Create handler_LAST_WORKING.py

handler_LAST_WORKING.py

@@ -0,0 +1,510 @@
+from dataclasses import dataclass
+from pathlib import Path
+import pathlib
+from typing import Dict, Any, Optional, Tuple
+import asyncio
+import base64
+import io
+import pprint
+import logging
+import random
+import traceback
+import os
+import numpy as np
+import torch
+import gc
+
+from diffusers import AutoencoderKLLTXVideo, LTXPipeline, LTXImageToVideoPipeline, LTXVideoTransformer3DModel
+#from diffusers.hooks import apply_enhance_a_video, EnhanceAVideoConfig
+#from teacache import apply_teacache
+
+from PIL import Image
+
+from varnish import Varnish
+from varnish.utils import is_truthy, process_input_image
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+# Get token from environment
+hf_token = os.getenv("HF_API_TOKEN")
+
+# Constraints
+MAX_LARGE_SIDE = 1280
+MAX_SMALL_SIDE = 768 # should be 720 but it must be divisible by 32
+MAX_FRAMES = 257
+
+# Check environment variable for pipeline support
+support_image_prompt = is_truthy(os.getenv("SUPPORT_INPUT_IMAGE_PROMPT"))
+
+@dataclass
+class GenerationConfig:
+    """Configuration for video generation"""
+
+    # general content settings
+    prompt: str = ""
+    negative_prompt: str = "saturated, highlight, overexposed, highlighted, overlit, shaking, too bright, worst quality, inconsistent motion, blurry, jittery, distorted, cropped, watermarked, watermark, logo, subtitle, subtitles, lowres"
+
+    # video model settings (will be used during generation of the initial raw video clip)
+    # we use small values to make things a bit faster
+    width: int = 768
+    height: int = 416
+
+
+    # this is a hack to fool LTX-Video into believing our input image is an actual video frame with poor encoding quality
+    # after a quick benchmark using the value 70 seems like a sweet spot
+    input_image_quality: int = 70
+
+    # users may tend to always set this to the max, to get as much useable content as possible (which is MAX_FRAMES ie. 257).
+    # The value must be a multiple of 8, plus 1 frame.
+    # visual glitches appear after about 169 frames, so we don't need more actually
+    num_frames: int = (8 * 14) + 1
+
+    # with the distilled model, a guidance scale of 1.0 is fine
+    guidance_scale: float = 1.0
+    
+    num_inference_steps: int = 8
+
+    # reproducible generation settings
+    seed: int = -1  # -1 means random seed
+
+    # varnish settings (will be used for post-processing after the raw video clip has been generated
+    fps: int = 30 # FPS of the final video (only applied at the the very end, when converting to mp4)
+    double_num_frames: bool = False # if True, the number of frames will be multiplied by 2 using RIFE
+    super_resolution: bool = False # if True, the resolution will be multiplied by 2 using Real_ESRGAN
+    
+    grain_amount: float = 0.0 # be careful, adding film grian can negatively impact video compression
+
+    # audio settings
+    enable_audio: bool = False  # Whether to generate audio
+    audio_prompt: str = ""  # Text prompt for audio generation
+    audio_negative_prompt: str = "voices, voice, talking, speaking, speech" # Negative prompt for audio generation
+
+    # The range of the CRF scale is 0–51, where:
+    # 0 is lossless (for 8 bit only, for 10 bit use -qp 0)
+    # 23 is the default
+    # 51 is worst quality possible
+    # A lower value generally leads to higher quality, and a subjectively sane range is 17–28.
+    # Consider 17 or 18 to be visually lossless or nearly so;
+    # it should look the same or nearly the same as the input but it isn't technically lossless.
+    # The range is exponential, so increasing the CRF value +6 results in roughly half the bitrate / file size, while -6 leads to roughly twice the bitrate.
+    quality: int = 18
+
+    # TeaCache settings
+    enable_teacache: bool = False
+    teacache_threshold: float = 0.05 # values: 0 (original), 0.03 (1.6x speedup), 0.05 (2.1x speedup).
