šØ Image generation
(Generated with AnimagineXL)
LocalAI supports generating images with Stable diffusion, running on CPU using C++ and Python implementations.
Usage
OpenAI docs: https://platform.openai.com/docs/api-reference/images/create
To generate an image you can send a POST request to the /v1/images/generations
endpoint with the instruction as the request body:
# 512x512 is supported too
curl http://localhost:8080/v1/images/generations -H "Content-Type: application/json" -d '{
"prompt": "A cute baby sea otter",
"size": "256x256"
}'
Available additional parameters: mode
, step
.
Note: To set a negative prompt, you can split the prompt with |
, for instance: a cute baby sea otter|malformed
.
curl http://localhost:8080/v1/images/generations -H "Content-Type: application/json" -d '{
"prompt": "floating hair, portrait, ((loli)), ((one girl)), cute face, hidden hands, asymmetrical bangs, beautiful detailed eyes, eye shadow, hair ornament, ribbons, bowties, buttons, pleated skirt, (((masterpiece))), ((best quality)), colorful|((part of the head)), ((((mutated hands and fingers)))), deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, missing limb, blurry, floating limbs, disconnected limbs, malformed hands, blur, out of focus, long neck, long body, Octane renderer, lowres, bad anatomy, bad hands, text",
"size": "256x256"
}'
Backends
stablediffusion-ggml
This backend is based on stable-diffusion.cpp. Every model supported by that backend is suppoerted indeed with LocalAI.
Setup
There are already several models in the gallery that are available to install and get up and running with this backend, you can for example run flux by searching it in the Model gallery (flux.1-dev-ggml
) or start LocalAI with run
:
local-ai run flux.1-dev-ggml
To use a custom model, you can follow these steps:
- Create a model file
stablediffusion.yaml
in the models folder:
name: stablediffusion
backend: stablediffusion-ggml
parameters:
model: gguf_model.gguf
step: 25
cfg_scale: 4.5
options:
- "clip_l_path:clip_l.safetensors"
- "clip_g_path:clip_g.safetensors"
- "t5xxl_path:t5xxl-Q5_0.gguf"
- "sampler:euler"
- Download the required assets to the
models
repository - Start LocalAI
Diffusers
Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. LocalAI has a diffusers backend which allows image generation using the diffusers
library.
(Generated with AnimagineXL)
Model setup
The models will be downloaded the first time you use the backend from huggingface
automatically.
Create a model configuration file in the models
directory, for instance to use Linaqruf/animagine-xl
with CPU:
name: animagine-xl
parameters:
model: Linaqruf/animagine-xl
backend: diffusers
# Force CPU usage - set to true for GPU
f16: false
diffusers:
cuda: false # Enable for GPU usage (CUDA)
scheduler_type: euler_a
Dependencies
This is an extra backend - in the container is already available and there is nothing to do for the setup. Do not use core images (ending with -core
). If you are building manually, see the build instructions.
Model setup
The models will be downloaded the first time you use the backend from huggingface
automatically.
Create a model configuration file in the models
directory, for instance to use Linaqruf/animagine-xl
with CPU:
name: animagine-xl
parameters:
model: Linaqruf/animagine-xl
backend: diffusers
cuda: true
f16: true
diffusers:
scheduler_type: euler_a
Local models
You can also use local models, or modify some parameters like clip_skip
, scheduler_type
, for instance:
name: stablediffusion
parameters:
model: toonyou_beta6.safetensors
backend: diffusers
step: 30
f16: true
cuda: true
diffusers:
pipeline_type: StableDiffusionPipeline
enable_parameters: "negative_prompt,num_inference_steps,clip_skip"
scheduler_type: "k_dpmpp_sde"
clip_skip: 11
cfg_scale: 8
Configuration parameters
The following parameters are available in the configuration file:
Parameter | Description | Default |
---|---|---|
f16 | Force the usage of float16 instead of float32 | false |
step | Number of steps to run the model for | 30 |
cuda | Enable CUDA acceleration | false |
enable_parameters | Parameters to enable for the model | negative_prompt,num_inference_steps,clip_skip |
scheduler_type | Scheduler type | k_dpp_sde |
cfg_scale | Configuration scale | 8 |
clip_skip | Clip skip | None |
pipeline_type | Pipeline type | AutoPipelineForText2Image |
