From 68f33b8cdf4fd849f81251c7d6978ff15fa2cd0c Mon Sep 17 00:00:00 2001
From: sa_ddam213 <sa_ddam213@live.com>
Date: Sat, 4 Jul 2026 13:02:26 +1200
Subject: [PATCH] Pipeline template

---
 .../Pipelines/Cosmos2Pipeline.py              | 445 +++++++++
 .../Templates/Cosmos2_T2I/model_index.json    |  28 +
 .../scheduler/scheduler_config.json           |  22 +
 .../Cosmos2_T2I/text_encoder/config.json      |  60 ++
 .../tokenizer/tokenizer_config.json           | 939 ++++++++++++++++++
 .../Cosmos2_T2I/transformer/config.json       |  29 +
 .../Templates/Cosmos2_T2I/vae/config.json     |  57 ++
 TensorStack.Python/TensorStack.Python.csproj  |   6 +
 8 files changed, 1586 insertions(+)
 create mode 100644 TensorStack.Python/Pipelines/Cosmos2Pipeline.py
 create mode 100644 TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/model_index.json
 create mode 100644 TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/scheduler/scheduler_config.json
 create mode 100644 TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/text_encoder/config.json
 create mode 100644 TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/tokenizer/tokenizer_config.json
 create mode 100644 TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/transformer/config.json
 create mode 100644 TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/vae/config.json

diff --git a/TensorStack.Python/Pipelines/Cosmos2Pipeline.py b/TensorStack.Python/Pipelines/Cosmos2Pipeline.py
new file mode 100644
index 0000000..aca4874
--- /dev/null
+++ b/TensorStack.Python/Pipelines/Cosmos2Pipeline.py
@@ -0,0 +1,445 @@
+import tensorstack.utils as Utils
+import tensorstack.data_objects as DataObjects
+import tensorstack.quantization as Quantization
+from tensorstack.enums import ProcessType, QuantTarget
+Utils.redirect_output()
+Utils.create_services()
+
+import torch
+import numpy as np
+from pathlib import Path
+from threading import Event
+from functools import partial
+from collections.abc import Buffer
+from typing import Dict, Sequence, List, Tuple, Optional, Any
+from transformers import T5Tokenizer, T5EncoderModel
+from diffusers import (
+    AutoencoderKLWan,
+    CosmosTransformer3DModel,
+    Cosmos2TextToImagePipeline
+)
+
+# Globals
+_config = None
+_model_config = None
+_pipeline = None
+_processType = None
+_execution_device = None
+_device_map = None
+_pipeline_device_map = None
+_control_net_name = None
+_control_net_cache = None
+_generator = None
+_isMemoryOffload = False
+_prompt_cache_key = None
+_prompt_cache_value = None
+_cancel_event = Event()
+_stopwatch = None
+_pipelineMap = {
+    ProcessType.TextToImage: Cosmos2TextToImagePipeline
+}
+
+
+#------------------------------------------------
+# Load Pipeline
+#------------------------------------------------
+def load(config_args: Dict[str, Any]) -> bool:
+    global _config, _pipeline, _generator, _processType, _execution_device, _isMemoryOffload
+
+    # Config
+    _config = DataObjects.PipelineConfig(**config_args)
+    _execution_device = Utils.get_execution_device(_config)
+    _generator = torch.Generator(device=_execution_device)
+    _processType = _config.process_type
+
+    # Initialize Pipeline
+    _pipeline = initialize(_config)
+
+    # Load Lora
+    Utils.load_lora_weights(_pipeline, _config)
+
+    # Memory
+    _isMemoryOffload = Utils.configure_pipeline_memory(_pipeline, _execution_device, _config)
+    Utils.trim_memory(_isMemoryOffload)
+    return True
+
+
+#------------------------------------------------
+# Reload Pipeline - ProcessType, LoraAdapters and ControlNet are the only options that can be modified
+#------------------------------------------------
+def reload(config_args: Dict[str, Any]) -> bool:
+    global _config, _pipeline, _processType
+
+    # Config
+    _config = DataObjects.PipelineConfig(**config_args)
+    _processType = _config.process_type
+
+    # Rebuild Pipeline
+    Utils.unload_lora_weights(_pipeline)
+    _pipeline = create_pipeline(_config)
+
+    # Load Lora
+    Utils.load_lora_weights(_pipeline, _config)