+
+    # Enhance-A-Video settings
+    enable_enhance_a_video: bool = False
+    enhance_a_video_weight: float = 5.0
+
+    # LoRA settings
+    lora_model_name: str = ""  # HuggingFace repo ID or path to LoRA model
+    lora_model_weight_file: str = ""  # Specific weight file to load from the LoRA model
+    lora_model_trigger: str = ""  # Optional trigger word to prepend to the prompt
+    
+    def validate_and_adjust(self) -> 'GenerationConfig':
+        """Validate and adjust parameters to meet constraints"""
+        # First check if it's one of our explicitly allowed resolutions
+        if not ((self.width == MAX_LARGE_SIDE and self.height == MAX_SMALL_SIDE) or 
+                (self.width == MAX_SMALL_SIDE and self.height == MAX_LARGE_SIDE)):
+            # For other resolutions, ensure total pixels don't exceed max
+            MAX_TOTAL_PIXELS = MAX_SMALL_SIDE * MAX_LARGE_SIDE # or 921600 = 1280 * 720
+            
+            # If total pixels exceed maximum, scale down proportionally
+            total_pixels = self.width * self.height
+            if total_pixels > MAX_TOTAL_PIXELS:
+                scale = (MAX_TOTAL_PIXELS / total_pixels) ** 0.5
+                self.width = max(128, min(MAX_LARGE_SIDE, round(self.width * scale / 32) * 32))
+                self.height = max(128, min(MAX_LARGE_SIDE, round(self.height * scale / 32) * 32))
+            else:
+                # Round dimensions to nearest multiple of 32
+                self.width = max(128, min(MAX_LARGE_SIDE, round(self.width / 32) * 32))
+                self.height = max(128, min(MAX_LARGE_SIDE, round(self.height / 32) * 32))
+        
+        # Adjust number of frames to be in format 8k + 1
+        k = (self.num_frames - 1) // 8
+        self.num_frames = min((k * 8) + 1, MAX_FRAMES)
+    
+        # Set random seed if not specified
+        if self.seed == -1:
+            self.seed = random.randint(0, 2**32 - 1)
+    
+        return self
+
+class EndpointHandler:
+    """Handles video generation requests using LTX models and Varnish post-processing"""
+    
+    def __init__(self, model_path: str = ""):
+        """Initialize the handler with LTX models and Varnish
+
+        Args:
+            model_path: Path to LTX model weights
+        """
+        print("EndpointHandler.__init__(): initializing..")
+        # Enable TF32 for potential speedup on Ampere GPUs
+        #torch.backends.cuda.matmul.allow_tf32 = True
+
+        # use distilled weights
+        model_path = Path("/repository/ltxv-2b-0.9.6-distilled-04-25.safetensors")
+
+        print("EndpointHandler.__init__(): initializing LTXVideoTransformer3DModel..")
+        transformer = LTXVideoTransformer3DModel.from_single_file(
+            model_path, torch_dtype=torch.bfloat16
+        )
+
+        print("EndpointHandler.__init__(): initializing AutoencoderKLLTXVideo..")
+        vae = AutoencoderKLLTXVideo.from_single_file(model_path, torch_dtype=torch.bfloat16)
+
+        if support_image_prompt:
+            print("EndpointHandler.__init__(): initializing LTXImageToVideoPipeline..")
+            self.image_to_video = LTXImageToVideoPipeline.from_pretrained(
+                "/repository",
+                transformer=transformer,
+                vae=vae,
+                torch_dtype=torch.bfloat16
+            ).to("cuda")
+
+            #apply_teacache(self.image_to_video)
+
+            # Compilation requires some time to complete, so it is best suited for
+            # situations where you prepare your pipeline once and then perform the 
+            # same type of inference operations multiple times.
+            # For example, calling the compiled pipeline on a different image size
+            # triggers compilation again which can be expensive.
