models dir back

models/latent_diffusion_pipeline.py

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+from diffusers import DDPMPipeline
+import torch
+import torch.nn.functional as F
+from typing import Optional, Union, List, Tuple
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.ddpm.pipeline_ddpm import ImagePipelineOutput
+import common_settings as common_settings
+import os
+import json
+from general_training_helper import get_scene_from_embeddings
+
+class UnconditionalDDPMPipeline(DDPMPipeline):
+    def __init__(self, unet, scheduler, block_embeddings=None):
+        super().__init__(unet, scheduler)
+
+        self.block_embeddings = block_embeddings
+    
+
+    def save_pretrained(self, save_directory):
+        os.makedirs(save_directory, exist_ok=True)
+        super().save_pretrained(save_directory)
+        # 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"))
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_path, **kwargs):
+        pipeline = super().from_pretrained(pretrained_model_path, **kwargs)
+        # Load block_embeddings tensor if it exists
+        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
+        return pipeline
+    
+
+
+    def give_sprite_scaling_factors(self, sprite_scaling_factors):
+        """
+        Set the sprite scaling factors for the pipeline.
+        This is used to apply per-sprite temperature scaling during inference.
+        """
+        self.sprite_scaling_factors = sprite_scaling_factors
+
+    def __call__(
+        self,
+        batch_size: int = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        num_inference_steps: int = common_settings.NUM_INFERENCE_STEPS,
+        output_type: Optional[str] = "tensor",
+        return_dict: bool = True,
+        height: int = common_settings.MARIO_HEIGHT, width: int = common_settings.MARIO_WIDTH, 
+        latents: Optional[torch.FloatTensor] = None,
+        show_progress_bar=True,
+    ) -> Union[ImagePipelineOutput, Tuple]:
+
+        self.unet.eval()
+        with torch.no_grad():
+
+            if latents is not None:
+                image = latents.to(self.device)
+            else:
+                image_shape = (
+                    batch_size,
+                    self.unet.config.in_channels,
+                    height,
+                    width
+                )
+
+                image = torch.randn(image_shape, generator=generator, device=self.device)
+
+            self.scheduler.set_timesteps(num_inference_steps)
+
+            iterator = self.progress_bar(self.scheduler.timesteps) if show_progress_bar else self.scheduler.timesteps
+            for t in iterator:
+                #print(image.shape)
+                model_output = self.unet(image, t).sample
+                image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample
+
+            # Apply per-sprite temperature scaling if enabled
+            if hasattr(self,"sprite_scaling_factors") and self.sprite_scaling_factors is not None:
+                image = image / self.sprite_scaling_factors.view(1, -1, 1, 1)
+
+            
+            if self.block_embeddings is not None:
+                image = get_scene_from_embeddings(image, self.block_embeddings)
+            else:
+                image = F.softmax(image, dim=1)
+                image = image.detach().cpu() 
+
+            if not return_dict:
+                return (image,)
+
+            return ImagePipelineOutput(images=image)
+
+    def print_unet_architecture(self):
+        """Prints the architecture of the UNet model."""
+        print(self.unet)

models/pipeline_loader.py

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+from models.text_diffusion_pipeline import TextConditionalDDPMPipeline
+from models.latent_diffusion_pipeline import UnconditionalDDPMPipeline
+import os
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+
+
+def get_pipeline(model_path):
+    # If model_path is a local directory, use the original logic
+    if os.path.isdir(model_path):
+        #Diffusion models
+        if os.path.exists(os.path.join(model_path, "unet")):
+            if os.path.exists(os.path.join(model_path, "text_encoder")):
+                #If it has a text encoder and a unet, it's text conditional diffusion
+                pipe = TextConditionalDDPMPipeline.from_pretrained(model_path)
+            else:
+                #If it has no text encoder, use the unconditional diffusion model
+                pipe = UnconditionalDDPMPipeline.from_pretrained(model_path)
+    else:
+        # Assume it's a Hugging Face Hub model ID
+        # Try to load config to determine if it's text-conditional
+        try:
+            config, _ = DiffusionPipeline.load_config(model_path)
+            has_text_encoder = "text_encoder" in config
+        except Exception:
+            print(f"Warning: Could not load config: {e}")
+            has_text_encoder = False
+        if has_text_encoder:
+            # Use the local pipeline file for custom_pipeline
+            pipe = DiffusionPipeline.from_pretrained(
+                model_path,
+                custom_pipeline="models.text_diffusion_pipeline.TextConditionalDDPMPipeline",
+                trust_remote_code=True,
+            )
+        else:
+            # Fallback: try unconditional 
+            pipe = DiffusionPipeline.from_pretrained(
+                model_path,
+                custom_pipeline="models.latent_diffusion_pipeline.UnconditionalDDPMPipeline",
+                trust_remote_code=True,
+            )
+
+    return pipe

models/text_diffusion_pipeline.py

@@ -0,0 +1,442 @@
+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 text_encoder.text_model import TransformerModel  
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel
+import common_settings as common_settings
+import sentence_transformers_helper as st_helper
+import text_encoder.text_model as text_model
+from 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}")
+