removed pipeline from repo

text_diffusion_pipeline.py

@@ -1,442 +0,0 @@
-import torch
-import torch.nn.functional as F
-from typing import NamedTuple, Optional
-import os
-from diffusers import DDPMPipeline, UNet2DConditionModel, DDPMScheduler
-import json
-# Running the main at the end of this requires messing with this import
-from models.text_model import TransformerModel  
-import torch
-import torch.nn.functional as F
-from transformers import AutoTokenizer, AutoModel
-import util.common_settings as common_settings
-#import models.sentence_transformers_helper as st_helper
-import models.text_model as text_model
-#from models.general_training_helper import get_scene_from_embeddings
-            
-class PipelineOutput(NamedTuple):
-    images: torch.Tensor
-    
-
-
-# Create a custom pipeline for text-conditional generation
-class TextConditionalDDPMPipeline(DDPMPipeline):
-    def __init__(self, unet, scheduler, text_encoder=None, tokenizer=None, supports_pretrained_split=False, block_embeddings=None):
-        super().__init__(unet=unet, scheduler=scheduler)
-        self.text_encoder = text_encoder
-        self.tokenizer = tokenizer
-        self.supports_negative_prompt = hasattr(unet, 'negative_prompt_support') and unet.negative_prompt_support
-        self.supports_pretrained_split = supports_pretrained_split
-        self.block_embeddings = block_embeddings
-
-        if self.tokenizer is None and self.text_encoder is not None:
-            # Use the tokenizer from the text encoder if not provided
-            self.tokenizer = self.text_encoder.tokenizer
-        
-        # Register the text_encoder so that .to(), .cpu(), .cuda(), etc. work correctly
-        self.register_modules(
-            unet=unet,
-            scheduler=scheduler,
-            text_encoder=self.text_encoder,
-            tokenizer=self.tokenizer,
-        )
-    
-    # Override the to() method to ensure text_encoder is moved to the correct device
-    def to(self, device=None, dtype=None):
-        # Call the parent's to() method first
-        pipeline = super().to(device, dtype)
-        
-        # Additionally move the text_encoder to the device
-        if self.text_encoder is not None:
-            self.text_encoder.to(device)
-        
-        return pipeline
-
-    def save_pretrained(self, save_directory):
-        os.makedirs(save_directory, exist_ok=True)
-        super().save_pretrained(save_directory)  # saves UNet and scheduler
-
-        # Save block_embeddings tensor if it exists
-        if self.block_embeddings is not None:
-            torch.save(self.block_embeddings, os.path.join(save_directory, "block_embeddings.pt"))
-
-        # Save supports_negative_prompt and supports_pretrained_split flags
-        with open(os.path.join(save_directory, "pipeline_config.json"), "w") as f:
-            json.dump({
-                "supports_negative_prompt": self.supports_negative_prompt,
-                "supports_pretrained_split": self.supports_pretrained_split,
-                "text_encoder_type": type(self.text_encoder).__name__   
-            }, f)
-
-
-        #Text encoder/tokenizer saving is different depending on if we're using a larger pretrained model
-        if isinstance(self.text_encoder, TransformerModel):
-            # Save custom text encoder
-            if self.text_encoder is not None:
-                self.text_encoder.save_pretrained(os.path.join(save_directory, "text_encoder"))
-        else:
-            #Save pretrained tokenizer by name, so we can load from huggingface instead of saving a giant local model
-            text_encoder_info = {
-                "text_encoder_name": self.text_encoder.config.name_or_path,
-                "tokenizer_name": self.tokenizer.name_or_path,
-            }
-
-            text_encoder_directory = os.path.join(save_directory, "text_encoder")
-            os.makedirs(text_encoder_directory, exist_ok=True)
-
-            with open(os.path.join(text_encoder_directory, "loading_info.json"), "w") as f:
-                json.dump(text_encoder_info, f)