+            #self.image_to_video = torch.compile(self.image_to_video, mode="reduce-overhead", fullgraph=True)
+
+        else:
+            print("EndpointHandler.__init__(): initializing LTXPipeline..")
+            # Initialize models with bfloat16 precision
+            self.text_to_video = LTXPipeline.from_pretrained(
+                "/repository",
+                transformer=transformer,
+                vae=vae,
+                torch_dtype=torch.bfloat16
+            ).to("cuda")
+            
+            #apply_teacache(self.text_to_video)
+
+            # Compilation requires some time to complete, so it is best suited for
+            # situations where you prepare your pipeline once and then perform the 
+            # same type of inference operations multiple times.
+            # For example, calling the compiled pipeline on a different image size
+            # triggers compilation again which can be expensive.
+            #self.text_to_video = torch.compile(self.text_to_video, mode="reduce-overhead", fullgraph=True)
+
+
+        # Initialize LoRA tracking
+        self._current_lora_model = None
+
+        #if support_image_prompt:
+        #    # Enable CPU offload for memory efficiency
+        #    self.image_to_video.enable_model_cpu_offload()
+        #    # Inject enhance-a-video functionality
+        #    inject_enhance_for_ltx(self.image_to_video.transformer)
+        #else:
+        #    # Enable CPU offload for memory efficiency
+        #    self.text_to_video.enable_model_cpu_offload()
+        #    # Inject enhance-a-video functionality
+        #    inject_enhance_for_ltx(self.text_to_video.transformer)
+         
+
+        # Initialize Varnish for post-processing
+        self.varnish = Varnish(
+            device="cuda",
+            model_base_dir="/repository/varnish",
+
+            # there is currently a bug with MMAudio and/or torch and/or the weight format and/or version..
+            # not sure how to fix that.. :/
+            #
+            # it says:
+            #   File "dist-packages/varnish.py", line 152, in __init__
+            #     self._setup_mmaudio()
+            #   File "dist-packages/varnish/varnish.py", line 165, in _setup_mmaudio
+            #     net.load_weights(torch.load(model.model_path, map_location=self.device, weights_only=False))
+            #                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+            #   File "dist-packages/torch/serialization.py", line 1384, in load
+            #     return _legacy_load(
+            #            ^^^^^^^^^^^^^
+            #   File "dist-packages/torch/serialization.py", line 1628, in _legacy_load
+            #     magic_number = pickle_module.load(f, **pickle_load_args)
+            #                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+            # _pickle.UnpicklingError: invalid load key, '<'.
+            enable_mmaudio=False,
+        )
+
+        # Determine if TeaCache is already installed or not
+        self.text_to_video_teacache = False
+        self.image_to_video_teacache = False
+
+
+    async def process_frames(
+        self,
+        frames: torch.Tensor,
+        config: GenerationConfig
+    ) -> tuple[str, dict]:
+        """Post-process generated frames using Varnish
+        
+        Args:
+            frames: Generated video frames tensor
+            config: Generation configuration
+            
+        Returns:
+            Tuple of (video data URI, metadata dictionary)
+        """
+        try:
+            # Process video with Varnish
+            result = await self.varnish(
+                input_data=frames, # note: this might contain a certain number of frames eg. 97, which will get doubled if double_num_frames is True
+                fps=config.fps, # this is the FPS of the final output video. This number can be used by Varnish to calculate the duration of a clip ((using frames * factor) / fps etc)
+                double_num_frames=config.double_num_frames, # if True, the number of frames will be multiplied by 2 using RIFE
+                super_resolution=config.super_resolution, # if True, the resolution will be multiplied by 2 using Real_ESRGAN
+                grain_amount=config.grain_amount,
+                enable_audio=config.enable_audio,
+                audio_prompt=config.audio_prompt,
+                audio_negative_prompt=config.audio_negative_prompt, 
+            )
+            
+            # Convert to data URI
+            video_uri = await result.write(type="data-uri", quality=config.quality)
+            
+            # Collect metadata
+            metadata = {
+                "width": result.metadata.width,
+                "height": result.metadata.height,
+                "num_frames": result.metadata.frame_count,
+                "fps": result.metadata.fps,
+                "duration": result.metadata.duration,
+                "seed": config.seed,
+            }
+            
+            return video_uri, metadata
+    
+        except Exception as e:
+            logger.error(f"Error in process_frames: {str(e)}")
+            raise RuntimeError(f"Failed to process frames: {str(e)}")
+
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """Process incoming requests for video generation
+        
+        Args:
+            data: Request data containing:
+                - inputs (dict): Dictionary containing input, which can be either "prompt" (text field) or "image" (input image)
+                - parameters (dict):
+                    - prompt (required, string): list of concepts to keep in the video.
