feat(img2vid,txt2vid): Initial support for img2vid,txt2vid (#1442)

* feat(img2vid): Initial support for img2vid

* doc(SD): fix SDXL Example

* Minor fixups for img2vid

* docs(img2img): fix example curl call

* feat(txt2vid): initial support

Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>

* diffusers: be retro-compatible with CUDA settings

* docs(img2vid, txt2vid): examples

* Add notice on docs

---------

Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>

api/backend/image.go

@@ -16,7 +16,7 @@ func ImageGeneration(height, width, mode, step, seed int, positive_prompt, negat
 		model.WithContext(o.Context),
 		model.WithModel(c.Model),
 		model.WithLoadGRPCLoadModelOpts(&proto.ModelOptions{
-			CUDA:          c.CUDA,
+			CUDA:          c.CUDA || c.Diffusers.CUDA,
 			SchedulerType: c.Diffusers.SchedulerType,
 			PipelineType:  c.Diffusers.PipelineType,
 			CFGScale:      c.Diffusers.CFGScale,

api/config/config.go

@@ -68,6 +68,7 @@ type GRPC struct {
 }
 
 type Diffusers struct {
+	CUDA             bool    `yaml:"cuda"`
 	PipelineType     string  `yaml:"pipeline_type"`
 	SchedulerType    string  `yaml:"scheduler_type"`
 	EnableParameters string  `yaml:"enable_parameters"` // A list of comma separated parameters to specify

api/openai/image.go

@@ -5,6 +5,8 @@ import (
 	"encoding/base64"
 	"encoding/json"
 	"fmt"
+	"io"
+	"net/http"
 	"os"
 	"path/filepath"
 	"strconv"
@@ -22,6 +24,26 @@ import (
 	"github.com/rs/zerolog/log"
 )
 
+func downloadFile(url string) (string, error) {
+	// Get the data
+	resp, err := http.Get(url)
+	if err != nil {
+		return "", err
+	}
+	defer resp.Body.Close()
+
+	// Create the file
+	out, err := os.CreateTemp("", "image")
+	if err != nil {
+		return "", err
+	}
+	defer out.Close()
+
+	// Write the body to file
+	_, err = io.Copy(out, resp.Body)
+	return out.Name(), err
+}
+
 // https://platform.openai.com/docs/api-reference/images/create
 
 /*
@@ -56,12 +78,31 @@ func ImageEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fiber.Ctx
 
 		src := ""
 		if input.File != "" {
+
+			fileData := []byte{}
+			// check if input.File is an URL, if so download it and save it
+			// to a temporary file
+			if strings.HasPrefix(input.File, "http://") || strings.HasPrefix(input.File, "https://") {
+				out, err := downloadFile(input.File)
+				if err != nil {
+					return fmt.Errorf("failed downloading file:%w", err)
+				}
+				defer os.RemoveAll(out)
+
+				fileData, err = os.ReadFile(out)
+				if err != nil {
+					return fmt.Errorf("failed reading file:%w", err)
+				}
+
+			} else {
 				// base 64 decode the file and write it somewhere
 				// that we will cleanup
-			decoded, err := base64.StdEncoding.DecodeString(input.File)
+				fileData, err = base64.StdEncoding.DecodeString(input.File)
 				if err != nil {
 					return err
 				}
+			}
+
 			// Create a temporary file
 			outputFile, err := os.CreateTemp(o.ImageDir, "b64")
 			if err != nil {
@@ -69,7 +110,7 @@ func ImageEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fiber.Ctx
 			}
 			// write the base64 result
 			writer := bufio.NewWriter(outputFile)
-			_, err = writer.Write(decoded)
+			_, err = writer.Write(fileData)
 			if err != nil {
 				outputFile.Close()
 				return err

backend/python/diffusers/backend_diffusers.py

@@ -18,9 +18,9 @@ import backend_pb2_grpc
 import grpc
 
 from diffusers import StableDiffusionXLPipeline, StableDiffusionDepth2ImgPipeline, DPMSolverMultistepScheduler, StableDiffusionPipeline, DiffusionPipeline, EulerAncestralDiscreteScheduler
-from diffusers import StableDiffusionImg2ImgPipeline, AutoPipelineForText2Image, ControlNetModel
+from diffusers import StableDiffusionImg2ImgPipeline, AutoPipelineForText2Image, ControlNetModel, StableVideoDiffusionPipeline
 from diffusers.pipelines.stable_diffusion import safety_checker
-from diffusers.utils import load_image
+from diffusers.utils import load_image,export_to_video
 from compel import Compel
 