-            
-
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_path, **kwargs):
-        #from diffusers.utils import load_config, load_state_dict
-        # Load model_index.json
-        #model_index = load_config(pretrained_model_path)
-
-        # Load components manually
-        unet_path = os.path.join(pretrained_model_path, "unet")
-        unet = UNet2DConditionModel.from_pretrained(unet_path)
-
-        scheduler_path = os.path.join(pretrained_model_path, "scheduler")
-        # Have heard that DDIMScheduler might be faster for inference, though not necessarily better
-        scheduler = DDPMScheduler.from_pretrained(scheduler_path)
-
-        tokenizer = None
-        text_encoder_path = os.path.join(pretrained_model_path, "text_encoder")
-        
-        if os.path.exists(text_encoder_path):
-            #Test for the new saving system, where we save a simple config file
-            if os.path.exists(os.path.join(text_encoder_path, "loading_info.json")):
-                with open(os.path.join(text_encoder_path, "loading_info.json"), "r") as f:
-                    encoder_config = json.load(f)
-
-                text_encoder = AutoModel.from_pretrained(encoder_config['text_encoder_name'], trust_remote_code=True)
-                tokenizer = AutoTokenizer.from_pretrained(encoder_config['tokenizer_name'])
-            
-            #Legacy loading system, loads models directly if the whole thing is saved in the directory
-            else:
-                try:
-                    text_encoder = AutoModel.from_pretrained(text_encoder_path, local_files_only=True, trust_remote_code=True)
-                    tokenizer = AutoTokenizer.from_pretrained(text_encoder_path, local_files_only=True)
-                except (ValueError, KeyError):
-                    text_encoder = TransformerModel.from_pretrained(text_encoder_path)
-                    tokenizer = text_encoder.tokenizer
-        else:
-            text_encoder = None
-
-        # Instantiate your pipeline
-        pipeline = cls(
-            unet=unet,
-            scheduler=scheduler,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            **kwargs,
-        )
-
-        #Loads block embeddings if present
-        block_embeds_path = os.path.join(pretrained_model_path, "block_embeddings.pt")
-        if os.path.exists(block_embeds_path):
-            pipeline.block_embeddings = torch.load(block_embeds_path, map_location="cpu")
-        else:
-            pipeline.block_embeddings = None
-        
-
-        # Load supports_negative_prompt flag if present
-        config_path = os.path.join(pretrained_model_path, "pipeline_config.json")
-        if os.path.exists(config_path):
-            with open(config_path, "r") as f:
-                config = json.load(f)
-            pipeline.supports_negative_prompt = config.get("supports_negative_prompt", False)
-            pipeline.supports_pretrained_split = config.get("supports_pretrained_split", False)
-        return pipeline
-
-    # --- Handle batching for captions ---
-    def _prepare_text_batch(self, text: Optional[str | list[str]], batch_size: int, name: str) -> Optional[list[str]]:
-        if text is None:
-            return None
-        if isinstance(text, str):
-            return [text] * batch_size
-        if isinstance(text, list):
-            if len(text) == 1:
-                return text * batch_size
-            if len(text) != batch_size:
-                raise ValueError(f"{name} list length {len(text)} does not match batch_size {batch_size}")
-            return text
-        raise ValueError(f"{name} must be a string or list of strings")
-
-    def _prepare_initial_sample(self, 
-                                raw_latent_sample: Optional[torch.Tensor],
-                                input_scene: Optional[torch.Tensor],
-                                batch_size: int, height: int, width: int,
-                                generator: Optional[torch.Generator]) -> torch.Tensor:
-        """Prepare the initial sample for diffusion."""