+                    - negative_prompt (optional, string): list of concepts to ignore in the video.
+                    - width (optional, int, default to 768): width, or horizontal size in pixels.
+                    - height (optional, int, default to 512): height, or vertical size in pixels.
+                    - input_image_quality (optional, int, default to 100): this is a trick we use to convert a "pristine" image into a "dirty" video frame. This helps fooling LTX-Video into turning the image into an animated one.
+                    - num_frames (optional, int, default to 129): the numer of frames must be a multiple of 8, plus 1 frame.
+                    - guidance_scale (optional, float, default to 3.5): Guidance scale (values between 3.0 and 4.0 are nice)
+                    - num_inference_steps (optional, int, default to 50): number of inference steps
+                    - seed (optional, int, default to -1): set a random number generator seed, -1 means random seed.
+                    - fps (optional, int, default to 24): FPS of the final video (eg. 24, 25, 30, 60)
+                    - double_num_frames (optional, bool): if enabled, the number of frames will be multiplied by 2 using RIFE
+                    - super_resolution (optional, bool): if enabled, the resolution will be multiplied by 2 using Real_ESRGAN
+                    - grain_amount (optional, float): amount of film grain to add to the output video
+                    - enable_audio (optional, bool): automatically generate an audio track
+                    - audio_prompt (optional, str): prompt to use for the audio generation (concepts to add)
+                    - audio_negative_prompt (optional, str): nehative prompt to use for the audio generation (concepts to ignore)
+                    - quality (optional, str, default to 18): The range of the CRF scale is 0–51, where 0 is lossless (for 8 bit only, for 10 bit use -qp 0), 23 is the default, and 51 is worst quality possible.
+                    - enable_teacache (optional, bool, default to False): Generate faster at the cost of a slight quality loss
+                    - teacache_threshold (optional, float, default to 0.05): Amount of cache, 0 (original), 0.03 (1.6x speedup), 0.05 (Default, 2.1x speedup).
+                    - enable_enhance_a_video (optional, bool, default to False): enable the enhance_a_video optimization
+                    - enhance_a_video_weight(optional, float, default to 5.0): amount of video enhancement to apply
+                    - lora_model_name(optional, str, default to ""): HuggingFace repo ID or path to LoRA model
+                    - lora_model_weight_file(optional, str, default to ""): Specific weight file to load from the LoRA model
+                    - lora_model_trigger(optional, str, default to ""): Optional trigger word to prepend to the prompt
+        Returns:
+            Dictionary containing:
+                - video: Base64 encoded MP4 data URI
+                - content-type: MIME type
+                - metadata: Generation metadata
+        """
+        inputs = data.get("inputs", dict())
+        #print(inputs)
+        
+        input_prompt = inputs.get("prompt", "")
+        input_image = inputs.get("image")
+        
+        params = data.get("parameters", dict())
+
+        if not input_image and not input_prompt:
+            raise ValueError("Either prompt or image must be provided")
+
+        #logger.debug(f"Raw parameters:")
+        # pprint.pprint(params)
+
+        # Create and validate configuration
+        config = GenerationConfig(
+            # general content settings
+            prompt=input_prompt,
+            negative_prompt=params.get("negative_prompt", GenerationConfig.negative_prompt),
+
+            # video model settings (will be used during generation of the initial raw video clip)
+            width=params.get("width", GenerationConfig.width),
+            height=params.get("height", GenerationConfig.height),
+            input_image_quality=params.get("input_image_quality", GenerationConfig.input_image_quality),
+            num_frames=params.get("num_frames", GenerationConfig.num_frames),
+            guidance_scale=params.get("guidance_scale", GenerationConfig.guidance_scale),
+            num_inference_steps=params.get("num_inference_steps", GenerationConfig.num_inference_steps),
+
+            # reproducible generation settings