+
+    # Memory
+    Utils.configure_pipeline_memory(_pipeline, _execution_device, _config)
+    Utils.trim_memory(_isMemoryOffload)
+    return True
+
+
+#------------------------------------------------
+# Switch Pipeline - ProcessType
+#------------------------------------------------
+def switch(process_type: ProcessType) -> bool:
+    global _pipeline, _processType
+
+    # Switch Pipeline
+    current = _processType
+    _processType = process_type
+    _pipeline = create_pipeline(_config)
+
+    print(f"[Generate] Switched pipeline: {current} => {process_type}")
+    return True
+
+
+#------------------------------------------------
+# Cancel Generation
+#------------------------------------------------
+def generateCancel() -> None:
+    _cancel_event.set()
+
+
+#------------------------------------------------
+# Unload Pipline
+#------------------------------------------------
+def unload() -> bool:
+    global _pipeline, _prompt_cache_key, _prompt_cache_value
+    _pipeline = None
+    _prompt_cache_key = None
+    _prompt_cache_value = None
+    Utils.trim_memory(_isMemoryOffload)
+    return True
+
+
+#------------------------------------------------
+# Get the notifications
+#------------------------------------------------
+def getNotifications() -> list[(str, Buffer)]:
+    return Utils.notification_get()
+
+
+#------------------------------------------------
+# Get the log entires
+#------------------------------------------------
+def getLogs() -> list[str]:
+    return Utils.get_output()
+
+
+#------------------------------------------------
+# Initialize Pipeline
+#------------------------------------------------
+def initialize(config: DataObjects.PipelineConfig):
+    global _model_config, _device_map, _pipeline_device_map
+
+    _device_map = Utils.get_device_map(config, _execution_device)
+    _pipeline_device_map = Utils.get_pipeline_device_map(config, _execution_device)
+    _model_config = Utils.get_model_config(__file__, config)
+    return create_pipeline(config)
+
+
+#------------------------------------------------
+# Load T5Tokenizer
+#------------------------------------------------
+def load_tokenizer(config: DataObjects.PipelineConfig, pipeline_kwargs: Dict[str, str]):
+    if _pipeline and _pipeline.tokenizer:
+        print(f"[Load] Loading Cached Tokenizer")
+        return _pipeline.tokenizer
+
+    tokenizer_path: Path = _model_config["tokenizer"]
+    tokenizer_config: Path = _model_config["tokenizer_config"]
+
+    # 1. Load from pretrained folder
+    print(f"[Load] Loading Pretrained Tokenizer")
+    tokenizer = T5Tokenizer.from_pretrained(
+        tokenizer_path,
+        config=tokenizer_config,
+        dtype=config.data_type,
+        **pipeline_kwargs
+    )
+    return tokenizer
+
+
+#------------------------------------------------
+# Load T5EncoderModel
+#------------------------------------------------
+def load_text_encoder(config: DataObjects.PipelineConfig, pipeline_kwargs: Dict[str, str]):
+    if _pipeline and _pipeline.text_encoder:
+        print(f"[Load] Loading Cached TextEncoder")
+        return _pipeline.text_encoder
+
+    text_encoder_path: Path = _model_config["text_encoder"]
+    text_encoder_config: Path = _model_config["text_encoder_config"]
+
+    # 1. Load from pretrained folder
+    print(f"[Load] Loading Pretrained TextEncoder")
+    text_encoder = T5EncoderModel.from_pretrained(
+        text_encoder_path,
+        config=text_encoder_config,
+        dtype=config.data_type,
+        device_map=_device_map if _device_map != "meta" else None,
+        quantization_config=Quantization.auto_pretrained_config(config, QuantTarget.TEXT_ENCODER),
+        **pipeline_kwargs
+    )
+    Utils.trim_memory(True)
+    return text_encoder
+
+
+#------------------------------------------------
+# Load CosmosTransformer3DModel
+#------------------------------------------------
+def load_transformer(config: DataObjects.PipelineConfig, pipeline_kwargs: Dict[str, str]):
+    if _pipeline and _pipeline.transformer:
+        print(f"[Load] Loading Cached Transformer")
+        return _pipeline.transformer
+
+    transformer_path: Path = _model_config["transformer"]
+    transformer_config: Path = _model_config["transformer_config"]
+
+    # 1. Load from single file
+    if transformer_path.is_file():
+        is_gguf = Utils.isGGUF(transformer_path)
+        print(f"[Load] Loading File Transformer")
+        transformer =  CosmosTransformer3DModel.from_single_file(
+            str(transformer_path),
+            config=str(transformer_config),
+            torch_dtype=config.data_type,
+            device_map=_device_map,
+            quantization_config=Quantization.auto_single_file_config(config, QuantTarget.TRANSFORMER, is_gguf),
+            **pipeline_kwargs
+        )
+        Quantization.quantize_model(config, transformer, is_gguf)
+        Utils.trim_memory(True)
+        return transformer
+
+    # 2. Load from pretrained folder
+    print(f"[Load] Loading Pretrained Transformer")
+    transformer =  CosmosTransformer3DModel.from_pretrained(
+        str(transformer_path),
+        torch_dtype=config.data_type,
+        device_map=_device_map,
+        quantization_config=Quantization.auto_pretrained_config(config, QuantTarget.TRANSFORMER),
+        **pipeline_kwargs
+    )
+    Utils.trim_memory(True)
+    return transformer
+
+
+#------------------------------------------------
+# Load AutoencoderKLWan
+#------------------------------------------------
+def load_vae(config: DataObjects.PipelineConfig, pipeline_kwargs: Dict[str, str]):
+    if _pipeline and _pipeline.vae:
+        print(f"[Load] Loading Cached Vae")
+        return _pipeline.vae
+
+    vae_path: Path = _model_config["vae"]
+    vae_config: Path = _model_config["vae_config"]
+    single_path: Path = _model_config["single_file"]
+    template_path: Path  = _model_config["template"]
+
+    # 1. Load from single file
+    if vae_path.is_file():
+        print(f"[Load] Loading SingleFile Vae")
+        auto_encoder =  AutoencoderKLWan.from_single_file(
+            str(vae_path),
+            config=str(vae_config),
+            torch_dtype=config.data_type,
+            device_map=_device_map,
+            **pipeline_kwargs
+        )
+        Utils.trim_memory(True)
+        return auto_encoder
+
+    # 2. Load component from single file
+    if single_path and single_path.is_file():
+        print(f"[Load] Loading Component Vae")
+        auto_encoder = Utils.from_component(Cosmos2TextToImagePipeline, "vae", single_path, template_path, _device_map, config.data_type)
+        if auto_encoder:
+            Utils.trim_memory(True)
+            return auto_encoder
+
+    # 3. Load from pretrained folder
+    print(f"[Load] Loading Pretrained Vae")
+    auto_encoder = AutoencoderKLWan.from_pretrained(
+        str(vae_path),
+        torch_dtype=config.data_type,
+        device_map=_device_map,
+        **pipeline_kwargs
+    )
+    Utils.trim_memory(True)
+    return auto_encoder
+
+
+#------------------------------------------------
+# Load ControlNetModel
+#------------------------------------------------
+def load_control_net(config: DataObjects.PipelineConfig, pipeline_kwargs: Dict[str, str]):
+    global _control_net_name, _control_net_cache
+
+    if _control_net_cache and _control_net_name == config.control_net.name:
+        print(f"[Load] Loading Cached ControlNet")
+        return _control_net_cache
+
+    if config.control_net.name is None:
+        _control_net_name = None
+        _control_net_cache = None
+        return None
+
+    # print(f"[Load] Loading Pretrained ControlNet")
+    # _control_net_name = config.control_net.name
+    # _control_net_cache = ControlNetModel.from_pretrained(
+    #     config.control_net.path,
+    #     torch_dtype=config.data_type,
+    #     device_map=_device_map,
+    #     **pipeline_kwargs
+    # )
+    return _control_net_cache
+
+
+#------------------------------------------------
+# Create a new pipeline
+#------------------------------------------------