lora_adapters | A list of lora adapters (file names relative to model directory) to apply | None |
lora_scales | A list of lora scales (floats) to apply | None |
There are available several types of schedulers:
Scheduler | Description |
---|---|
ddim | DDIM |
pndm | PNDM |
heun | Heun |
unipc | UniPC |
euler | Euler |
euler_a | Euler a |
lms | LMS |
k_lms | LMS Karras |
dpm_2 | DPM2 |
k_dpm_2 | DPM2 Karras |
dpm_2_a | DPM2 a |
k_dpm_2_a | DPM2 a Karras |
dpmpp_2m | DPM++ 2M |
k_dpmpp_2m | DPM++ 2M Karras |
dpmpp_sde | DPM++ SDE |
k_dpmpp_sde | DPM++ SDE Karras |
dpmpp_2m_sde | DPM++ 2M SDE |
k_dpmpp_2m_sde | DPM++ 2M SDE Karras |
Pipelines types available:
Pipeline type | Description |
---|---|
StableDiffusionPipeline | Stable diffusion pipeline |
StableDiffusionImg2ImgPipeline | Stable diffusion image to image pipeline |
StableDiffusionDepth2ImgPipeline | Stable diffusion depth to image pipeline |
DiffusionPipeline | Diffusion pipeline |
StableDiffusionXLPipeline | Stable diffusion XL pipeline |
StableVideoDiffusionPipeline | Stable video diffusion pipeline |
AutoPipelineForText2Image | Automatic detection pipeline for text to image |
VideoDiffusionPipeline | Video diffusion pipeline |
StableDiffusion3Pipeline | Stable diffusion 3 pipeline |
FluxPipeline | Flux pipeline |
FluxTransformer2DModel | Flux transformer 2D model |
SanaPipeline | Sana pipeline |
Advanced: Additional parameters
Additional arbitrarly parameters can be specified in the option field in key/value separated by :
:
name: animagine-xl
# ...
options:
- "cfg_scale:6"
Note: There is no complete parameter list. Any parameter can be passed arbitrarly and is passed to the model directly as argument to the pipeline. Different pipelines/implementations support different parameters.
The example above, will result in the following python code when generating images:
pipe(
prompt="A cute baby sea otter", # Options passed via API
size="256x256", # Options passed via API
cfg_scale=6 # Additional parameter passed via configuration file
)
Usage
Text to Image
Use the image
generation endpoint with the model
name from the configuration file:
curl http://localhost:8080/v1/images/generations \
-H "Content-Type: application/json" \
-d '{
"prompt": "<positive prompt>|<negative prompt>",
"model": "animagine-xl",
"step": 51,
"size": "1024x1024"
}'
Image to Image
https://huggingface.co/docs/diffusers/using-diffusers/img2img
An example model (GPU):
name: stablediffusion-edit
parameters:
model: nitrosocke/Ghibli-Diffusion
backend: diffusers
step: 25
cuda: true
f16: true
diffusers:
pipeline_type: StableDiffusionImg2ImgPipeline
enable_parameters: "negative_prompt,num_inference_steps,image"
IMAGE_PATH=/path/to/your/image
(echo -n '{"file": "'; base64 $IMAGE_PATH; echo '", "prompt": "a sky background","size": "512x512","model":"stablediffusion-edit"}') |
curl -H "Content-Type: application/json" -d @- http://localhost:8080/v1/images/generations
Depth to Image
https://huggingface.co/docs/diffusers/using-diffusers/depth2img
name: stablediffusion-depth
parameters:
model: stabilityai/stable-diffusion-2-depth
backend: diffusers
step: 50
# Force CPU usage
f16: true
cuda: true
diffusers:
pipeline_type: StableDiffusionDepth2ImgPipeline
enable_parameters: "negative_prompt,num_inference_steps,image"
cfg_scale: 6
(echo -n '{"file": "'; base64 ~/path/to/image.jpeg; echo '", "prompt": "a sky background","size": "512x512","model":"stablediffusion-depth"}') |
curl -H "Content-Type: application/json" -d @- http://localhost:8080/v1/images/generations
img2vid
name: img2vid
parameters:
model: stabilityai/stable-video-diffusion-img2vid
backend: diffusers
step: 25
# Force CPU usage
f16: true
cuda: true
diffusers:
pipeline_type: StableVideoDiffusionPipeline
(echo -n '{"file": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png?download=true","size": "512x512","model":"img2vid"}') |
curl -H "Content-Type: application/json" -X POST -d @- http://localhost:8080/v1/images/generations
txt2vid
name: txt2vid
parameters:
model: damo-vilab/text-to-video-ms-1.7b
backend: diffusers
step: 25
# Force CPU usage
f16: true
cuda: true
diffusers:
pipeline_type: VideoDiffusionPipeline
cuda: true
(echo -n '{"prompt": "spiderman surfing","size": "512x512","model":"txt2vid"}') |
curl -H "Content-Type: application/json" -X POST -d @- http://localhost:8080/v1/images/generations
Last updated 17 Feb 2025, 16:51 +0100 .