+            seed=params.get("seed", GenerationConfig.seed),
+            
+            # varnish settings (will be used for post-processing after the raw video clip has been generated)
+            fps=params.get("fps", GenerationConfig.fps), # FPS of the final video (only applied at the the very end, when converting to mp4)
+            double_num_frames=params.get("double_num_frames", GenerationConfig.double_num_frames), # if True, the number of frames will be multiplied by 2 using RIFE
+            super_resolution=params.get("super_resolution", GenerationConfig.super_resolution), # if True, the resolution will be multiplied by 2 using Real_ESRGAN
+            grain_amount=params.get("grain_amount", GenerationConfig.grain_amount),
+            enable_audio=params.get("enable_audio", GenerationConfig.enable_audio),
+            audio_prompt=params.get("audio_prompt", GenerationConfig.audio_prompt),
+            audio_negative_prompt=params.get("audio_negative_prompt", GenerationConfig.audio_negative_prompt),
+            quality=params.get("quality", GenerationConfig.quality),
+            
+            # TeaCache settings
+            enable_teacache=params.get("enable_teacache", False),
+
+            # values: 0 (original), 0.03 (1.6x speedup), 0.05 (2.1x speedup).
+            teacache_threshold=params.get("teacache_threshold", 0.05),
+
+            
+            # Add enhance-a-video settings
+            enable_enhance_a_video=params.get("enable_enhance_a_video", False),
+            enhance_a_video_weight=params.get("enhance_a_video_weight", 5.0),
+
+            # LoRA settings
+            lora_model_name=params.get("lora_model_name", ""),
+            lora_model_weight_file=params.get("lora_model_weight_file", ""),
+            lora_model_trigger=params.get("lora_model_trigger", ""),
+        ).validate_and_adjust()
+        
+        #logger.debug(f"Global request settings:")
+        #pprint.pprint(config)
+
+        try:
+            with torch.amp.autocast_mode.autocast('cuda', torch.bfloat16), torch.no_grad(), torch.inference_mode():
+                # Set random seeds
+                random.seed(config.seed)
+                np.random.seed(config.seed)
+                torch.manual_seed(config.seed)
+                generator = torch.Generator(device='cuda')
+                generator = generator.manual_seed(config.seed)
+
+                # Configure enhance-a-video
+                #if config.enable_enhance_a_video:
+                #    enable_enhance()
+                #    set_enhance_weight(config.enhance_a_video_weight)
+                
+                # Prepare generation parameters for the video model (we omit params that are destined to Varnish, or things like the seed which is set externally)
+                generation_kwargs = {
+                   # general content settings
+                    "prompt": config.prompt,
+                    "negative_prompt": config.negative_prompt,
+        
+                    # video model settings (will be used during generation of the initial raw video clip)
+                    "width": config.width,
+                    "height": config.height,
+                    "num_frames": config.num_frames,
+                    "guidance_scale": config.guidance_scale,
+                    "num_inference_steps": config.num_inference_steps,
+ 
+                    # constants
+                    "output_type": "pt",
+                    "generator": generator,
+
+                    # Timestep for decoding VAE noise: the timestep at which generated video is decoded
+                    "decode_timestep": 0.05,
+                    
+                    # Noise level for decoding VAE noise: the interpolation factor between random noise and denoised latents at the decode timestep
+                    "decode_noise_scale": 0.025,
+                }
+                #logger.info(f"Video model generation settings:")
+                #pprint.pprint(generation_kwargs)
+
+                # Handle LoRA loading/unloading
+                if hasattr(self, '_current_lora_model'):
+                    if self._current_lora_model != (config.lora_model_name, config.lora_model_weight_file):
+                        # Unload previous LoRA if it exists and is different
+                        if hasattr(self.text_to_video, 'unload_lora_weights'):
+                            print("Unloading LoRA weights for the text_to_video pipeline..")