 from transformers import CLIPTextModel
@@ -31,6 +31,10 @@ _ONE_DAY_IN_SECONDS = 60 * 60 * 24
 COMPEL=os.environ.get("COMPEL", "1") == "1"
 CLIPSKIP=os.environ.get("CLIPSKIP", "1") == "1"
 SAFETENSORS=os.environ.get("SAFETENSORS", "1") == "1"
+CHUNK_SIZE=os.environ.get("CHUNK_SIZE", "8")
+FPS=os.environ.get("FPS", "7")
+DISABLE_CPU_OFFLOAD=os.environ.get("DISABLE_CPU_OFFLOAD", "0") == "1"
+FRAMES=os.environ.get("FRAMES", "64")
 
 # If MAX_WORKERS are specified in the environment use it, otherwise default to 1
 MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
@@ -163,7 +167,8 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                     modelFile = request.ModelFile
             
             fromSingleFile = request.Model.startswith("http") or request.Model.startswith("/") or local
-            
+            self.img2vid=False
+            self.txt2vid=False
             ## img2img
             if (request.PipelineType == "StableDiffusionImg2ImgPipeline") or (request.IMG2IMG and request.PipelineType == ""):
                 if fromSingleFile:
@@ -179,6 +184,14 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                 self.pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(request.Model,
                             torch_dtype=torchType,
                             guidance_scale=cfg_scale)
+            ## img2vid
+            elif request.PipelineType == "StableVideoDiffusionPipeline":
+                self.img2vid=True
+                self.pipe = StableVideoDiffusionPipeline.from_pretrained(
+                    request.Model, torch_dtype=torchType, variant=variant
+                )
+                if not DISABLE_CPU_OFFLOAD:
+                    self.pipe.enable_model_cpu_offload()
             ## text2img
             elif request.PipelineType == "AutoPipelineForText2Image" or request.PipelineType == "":
                 self.pipe = AutoPipelineForText2Image.from_pretrained(request.Model,
@@ -199,6 +212,11 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                 self.pipe = DiffusionPipeline.from_pretrained(request.Model,
                                                         torch_dtype=torchType,
                                                         guidance_scale=cfg_scale)
+            elif request.PipelineType == "VideoDiffusionPipeline":
+                self.txt2vid=True
+                self.pipe = DiffusionPipeline.from_pretrained(request.Model,
+                                                        torch_dtype=torchType,
+                                                        guidance_scale=cfg_scale)
             elif request.PipelineType == "StableDiffusionXLPipeline":
                 if fromSingleFile:
                     self.pipe = StableDiffusionXLPipeline.from_single_file(modelFile,
@@ -222,6 +240,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
             if request.SchedulerType != "":
                 self.pipe.scheduler = get_scheduler(request.SchedulerType, self.pipe.scheduler.config)
                 
+            if not self.img2vid:
                 self.compel = Compel(tokenizer=self.pipe.tokenizer, text_encoder=self.pipe.text_encoder)
 
 
@@ -331,7 +350,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
             "num_inference_steps": steps,
         }
 
-        if request.src != "" and not self.controlnet:
+        if request.src != "" and not self.controlnet and not self.img2vid:
             image = Image.open(request.src)
             options["image"] = image
         elif self.controlnet and request.src:
@@ -359,6 +378,21 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                 request.seed
             )
 
+        if self.img2vid:
+            # Load the conditioning image
+            image = load_image(request.src)
+            image = image.resize((1024, 576))
+
+            generator = torch.manual_seed(request.seed)
+            frames = self.pipe(image, decode_chunk_size=CHUNK_SIZE, generator=generator).frames[0]
+            export_to_video(frames, request.dst, fps=FPS)
+            return backend_pb2.Result(message="Media generated successfully", success=True)
+
+        if self.txt2vid:
+            video_frames = self.pipe(prompt, num_inference_steps=steps, num_frames=int(FRAMES)).frames
+            export_to_video(video_frames, request.dst)
+            return backend_pb2.Result(message="Media generated successfully", success=True)
+
         image = {}
         if COMPEL:
             conditioning = self.compel.build_conditioning_tensor(prompt)
@@ -377,7 +411,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         # save the result
         image.save(request.dst)
 
