_g; SD 3 then still tokenizes
with the main model’s tokenizers.
Generation settings
Section titled “Generation settings”When you select an SD3 model, InvokeAI applies these defaults, which come from the SD3.5 Medium reference settings:
| Setting | Default |
|---|---|
| Steps | 40 |
| CFG Scale | 4.5 |
| Resolution | 1024 × 1024 |
- CFG Scale applies true classifier-free guidance. Stability’s reference setting for Large is about 28 steps at CFG 3.5. Use that as a starting point when you switch to Large.
- Width and height must be multiples of 16.
- Negative prompts are supported and always used.
- No scheduler choice. SD3 always uses its own flow-matching sampler, so the Scheduler control is hidden.
- Prompt weighting (Compel syntax) does not apply to SD3. See Prompt Syntax.
Canvas
Section titled “Canvas”On the Canvas, SD3 supports text-to-image, image-to-image, inpainting and outpainting. ControlNet, reference images and regional guidance are not available for SD3.
SD 3 and SD 3.5 LoRAs are supported in the layouts trainers publish them in: the diffusers DreamBooth script (a folder
with pytorch_lora_weights.safetensors, or the file itself), ai-toolkit, PEFT adapters, SimpleTuner’s LyCORIS files
(LoKR) and kohya sd-scripts (lora_unet_joint_blocks_…, or lora_unet_transformer_blocks_… for trainers that keep
diffusers’ module names). A LoRA that also trains the CLIP-L and CLIP-G text encoders applies to them too; T5 layers
in a LoRA are skipped with a warning, because the T5 encoder takes no LoRA. DoRA files, and files with layers that
match no SD 3 module, are refused when you install them, as are files that mix SD 3 Medium and Large widths, name
blocks beyond the model’s depth, or name only SD 3 modules without one complete layer to tell the size by.
- Medium and Large. A LoRA is trained for one size: SD 3 Medium and SD 3.5 Medium share one, SD 3.5 Large (and Large Turbo) the other. Invoke records the size of LoRAs and of SD 3 models when you install them, and the concept picker only offers LoRAs that fit the selected model. SD 3 models installed with an earlier version carry no size; the picker then offers every SD 3 LoRA. To add it, select the model in the Model Manager and use Re-identify (which also resets the model’s default settings). A LoRA that does not fit is refused when the generation starts, before the transformer is moved to the GPU. Text-encoder work, including CLIP LoRAs, can already have run by then.
- SD 3.5 Medium LoRAs on SD 3 Medium. SD 3.5 Medium adds a second attention to its first 13 blocks. A LoRA that trains it does not fit SD 3 Medium and is refused there.
- FP8 Storage. With FP8 Storage the LoRA is applied alongside the weights instead of being merged into them; it then stays in VRAM for the whole generation, and InvokeAI reserves room for it.
Earlier versions identified some SD 3 LoRAs as Qwen-Image LoRAs and others not at all. Select such a LoRA in the Model Manager and use Re-identify to fix it. If the file is one Invoke now refuses (for example a LoRA with layers that match no SD 3 module, or an incomplete layer), Re-identify reports the reason and the model stays as it was; remove it or install a corrected file.
PiD super-resolution decode
Section titled “PiD super-resolution decode”SD3 works with PiD decoding. PiD replaces the VAE decode with a 4× super-resolving pixel diffusion pass. Two decoders are available as starter models: PiD Decoder SD3 (2K) and PiD Decoder SD3 (2K to 4K). Each is about 5 GB and installs the shared Gemma-2 caption encoder as a dependency. See PiD Super-Resolution Decode.
Workflow editor
Section titled “Workflow editor”The SD3 nodes are Main Model - SD3, Prompt - SD3, Denoise - SD3, Image to Latents - SD3 and Latents to Image - SD3. Latents to Image - SD3 + PiD (4x SR) decodes with PiD instead of the VAE.
The T5Encoder input of Prompt - SD3 is optional. SD3 was trained with text-encoder dropout, so leaving T5 unconnected still produces images and saves the time and memory of loading T5. When T5 is connected, it reads at most 256 tokens of the prompt.