+                            self.text_to_video.unload_lora_weights()
+                        
+                        if support_image_prompt and hasattr(self.image_to_video, 'unload_lora_weights'):
+                            print("Unloading LoRA weights for the image_to_video pipeline..")
+                            self.image_to_video.unload_lora_weights()
+    
+                if config.lora_model_name:
+                    # Load new LoRA
+                    if hasattr(self.text_to_video, 'load_lora_weights'):
+                        print("Loading LoRA weights for the text_to_video pipeline..")
+                        self.text_to_video.load_lora_weights(
+                            config.lora_model_name,
+                            weight_name=config.lora_model_weight_file if config.lora_model_weight_file else None,
+                            token=hf_token,
+                        )
+                    if support_image_prompt and hasattr(self.image_to_video, 'load_lora_weights'):
+                        print("Loading LoRA weights for the image_to_video pipeline..")
+                        self.image_to_video.load_lora_weights(
+                            config.lora_model_name,
+                            weight_name=config.lora_model_weight_file if config.lora_model_weight_file else None,
+                            token=hf_token,
+                        )
+                    self._current_lora_model = (config.lora_model_name, config.lora_model_weight_file)
+    
+                # Modify prompt if trigger word is provided
+                if config.lora_model_trigger:
+                    generation_kwargs["prompt"] = f"{config.lora_model_trigger} {generation_kwargs['prompt']}"
+
+                #enhance_a_video_config = EnhanceAVideoConfig(
+                #    weight=config.enhance_a_video_weight if config.enable_enhance_a_video else 0.0,
+                #    # doing some testing
+                #    num_frames_callback=lambda: (8 + 1),
+                #    # num_frames_callback=lambda: config.num_frames,
+                #    # num_frames_callback=lambda: (config.num_frames - 1),
+                #    
+                #    _attention_type=1
+                #)
+                
+                # Check if image-to-video generation is requested
+                if support_image_prompt and input_image:
+                    processed_image = process_input_image(
+                        input_image,
+                        config.width,
+                        config.height,
+                        config.input_image_quality,
+                    )
+                    generation_kwargs["image"] = processed_image
+                    # disabled (we cannot install the hook multiple times, we would have to uninstall it first or find another way to dynamically enable it, eg. using the weight only)
+                    # apply_enhance_a_video(self.image_to_video.transformer, enhance_a_video_config)
+                    frames = self.image_to_video(**generation_kwargs).frames
+                else:
+                    # disabled (we cannot install the hook multiple times, we would have to uninstall it first or find another way to dynamically enable it, eg. using the weight only)
+                    # apply_enhance_a_video(self.text_to_video.transformer, enhance_a_video_config)
+                    frames = self.text_to_video(**generation_kwargs).frames
+
+                try:
+                    loop = asyncio.get_event_loop()
+                except RuntimeError:
+                    loop = asyncio.new_event_loop()
+                    asyncio.set_event_loop(loop)
+                
+                video_uri, metadata = loop.run_until_complete(self.process_frames(frames, config))
+
+                torch.cuda.empty_cache()
+                torch.cuda.reset_peak_memory_stats()
+                gc.collect()
+                
+                return {
+                    "video": video_uri,
+                    "content-type": "video/mp4",
+                    "metadata": metadata
+                }
+
+        except Exception as e:
+            message = f"Error generating video ({str(e)})\n{traceback.format_exc()}"
+            print(message)
+            raise RuntimeError(message)