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🎨 Image generation

anime_girl (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-cpp

mode=0	mode=1 (winograd/sgemm)

Note: image generator supports images up to 512x512. You can use other tools however to upscale the image, for instance: https://github.com/upscayl/upscayl.

Setup

Note: In order to use the images/generation endpoint with the stablediffusion C++ backend, you need to build LocalAI with GO_TAGS=stablediffusion. If you are using the container images, it is already enabled.

While the API is running, you can install the model by using the /models/apply endpoint and point it to the stablediffusion model in the models-gallery:

  curl http://localhost:8080/models/apply -H "Content-Type: application/json" -d '{
  "url": "github:go-skynet/model-gallery/stablediffusion.yaml"
}'

You can set the PRELOAD_MODELS environment variable:

  PRELOAD_MODELS=[{"url": "github:go-skynet/model-gallery/stablediffusion.yaml"}]

or as arg:

  local-ai --preload-models '[{"url": "github:go-skynet/model-gallery/stablediffusion.yaml"}]'

or in a YAML file:

  local-ai --preload-models-config "/path/to/yaml"

YAML:

  - url: github:go-skynet/model-gallery/stablediffusion.yaml

Create a model file stablediffusion.yaml in the models folder:

  name: stablediffusion
backend: stablediffusion
parameters:
  model: stablediffusion_assets

Create a stablediffusion_assets directory inside your models directory
Download the ncnn assets from https://github.com/EdVince/Stable-Diffusion-NCNN#out-of-box and place them in stablediffusion_assets.

The models directory should look like the following:

  models
├── stablediffusion_assets
│   ├── AutoencoderKL-256-256-fp16-opt.param
│   ├── AutoencoderKL-512-512-fp16-opt.param
│   ├── AutoencoderKL-base-fp16.param
│   ├── AutoencoderKL-encoder-512-512-fp16.bin
│   ├── AutoencoderKL-fp16.bin
│   ├── FrozenCLIPEmbedder-fp16.bin
│   ├── FrozenCLIPEmbedder-fp16.param
│   ├── log_sigmas.bin
│   ├── tmp-AutoencoderKL-encoder-256-256-fp16.param
│   ├── UNetModel-256-256-MHA-fp16-opt.param
│   ├── UNetModel-512-512-MHA-fp16-opt.param
│   ├── UNetModel-base-MHA-fp16.param
│   ├── UNetModel-MHA-fp16.bin
│   └── vocab.txt
└── stablediffusion.yaml

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.

anime_girl (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`

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

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

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Last updated 03 Dec 2024, 22:41 +0100 . history

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🎨 Image generation

Usage link

Backends link

stablediffusion-cpp link

Setup link

Diffusers link

Model setup link

Dependencies link

Model setup link

Local models link

Configuration parameters link

Usage link

Text to Image link

Image to Image link

Depth to Image link

img2vid link

txt2vid link

Usage

Backends

stablediffusion-cpp

Setup

Diffusers

Model setup

Dependencies

Model setup

Local models

Configuration parameters

Usage

Text to Image

Image to Image

Depth to Image

img2vid

txt2vid