+def create_pipeline(config: DataObjects.PipelineConfig):
+    template_path: Path = _model_config["template"]
+    pipeline_kwargs = {
+        "variant": config.variant,
+        "use_safetensors":True,
+        "low_cpu_mem_usage":True,
+        "local_files_only":True,
+    }
+
+    # Load Models
+    tokenizer = load_tokenizer(config, pipeline_kwargs)
+    text_encoder = load_text_encoder(config, pipeline_kwargs)
+    transformer = load_transformer(config, pipeline_kwargs)
+    vae = load_vae(config, pipeline_kwargs)
+    control_net = load_control_net(config, pipeline_kwargs)
+    if control_net is not None:
+        pipeline_kwargs.update({"controlnet": control_net})
+
+    # Build Pipeline
+    pipeline = _pipelineMap[_processType]
+    return pipeline.from_pretrained(
+        template_path,
+        tokenizer=tokenizer,
+        text_encoder=text_encoder,
+        transformer=transformer,
+        vae=vae,
+        torch_dtype=config.data_type,
+        device_map=_pipeline_device_map,
+        **pipeline_kwargs
+    )
+
+
+#------------------------------------------------
+# Generate Image/Video
+#------------------------------------------------
+def generate(
+        inference_args: Dict[str, Any],
+        input_tensors: Optional[List[Tuple[Sequence[float],Sequence[int]]]] = None,
+        control_tensors: Optional[List[Tuple[Sequence[float],Sequence[int]]]] = None,
+    ) -> Sequence[Buffer]:
+    global _prompt_cache_key, _prompt_cache_value, _stopwatch
+    _cancel_event.clear()
+    _pipeline._interrupt = False
+    _stopwatch = Utils.Stopwatch()
+    _stopwatch.start()
+    Utils.notification_push(key="Generate", subkey="Initialize")
+
+    # Input Images
+    images = Utils.prepare_images(input_tensors)
+    image_count = Utils.get_len(images)
+    control_images = Utils.prepare_images(control_tensors)
+    control_image_count = Utils.get_len(control_images)
+    print(f"[Generate] Input Received - Tensors: {image_count}, Control Tensors: {control_image_count}")
+
+    # Options
+    options = DataObjects.PipelineOptions(**inference_args)
+
+    # Scheduler
+    _pipeline.scheduler = Utils.create_scheduler(options.scheduler_options)
+
+    # AutoEncoder
+    Utils.configure_vae_memory(_pipeline, options.enable_vae_tiling, options.enable_vae_slicing)
+
+    # Lora Adapters
+    Utils.set_lora_weights(_pipeline, options)
+
+    # Notify
+    Utils.notification_push(key="Generate", subkey="TextEncoder", elapsedkey="Initialize", elapsed=_stopwatch.reset())
+
+    # Prompt Cache
+    prompt_cache_key = (options.prompt, options.negative_prompt, options.guidance_scale > 1)
+    if _prompt_cache_key != prompt_cache_key:
+        print(f"[Generate] Encoding prompt")
+        with torch.no_grad():
+            _prompt_cache_value = _pipeline.encode_prompt(
+                prompt=options.prompt,
+                negative_prompt=options.negative_prompt,
+                do_classifier_free_guidance=options.guidance_scale > 1,
+                device=_pipeline._execution_device,
+                max_sequence_length=512
+            )
+            _prompt_cache_key = prompt_cache_key
+
+    # Notify
+    Utils.notification_push(key="Generate", subkey="Transformer", elapsedkey="TextEncoder", elapsed=_stopwatch.reset())
+
+    # Pipeline Options
+    (prompt_embeds, negative_prompt_embeds) = _prompt_cache_value
+    pipeline_options = {
+        "prompt_embeds": prompt_embeds,
+        "negative_prompt_embeds": negative_prompt_embeds,
+        "height": options.height,
+        "width": options.width,
+        "generator": _generator.manual_seed(options.seed),
+        "guidance_scale": options.guidance_scale,
+        "num_inference_steps": options.steps,
+        "output_type": "np",
+        "callback_on_step_end": partial(_progress_callback, height=options.height, width=options.width),
+        "callback_on_step_end_tensor_inputs": ["latents"],
+    }
+
+    # Run Pipeline
+    output = _pipeline(**pipeline_options)[0]
+
+    # (Batch, Channel, Height, Width)
+    output = output.transpose(0, 3, 1, 2).astype(np.float32)