- 
-        sample_shape = (batch_size, self.unet.config.in_channels, height, width)
-
-        if raw_latent_sample is not None:
-            if input_scene is not None:
-                raise ValueError("Cannot provide both raw_latent_sample and input_scene")
-            sample = raw_latent_sample.to(self.device)
-            if sample.shape[1] != sample_shape[1]:
-                raise ValueError(f"Wrong number of channels in raw_latent_sample: Expected {self.unet.config.in_channels} but got {sample.shape[1]}")
-            if sample.shape[0] == 1 and batch_size > 1:
-                sample = sample.repeat(batch_size, 1, 1, 1)
-            elif sample.shape[0] != batch_size:
-                raise ValueError(f"raw_latent_sample batch size {sample.shape[0]} does not match batch_size {batch_size}")
-        elif input_scene is not None:
-            # input_scene can be (H, W) or (batch_size, H, W)
-            scene_tensor = torch.tensor(input_scene, dtype=torch.long, device=self.device)
-            if scene_tensor.dim() == 2:
-                # (H, W) -> repeat for batch
-                scene_tensor = scene_tensor.unsqueeze(0).repeat(batch_size, 1, 1)
-            elif scene_tensor.shape[0] == 1 and batch_size > 1:
-                scene_tensor = scene_tensor.repeat(batch_size, 1, 1)
-            elif scene_tensor.shape[0] != batch_size:
-                raise ValueError(f"input_scene batch size {scene_tensor.shape[0]} does not match batch_size {batch_size}")
-            # One-hot encode: (batch, H, W, C)
-            one_hot = F.one_hot(scene_tensor, num_classes=self.unet.config.in_channels).float()
-            # (batch, H, W, C) -> (batch, C, H, W)
-            sample = one_hot.permute(0, 3, 1, 2)
-        else:
-            # Start from random noise
-            sample = torch.randn(sample_shape, generator=generator, device=self.device)
-
-        return sample
-
-    def __call__(
-        self,
-        caption: Optional[str | list[str]] = None,
-        negative_prompt: Optional[str | list[str]] = None,
-        generator: Optional[torch.Generator] = None,
-        num_inference_steps: int = common_settings.NUM_INFERENCE_STEPS,
-        guidance_scale: float = common_settings.GUIDANCE_SCALE,
-        height: int = common_settings.MARIO_HEIGHT,
-        width: int = common_settings.MARIO_WIDTH,
-        raw_latent_sample: Optional[torch.FloatTensor] = None,
-        input_scene: Optional[torch.Tensor] = None,
-        output_type: str = "tensor",
-        batch_size: int = 1,
-        show_progress_bar: bool = True,
-    ) -> PipelineOutput:
-        """Generate a batch of images based on text input using the diffusion model.
-
-        Args:
-            caption: Text description(s) of the desired output. Can be a string or list of strings.
-            negative_prompt: Text description(s) of what should not appear in the output. String or list.
-            generator: Random number generator for reproducibility.
-            num_inference_steps: Number of denoising steps (more = higher quality, slower).
-            guidance_scale: How strongly the generation follows the text prompt (higher = stronger).
-            height: Height of generated image in tiles.
-            width: Width of generated image in tiles.
-            raw_latent_sample: Optional starting point for diffusion instead of random noise.
-                Must have correct number of channels matching the UNet.
-            input_scene: Optional 2D or 3D int tensor where each value corresponds to a tile type.
-                Will be converted to one-hot encoding as starting point.
-            output_type: Currently only "tensor" is supported.
-            batch_size: Number of samples to generate in parallel.
-
-        Returns:
-            PipelineOutput containing the generated image tensor (batch_size, ...).
-        """
-
-        #       I would like to simplify the code to this, but the AI suggestion didn't work, and 
-        #       I did not feel good just pasting it all in. Will need to tackle it bit by bit.