-        return backend_pb2.Result(message="Model loaded successfully", success=True)
+        return backend_pb2.Result(message="Media generated", success=True)
 
 def serve(address):
     server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))

docs/content/features/image-generation.md

@@ -147,7 +147,6 @@ backend: diffusers
 # Force CPU usage - set to true for GPU
 f16: false
 diffusers:
-  pipeline_type: StableDiffusionXLPipeline
   cuda: false # Enable for GPU usage (CUDA)
   scheduler_type: euler_a
 ```

docs/content/howtos/easy-setup-sd.md

@@ -15,7 +15,6 @@ backend: diffusers
 # Force CPU usage - set to true for GPU
 f16: false
 diffusers:
-  pipeline_type: StableDiffusionXLPipeline
   cuda: false # Enable for GPU usage (CUDA)
   scheduler_type: dpm_2_a
 ```

docs/content/model-compatibility/diffusers.md

@@ -27,12 +27,9 @@ name: animagine-xl
 parameters:
   model: Linaqruf/animagine-xl
 backend: diffusers
-
+cuda: true
-# Force CPU usage - set to true for GPU
+f16: true
-f16: false
 diffusers:
-  pipeline_type: StableDiffusionXLPipeline
-  cuda: false # Enable for GPU usage (CUDA)
   scheduler_type: euler_a
 ```
 
@@ -47,9 +44,9 @@ parameters:
 backend: diffusers
 step: 30
 f16: true
+cuda: true
 diffusers:
   pipeline_type: StableDiffusionPipeline
-  cuda: true
   enable_parameters: "negative_prompt,num_inference_steps,clip_skip"
   scheduler_type: "k_dpmpp_sde"
   cfg_scale: 8
@@ -69,7 +66,7 @@ The following parameters are available in the configuration file:
 | `scheduler_type` | Scheduler type | `k_dpp_sde` |
 | `cfg_scale` | Configuration scale | `8` |
 | `clip_skip` | Clip skip | None |
-| `pipeline_type` | Pipeline type | `StableDiffusionPipeline` |
+| `pipeline_type` | Pipeline type | `AutoPipelineForText2Image` |
 
 There are available several types of schedulers:
 
@@ -131,17 +128,16 @@ parameters:
   model: nitrosocke/Ghibli-Diffusion
 backend: diffusers
 step: 25
-
+cuda: true
 f16: true
 diffusers:
   pipeline_type: StableDiffusionImg2ImgPipeline
-  cuda: true
   enable_parameters: "negative_prompt,num_inference_steps,image"
 ```
 
 ```bash
 IMAGE_PATH=/path/to/your/image
-(echo -n '{"image": "'; base64 $IMAGE_PATH; echo '", "prompt": "a sky background","size": "512x512","model":"stablediffusion-edit"}') |
+(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
 ```
 
@@ -157,14 +153,67 @@ backend: diffusers
 step: 50
 # Force CPU usage
 f16: true
+cuda: true
 diffusers:
   pipeline_type: StableDiffusionDepth2ImgPipeline
-  cuda: true
   enable_parameters: "negative_prompt,num_inference_steps,image"
   cfg_scale: 6
 ```
 
 ```bash
-(echo -n '{"image": "'; base64 ~/path/to/image.jpeg; echo '", "prompt": "a sky background","size": "512x512","model":"stablediffusion-depth"}') |
+(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
+
+{{% notice note %}}
+
+Experimental and available only on master builds. See: https://github.com/mudler/LocalAI/pull/1442
+
+{{% /notice %}}
+
+```yaml
+name: img2vid
+parameters:
+  model: stabilityai/stable-video-diffusion-img2vid
+backend: diffusers
+step: 25
+# Force CPU usage
+f16: true
+cuda: true
+diffusers:
+  pipeline_type: StableVideoDiffusionPipeline
+```
+
+```bash
+(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
+
+{{% notice note %}}
+
+Experimental and available only on master builds. See: https://github.com/mudler/LocalAI/pull/1442
+
+{{% /notice %}}
+
+```yaml
+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
+```
+
+```bash
+(echo -n '{"prompt": "spiderman surfing","size": "512x512","model":"txt2vid"}') |
+curl -H "Content-Type: application/json" -X POST -d @- http://localhost:8080/v1/images/generations
+```