🤖 Self-hosted, community-driven, local OpenAI-compatible API with Keycloak Auth Flak app as frontend. 🏠
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FlaskAI/api/openai/image.go

159 lines
3.4 KiB

package openai
import (
"encoding/base64"
"encoding/json"
"fmt"
"io/ioutil"
"os"
"path/filepath"
"strconv"
"strings"
"github.com/go-skynet/LocalAI/api/backend"
config "github.com/go-skynet/LocalAI/api/config"
"github.com/go-skynet/LocalAI/api/options"
model "github.com/go-skynet/LocalAI/pkg/model"
"github.com/gofiber/fiber/v2"
"github.com/rs/zerolog/log"
)
// https://platform.openai.com/docs/api-reference/images/create
/*
*
curl http://localhost:8080/v1/images/generations \
-H "Content-Type: application/json" \
-d '{
"prompt": "A cute baby sea otter",
"n": 1,
"size": "512x512"
}'
*
*/
func ImageEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fiber.Ctx) error {
return func(c *fiber.Ctx) error {
m, input, err := readInput(c, o.Loader, false)
if err != nil {
return fmt.Errorf("failed reading parameters from request:%w", err)
}
if m == "" {
m = model.StableDiffusionBackend
}
log.Debug().Msgf("Loading model: %+v", m)
config, input, err := readConfig(m, input, cm, o.Loader, o.Debug, 0, 0, false)
if err != nil {
return fmt.Errorf("failed reading parameters from request:%w", err)
}
log.Debug().Msgf("Parameter Config: %+v", config)
// XXX: Only stablediffusion is supported for now
if config.Backend == "" {
config.Backend = model.StableDiffusionBackend
}
sizeParts := strings.Split(input.Size, "x")
if len(sizeParts) != 2 {
return fmt.Errorf("Invalid value for 'size'")
}
width, err := strconv.Atoi(sizeParts[0])
if err != nil {
return fmt.Errorf("Invalid value for 'size'")
}
height, err := strconv.Atoi(sizeParts[1])
if err != nil {
return fmt.Errorf("Invalid value for 'size'")
}
b64JSON := false
if input.ResponseFormat == "b64_json" {
b64JSON = true
}
var result []Item
for _, i := range config.PromptStrings {
n := input.N
if input.N == 0 {
n = 1
}
for j := 0; j < n; j++ {
prompts := strings.Split(i, "|")
positive_prompt := prompts[0]
negative_prompt := ""
if len(prompts) > 1 {
negative_prompt = prompts[1]
}
mode := 0
step := 15
if input.Mode != 0 {
mode = input.Mode
}
if input.Step != 0 {
step = input.Step
}
tempDir := ""
if !b64JSON {
tempDir = o.ImageDir
}
// Create a temporary file
outputFile, err := ioutil.TempFile(tempDir, "b64")
if err != nil {
return err
}
outputFile.Close()
output := outputFile.Name() + ".png"
// Rename the temporary file
err = os.Rename(outputFile.Name(), output)
if err != nil {
return err
}
baseURL := c.BaseURL()
fn, err := backend.ImageGeneration(height, width, mode, step, input.Seed, positive_prompt, negative_prompt, output, o.Loader, *config, o)
if err != nil {
return err
}
if err := fn(); err != nil {
return err
}
item := &Item{}
if b64JSON {
defer os.RemoveAll(output)
data, err := os.ReadFile(output)
if err != nil {
return err
}
item.B64JSON = base64.StdEncoding.EncodeToString(data)
} else {
base := filepath.Base(output)
item.URL = baseURL + "/generated-images/" + base
}
result = append(result, *item)
}
}
resp := &OpenAIResponse{
Data: result,
}
jsonResult, _ := json.Marshal(resp)
log.Debug().Msgf("Response: %s", jsonResult)
// Return the prediction in the response body
return c.JSON(resp)
}
}