-
-        #        if caption is not None and self.text_encoder is None:
-        #            raise ValueError("Text encoder required for conditional generation")
-    
-        #        self.unet.eval()
-        #        if self.text_encoder is not None:
-        #            self.text_encoder.to(self.device)
-        #            self.text_encoder.eval()
-        #
-        #        with torch.no_grad():
-        #            # Process text inputs
-        #            captions = self.prepare_text_batch(caption, batch_size, "caption")
-        #            negatives = self.prepare_text_batch(negative_prompt, batch_size, "negative_prompt")
-         
-        #            # Get embeddings
-        #            text_embeddings = self.prepare_embeddings(captions, negatives, batch_size)
-        #            
-        #            # Set up initial latent state
-        #            sample = self.prepare_initial_sample(raw_latent_sample, input_scene, 
-        #                                              batch_size, height, width, generator)
-           
-        #            # Run diffusion process
-        #            sample = self.run_diffusion(sample, text_embeddings, num_inference_steps,
-        #                                      guidance_scale, generator, show_progress_bar,
-        #                                      has_caption=caption is not None,
-        #                                      has_negative=negative_prompt is not None)
-         
-        #            # Format output
-        #            if output_type == "tensor":
-        #                sample = F.softmax(sample, dim=1)
-        #            else:
-        #                raise ValueError(f"Unsupported output type: {output_type}")
-
-        #        return PipelineOutput(images=sample)
-
-        # Validate text encoder if we need it
-        if caption is not None and self.text_encoder is None:
-            raise ValueError("Text encoder is required for conditional generation")
-
-        self.unet.eval()
-        if self.text_encoder is not None:
-            self.text_encoder.to(self.device)
-            self.text_encoder.eval()
-
-        with torch.no_grad():
-            captions = self._prepare_text_batch(caption, batch_size, "caption")
-            negatives = self._prepare_text_batch(negative_prompt, batch_size, "negative_prompt")
-
-            # --- Prepare text embeddings ---
-            if(isinstance(self.text_encoder, TransformerModel)):
-                text_embeddings = text_model.get_embeddings(batch_size=batch_size,
-                                                            tokenizer=self.text_encoder.tokenizer,
-                                                            text_encoder=self.text_encoder,
-                                                            captions=captions,
-                                                            neg_captions=negatives,
-                                                            device=self.device)
-            else: #Case for the pre-trained text encoder
-                if(self.supports_pretrained_split): #If we have a split flag incorporated
-                    text_embeddings = st_helper.get_embeddings_split(batch_size = batch_size,
-                                                            tokenizer=self.tokenizer,
-                                                            model=self.text_encoder,
-                                                            captions=captions,
-                                                            neg_captions=negatives,
-                                                            device=self.device)
-                else:
-                    text_embeddings = st_helper.get_embeddings(batch_size = batch_size,
-                                                                tokenizer=self.tokenizer,
-                                                                model=self.text_encoder,
-                                                                captions=captions,
-                                                                neg_captions=negatives,
-                                                                device=self.device)
-
-                            
-            # --- Set up initial latent state ---
-            sample = self._prepare_initial_sample(raw_latent_sample, input_scene, 
-                                                 batch_size, height, width, generator)
-
-            # --- Set up diffusion process ---
-            self.scheduler.set_timesteps(num_inference_steps)
-
-            # Denoising loop
-            iterator = self.progress_bar(self.scheduler.timesteps) if show_progress_bar else self.scheduler.timesteps
-            for t in iterator:
-                # Handle conditional generation
-                if captions is not None:
-                    if negatives is not None:
-                        # Three copies for negative prompt guidance
-                        model_input = torch.cat([sample, sample, sample], dim=0)
-                    else:
-                        # Two copies for standard classifier-free guidance
-                        model_input = torch.cat([sample, sample], dim=0)
-                else:
-                    model_input = sample
-
-                # Predict noise residual
-                model_kwargs = {"encoder_hidden_states": text_embeddings}
-                noise_pred = self.unet(model_input, t, **model_kwargs).sample
-
-                # Apply guidance
-                if captions is not None:
-                    if negatives is not None:
-                        # Split predictions for negative, unconditional, and text-conditional
-                        noise_pred_neg, noise_pred_uncond, noise_pred_text = noise_pred.chunk(3)
-                        noise_pred_guided = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-                        noise_pred = noise_pred_guided - guidance_scale * (noise_pred_neg - noise_pred_uncond)
-                    else:
-                        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # Compute previous sample: x_{t-1} = scheduler(x_t, noise_pred)
-                sample = self.scheduler.step(noise_pred, t, sample, generator=generator).prev_sample
-
-            # Convert to output format
-            if output_type == "tensor":
-                if self.block_embeddings is not None:
-                    sample = get_scene_from_embeddings(sample, self.block_embeddings)
-                else:
-                    # Apply softmax to get probabilities for each tile type
-                    sample = F.softmax(sample, dim=1)
-                    sample = sample.detach().cpu() 
-            else:
-                raise ValueError(f"Unsupported output type: {output_type}")
-
-        return PipelineOutput(images=sample)
-
-    def print_unet_architecture(self):
-        """Prints the architecture of the UNet model."""