+
+    # Notify
+    Utils.notification_push(key="Generate", subkey="AutoEncoder", elapsedkey="Transformer", elapsed = _stopwatch.reset())
+    Utils.notification_push(key="Generate", subkey="Complete", elapsedkey="AutoEncoder", elapsed = _stopwatch.stop())
+
+    # Cleanup
+    Utils.trim_memory(_isMemoryOffload)
+    return [ np.ascontiguousarray(output) ]
+
+
+#------------------------------------------------
+# Diffusers pipeline callback to capture step artifacts
+#------------------------------------------------
+def _progress_callback(pipe, step: int, total_steps: int, info: Dict, height: int, width: int):
+    if _cancel_event.is_set():
+        pipe._interrupt = True
+        raise Exception("Operation Canceled")
+
+    def preview_latents(latents):
+        if latents is None:
+            return []
+        return latents.float().cpu()
+
+    steps = pipe._num_timesteps
+    elapsed = _stopwatch.reset()
+    step_latents = preview_latents(info.get("latents"))
+    Utils.notification_push(key="Generate", subkey="Step", elapsedkey="Step", value=step + 1, maximum=steps, elapsed=elapsed, tensor=step_latents)
+    return info
\ No newline at end of file
diff --git a/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/model_index.json b/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/model_index.json
new file mode 100644
index 0000000..40bd3b2
--- /dev/null
+++ b/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/model_index.json
@@ -0,0 +1,28 @@
+{
+  "_class_name": "Cosmos2TextToImagePipeline",
+  "_diffusers_version": "0.34.0.dev0",
+  "safety_checker": [
+    null,
+    null
+  ],
+  "scheduler": [
+    "diffusers",
+    "FlowMatchEulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "T5EncoderModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "T5TokenizerFast"
+  ],
+  "transformer": [
+    "diffusers",
+    "CosmosTransformer3DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKLWan"
+  ]
+}
diff --git a/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/scheduler/scheduler_config.json b/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/scheduler/scheduler_config.json
new file mode 100644
index 0000000..07252bf
--- /dev/null
+++ b/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/scheduler/scheduler_config.json
@@ -0,0 +1,22 @@
+{
+  "_class_name": "FlowMatchEulerDiscreteScheduler",
+  "_diffusers_version": "0.34.0.dev0",
+  "base_image_seq_len": 256,
+  "base_shift": 0.5,
+  "final_sigmas_type": "sigma_min",
+  "invert_sigmas": false,
+  "max_image_seq_len": 4096,
+  "max_shift": 1.15,
+  "num_train_timesteps": 1000,
+  "shift": 1.0,
+  "shift_terminal": null,
+  "sigma_data": 1.0,
+  "sigma_max": 80.0,
+  "sigma_min": 0.002,
+  "stochastic_sampling": false,
+  "time_shift_type": "exponential",
+  "use_beta_sigmas": false,
+  "use_dynamic_shifting": false,
+  "use_exponential_sigmas": false,
+  "use_karras_sigmas": true
+}
diff --git a/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/text_encoder/config.json b/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/text_encoder/config.json
new file mode 100644
index 0000000..6c85dc4
--- /dev/null
+++ b/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/text_encoder/config.json
@@ -0,0 +1,60 @@
+{
+  "architectures": [
+    "T5EncoderModel"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 65536,
+  "d_kv": 128,
+  "d_model": 1024,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 24,
+  "num_heads": 128,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.3",
+  "use_cache": true,
+  "vocab_size": 32128
+}
diff --git a/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/tokenizer/tokenizer_config.json b/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/tokenizer/tokenizer_config.json
new file mode 100644
index 0000000..71cc075
--- /dev/null
+++ b/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/tokenizer/tokenizer_config.json
@@ -0,0 +1,939 @@
+{
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<extra_id_99>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32001": {