-        print(self.unet)
-
-    def print_text_encoder_architecture(self):
-        """Prints the architecture of the text encoder model, if it exists."""
-        if self.text_encoder is not None:
-            print(self.text_encoder)
-        else:
-            print("No text encoder is set.")
-
-    def save_unet_architecture_pdf(self, height, width, filename="unet_architecture", batch_size=1, device=None):
-        """
-        Have to separately install torchview for this to work
-
-        Saves a visualization of the UNet architecture as a PDF using torchview.
-        Args:
-            height: Height of the dummy input.
-            width: Width of the dummy input.
-            filename: Output PDF filename.
-            batch_size: Batch size for dummy input.
-            device: Device to run the dummy input on (defaults to pipeline device).
-        """
-        from torchview import draw_graph
-        import graphviz
-        
-        if device is None:
-            device = self.device if hasattr(self, 'device') else 'cpu'
-        in_channels = self.unet.config.in_channels if hasattr(self.unet, 'config') else 1
-        sample_shape = tuple([batch_size, in_channels, height, width])
-
-        dummy_x = torch.randn(size=sample_shape, device=device)
-        dummy_t = torch.tensor([0] * batch_size, dtype=torch.long, device=device)
-
-        # Prepare dummy text embedding (match what your UNet expects)
-        if hasattr(self.unet, 'config') and hasattr(self.unet.config, 'cross_attention_dim'):
-            cross_attention_dim = self.unet.config.cross_attention_dim
-        else:
-            cross_attention_dim = 128  # fallback
-        encoder_hidden_states = torch.randn(batch_size, 1, cross_attention_dim, device=device)
-
-        self.unet.eval()
-        inputs = (dummy_x, dummy_t, encoder_hidden_states)
-        #self.unet.down_blocks = self.unet.down_blocks[:2]
-
-        graph = draw_graph(
-            model=self.unet,
-            input_data=inputs,
-            expand_nested=False,
-            #enable_output_shape=True,   
-            #roll_out="nested",
-            depth=1
-        )
-        #graph.visual_graph.engine = "neato"
-        graph.visual_graph.attr(#rankdir="LR",
-                                nodesep="0.1",      # decrease space between nodes in the same rank (default ~0.25)
-                                ranksep="0.2",       # decrease space between ranks (default ~0.5)
-                                concentrate="true"  # merge edges between nodes in the same rank
-                            )
-        graph.visual_graph.node_attr.update(
-            shape="rectangle",
-            width="1.5",   # narrow width
-            height="0.5"  # taller height to make vertical rectangles
-            #fixedsize="true"
-        )
-        
-        graph.visual_graph.render(filename, format='pdf', cleanup=False)  # Cleanup removes intermediate files
-        graph.visual_graph.save('unet_architecture.dot')
-
-        # Save the graph to a PDF file
-        print(f"UNet architecture saved to {filename}")
-