+      "content": "<extra_id_98>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32002": {
+      "content": "<extra_id_97>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32003": {
+      "content": "<extra_id_96>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32004": {
+      "content": "<extra_id_95>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32005": {
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+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
+    "<extra_id_74>",
+    "<extra_id_75>",
+    "<extra_id_76>",
+    "<extra_id_77>",
+    "<extra_id_78>",
+    "<extra_id_79>",
+    "<extra_id_80>",
+    "<extra_id_81>",
+    "<extra_id_82>",
+    "<extra_id_83>",
+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
+    "<extra_id_87>",
+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "extra_ids": 100,
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}
diff --git a/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/transformer/config.json b/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/transformer/config.json
new file mode 100644
index 0000000..0cbfb6c
--- /dev/null
+++ b/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/transformer/config.json
@@ -0,0 +1,29 @@
+{
+  "_class_name": "CosmosTransformer3DModel",
+  "_diffusers_version": "0.34.0.dev0",
+  "adaln_lora_dim": 256,
+  "attention_head_dim": 128,
+  "concat_padding_mask": true,
+  "extra_pos_embed_type": null,
+  "in_channels": 16,
+  "max_size": [
+    128,
+    240,
+    240
+  ],
+  "mlp_ratio": 4.0,
+  "num_attention_heads": 16,
+  "num_layers": 28,
+  "out_channels": 16,
+  "patch_size": [
+    1,
+    2,
+    2
+  ],
+  "rope_scale": [
+    1.0,
+    4.0,
+    4.0
+  ],
+  "text_embed_dim": 1024
+}
diff --git a/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/vae/config.json b/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/vae/config.json
new file mode 100644
index 0000000..e95123c
--- /dev/null
+++ b/TensorStack.Python/Pipelines/Templates/Cosmos2_T2I/vae/config.json
@@ -0,0 +1,57 @@
+{
+  "_class_name": "AutoencoderKLWan",
+  "_diffusers_version": "0.34.0.dev0",
+  "_name_or_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+  "attn_scales": [],
+  "base_dim": 96,
+  "dim_mult": [
+    1,
+    2,
+    4,
+    4
+  ],
+  "dropout": 0.0,
+  "latents_mean": [
+    -0.7571,
+    -0.7089,
+    -0.9113,
+    0.1075,
+    -0.1745,
+    0.9653,
+    -0.1517,
+    1.5508,
+    0.4134,
+    -0.0715,
+    0.5517,
+    -0.3632,
+    -0.1922,
+    -0.9497,
+    0.2503,
+    -0.2921
+  ],
+  "latents_std": [
+    2.8184,
+    1.4541,
+    2.3275,
+    2.6558,
+    1.2196,
+    1.7708,
+    2.6052,
+    2.0743,
+    3.2687,
+    2.1526,
+    2.8652,
+    1.5579,
+    1.6382,
+    1.1253,
+    2.8251,
+    1.916
+  ],
+  "num_res_blocks": 2,
+  "temperal_downsample": [
+    false,
+    true,
+    true
+  ],
+  "z_dim": 16
+}
diff --git a/TensorStack.Python/TensorStack.Python.csproj b/TensorStack.Python/TensorStack.Python.csproj
index 5ab94d6..ccaba6c 100644
--- a/TensorStack.Python/TensorStack.Python.csproj
+++ b/TensorStack.Python/TensorStack.Python.csproj
@@ -46,6 +46,12 @@
 
   <!--Python Lib-->
   <ItemGroup>
+    <Content Include="Pipelines\Cosmos2Pipeline.py">
+      <PackagePath>contentFiles\any\any\Pipelines\</PackagePath>
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+      <PackageCopyToOutput>true</PackageCopyToOutput>
+      <Pack>true</Pack>
+    </Content>
     <Content Include="Python\lib\tensorstack\enums.py" Pack="true" PackagePath="contentFiles\any\any\Python\lib\tensorstack\" CopyToOutputDirectory="Always" PackageCopyToOutput="true" />
     <Content Include="Python\lib\tensorstack\export.py" Pack="true" PackagePath="contentFiles\any\any\Python\lib\tensorstack\" CopyToOutputDirectory="Always" PackageCopyToOutput="true" />
     <Content Include="Python\lib\tensorstack\utils.py" Pack="true" PackagePath="contentFiles\any\any\Python\lib\tensorstack\" CopyToOutputDirectory="Always" PackageCopyToOutput="true" />