diff --git a/README.md b/README.md index ed3571b3..65933580 100644 --- a/README.md +++ b/README.md @@ -25,6 +25,7 @@ - [Quickstart](#quickstart) - [Generator with Diffusers](#generator-with-diffusers) - [Generator with vLLM-Omni](#generator-with-vllm-omni) + - [Generator with SGLang](#generator-with-sglang) - [Reasoner with Transformers](#reasoner-with-transformers) - [Reasoner with vLLM](#reasoner-with-vllm) - [Troubleshooting](#troubleshooting) @@ -413,6 +414,169 @@ References: +#### Generator with SGLang + +
+Expand SGLang generator setup, endpoints, and request reference + +Use SGLang Diffusion for native Cosmos 3 visual generation behind OpenAI-compatible image and video APIs. Cosmos 3 also includes video-with-sound and action/policy models; this SGLang section focuses on the currently supported text-to-image, text-to-video, and image-to-video generator serving paths. + +Supported checkpoints: + +| Model | Status | Notes | +| --- | --- | --- | +| `nvidia/Cosmos3-Nano` | Supported | Text-to-image, text-to-video, image-to-video | +| `nvidia/Cosmos3-Super` | Supported | Use multiple GPUs for the 64B checkpoint | +| `nvidia/Cosmos3-Super-Text2Image` | Supported | Text-to-image specialized checkpoint | +| `nvidia/Cosmos3-Super-Image2Video` | Supported | Image-to-video specialized checkpoint | +| `nvidia/Cosmos3-Nano-Policy-DROID` | Supported | Action/policy checkpoint | + +Install SGLang from the main branch with diffusion extras: + +```shell +git clone --branch main https://github.com/sgl-project/sglang.git +cd sglang +python -m venv .venv +source .venv/bin/activate +python -m pip install --upgrade pip +pip install -e "python[diffusion]" +pip install "cosmos-guardrail==0.3.1" +``` + +> **Version note:** Cosmos 3 support in SGLang Diffusion currently requires the SGLang main branch. Switch to a stable SGLang release once Cosmos 3 support is included there. + +Start a Nano server: + +```shell +sglang serve --model-path nvidia/Cosmos3-Nano +``` + +For a video-specialized checkpoint, use `Cosmos3-Super-Image2Video` with multiple GPUs: + +```shell +sglang serve \ + --model-path nvidia/Cosmos3-Super-Image2Video \ + --num-gpus 4 +``` + +This is the performance-mode setup. If it runs out of memory, switch to SGLang Diffusion's memory preset: + +```shell +sglang serve \ + --model-path nvidia/Cosmos3-Super-Image2Video \ + --num-gpus 4 \ + --performance-mode memory +``` + +Vision endpoints: + +| Mode | Endpoint | Notes | +| --- | --- | --- | +| Text to image | `POST /v1/images/generations` | Returns base64 by default for Cosmos 3 | +| Text to video | `POST /v1/videos` | Creates an async job; poll `GET /v1/videos/{id}` and download `/content` | +| Image to video | `POST /v1/videos` | Upload the conditioning image with `input_reference` | +| Video to Video | `POST /v1/videos` | Upload the conditioning video with `video_reference` and choose which frames stay as clean conditioning | +| Video with sound | `POST /v1/videos` | Add `generate_sound=true` to produce a soundtrack alongside the video | + +Action modes use Cosmos 3 as a world model: they condition on an embodiment (`domain_name`) and exchange video and action sequences. Policy and inverse dynamics return a predicted action chunk, and read the action data from the completed result; forward dynamics returns only video. + +| Mode | `action_mode` | Input | Output | +| --- | --- | --- | --- | +| Policy | `policy` | Image + instruction | Video + predicted action chunk | +| Inverse dynamics | `inverse_dynamics` | Video + instruction | Video + predicted action chunk | +| Forward dynamics | `forward_dynamics` | Image + action chunk | Video | + +Pass embodiment settings through `extra_params`: `action_mode`, `domain_name` (for example `bridge_orig_lerobot`, `av`, or `camera_pose`), `raw_action_dim`, and optionally `action_view_point`. SGLang derives the action chunk length from `num_frames - 1`, so set `num_frames` to `action_chunk_size + 1`. +For forward dynamics, pass the action trajectory directly in `extra_params["action"]` as a JSON array of shape `[action_chunk_size, raw_action_dim]`. SGLang does not use action_path for HTTP requests, so no `--allowed-local-media-path` setup is needed for action files. + +Text-to-video example: + +```shell +# Submit an async video generation job and capture its ID. +job_id=$(curl -sS -X POST http://localhost:30000/v1/videos \ + --form-string "prompt=A small warehouse robot moves a blue box across a clean floor." \ + --form-string "negative_prompt=blurry, distorted, low quality" \ + --form-string "size=1280x720" \ + --form-string "num_frames=81" \ + --form-string "fps=24" \ + --form-string "num_inference_steps=35" \ + --form-string "guidance_scale=4.0" \ + --form-string "flow_shift=10.0" \ + --form-string "seed=42" \ + --form-string 'extra_params={"guardrails":true,"use_resolution_template":false,"use_duration_template":false}' \ + | jq -r .id) + +# Poll until the job completes. Cosmos 3 video generation can take several minutes. +status="" +until [ "$status" = "completed" ]; do + status=$(curl -sS "http://localhost:30000/v1/videos/${job_id}" | jq -r .status) + [ "$status" = "failed" ] && exit 1 + sleep 5 +done + +# Download the completed MP4. +curl -sS -L "http://localhost:30000/v1/videos/${job_id}/content" \ + -o cosmos3_t2v_output.mp4 +``` + +Text-to-image example: + +```shell +curl -sS -X POST http://localhost:30000/v1/images/generations \ + -H "Content-Type: application/json" \ + -d '{ + "prompt": "A warehouse robot folds a blue cloth on a clean workbench.", + "size": "1280x720", + "n": 1, + "num_inference_steps": 35, + "guidance_scale": 6.0, + "flow_shift": 10.0, + "seed": 0, + "extra_args": { + "use_resolution_template": false, + "guardrails": true + } + }' +``` + +Video-to-video-with-sound example: + +```shell +job_id=$(curl -sS --fail-with-body -X POST "http://localhost:30000/v1/videos" \ + -H "Accept: application/json" \ + --form-string 'prompt=A small warehouse robot moves a blue box across a clean floor.' \ + --form-string 'negative_prompt=blurry, distorted, low quality' \ + --form-string 'size=1280x720' \ + --form-string 'num_frames=61' \ + --form-string 'fps=24' \ + --form-string 'num_inference_steps=30' \ + --form-string 'guidance_scale=4.0' \ + --form-string 'flow_shift=10.0' \ + --form-string 'seed=1234' \ + --form-string 'generate_sound=true' \ + --form-string 'extra_params={"use_resolution_template":false,"use_duration_template":false,"guardrails":true}' \ + -F 'video_reference=@/path/to/video.mp4;type=video/mp4' \ + | jq -r .id) + +# Poll until the job completes. Cosmos 3 video generation can take several minutes. +status="" +until [ "$status" = "completed" ]; do + status=$(curl -sS "http://localhost:30000/v1/videos/${job_id}" | jq -r .status) + [ "$status" = "failed" ] && exit 1 + sleep 5 +done + +# Download the completed MP4. +curl -sS -L "http://localhost:30000/v1/videos/${job_id}/content" \ + -o cosmos3_v2vs_output.mp4 +``` + +SGLang accepts Cosmos 3 request options including `max_sequence_length`, `flow_shift`, `extra_params.guardrails`, `extra_params.use_resolution_template`, and `extra_params.use_duration_template`. Guardrails are enabled by default when `cosmos-guardrail` is installed; set `SGLANG_DISABLE_COSMOS3_GUARDRAILS=1` before starting the server to skip loading the guardrail models. + +For complete serving instructions and request examples, see the [Cosmos3 SGLang cookbook](https://docs.sglang.io/cookbook/diffusion/Cosmos/Cosmos3). + +
+ #### Reasoner with Transformers Coming soon! @@ -497,10 +661,13 @@ We are building examples that show Cosmos 3 capabilities end to end, including w | Generator (audiovisual) with Diffusers | Generator | Text-to-image, plus text-to-video and image-to-video each with or without synchronized sound, via `Cosmos3OmniPipeline`. | [Notebook](cookbooks/cosmos3/generator/audiovisual/run_with_diffusers.ipynb) | [![Render with nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/nvidia/cosmos/blob/main/cookbooks/cosmos3/generator/audiovisual/run_with_diffusers.ipynb) | | Generator (audiovisual) with Cosmos Framework | Generator | Text-to-image, plus text-to-video and image-to-video each with sound on or off, through the `cosmos_framework.scripts.inference` entrypoint. | [Notebook](cookbooks/cosmos3/generator/audiovisual/run_with_cosmos_framework.ipynb) | [![Render with nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/nvidia/cosmos/blob/main/cookbooks/cosmos3/generator/audiovisual/run_with_cosmos_framework.ipynb) | | Generator (audiovisual) with vLLM-Omni | Generator | Text-to-image, plus text-to-video and image-to-video each with sound on or off, against an OpenAI-compatible vLLM-Omni server. | [Notebook](cookbooks/cosmos3/generator/audiovisual/run_with_vllm_omni.ipynb) | [![Render with nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/nvidia/cosmos/blob/main/cookbooks/cosmos3/generator/audiovisual/run_with_vllm_omni.ipynb) | +| Generator (audiovisual) with SGLang | Generator | Text-to-image, plus text-to-video and image-to-video each with sound on or off, against an OpenAI-compatible SGLang server. | [Notebook](cookbooks/cosmos3/generator/audiovisual/run_with_sglang.ipynb) | [![Render with nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/nvidia/cosmos/blob/main/cookbooks/cosmos3/generator/audiovisual/run_with_sglang.ipynb) | | Forward dynamics with Cosmos Framework | Generator | Forward dynamics: action-conditioned future-observation prediction for AV, DROID, and UMI, through the `cosmos_framework.scripts.inference` entrypoint. | [Notebook](cookbooks/cosmos3/generator/action/run_fd_with_cosmos_framework.ipynb) | [![Render with nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/nvidia/cosmos/blob/main/cookbooks/cosmos3/generator/action/run_fd_with_cosmos_framework.ipynb) | | Forward dynamics with vLLM-Omni | Generator | Forward dynamics: action-conditioned future-observation prediction for AV, DROID, and UMI, against an OpenAI-compatible vLLM-Omni server. | [Notebook](cookbooks/cosmos3/generator/action/run_fd_with_vllm.ipynb) | [![Render with nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/nvidia/cosmos/blob/main/cookbooks/cosmos3/generator/action/run_fd_with_vllm.ipynb) | +| Forward dynamics with SGLang | Generator | Forward dynamics: action-conditioned future-observation prediction for AV, DROID, and UMI, against an OpenAI-compatible SGLang server. | [Notebook](cookbooks/cosmos3/generator/action/run_fd_with_sglang.ipynb) | [![Render with nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/nvidia/cosmos/blob/main/cookbooks/cosmos3/generator/action/run_fd_with_sglang.ipynb) | | Inverse dynamics with Cosmos Framework | Generator | Inverse dynamics: ego-motion trajectory prediction from input AV video, through the `cosmos_framework.scripts.inference` entrypoint. | [Notebook](cookbooks/cosmos3/generator/action/run_id_with_cosmos_framework.ipynb) | [![Render with nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/nvidia/cosmos/blob/main/cookbooks/cosmos3/generator/action/run_id_with_cosmos_framework.ipynb) | | Inverse dynamics with vLLM-Omni | Generator | Inverse dynamics: ego-motion trajectory prediction from input AV video, against an OpenAI-compatible vLLM-Omni server. | [Notebook](cookbooks/cosmos3/generator/action/run_id_with_vllm.ipynb) | [![Render with nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/nvidia/cosmos/blob/main/cookbooks/cosmos3/generator/action/run_id_with_vllm.ipynb) | +| Inverse dynamics with SGLang | Generator | Inverse dynamics: ego-motion trajectory prediction from input AV video, against an OpenAI-compatible SGLang server. | [Notebook](cookbooks/cosmos3/generator/action/run_id_with_sglang.ipynb) | [![Render with nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/nvidia/cosmos/blob/main/cookbooks/cosmos3/generator/action/run_id_with_sglang.ipynb) | | Reasoner with Cosmos Framework | Reasoner | Text and image reasoning: detailed captioning, robot task planning, 2D grounding, describe-anything, and action-trajectory prompts, through the `cosmos_framework.scripts.inference` entrypoint. | [Notebook](cookbooks/cosmos3/reasoner/run_with_cosmos_framework.ipynb) | [![Render with nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/nvidia/cosmos/blob/main/cookbooks/cosmos3/reasoner/run_with_cosmos_framework.ipynb) | | Reasoner with vLLM | Reasoner | Image and video reasoning: captioning, temporal localization, embodied reasoning, common-sense reasoning, 2D grounding, describe-anything, action CoT, driving scenes, physical-plausibility, and situation understanding, against an OpenAI-compatible vLLM server (Cosmos3-Super on 4 GPUs by default; switch to Nano per the cookbook README). | [Notebook](cookbooks/cosmos3/reasoner/run_with_vllm.ipynb) | [![Render with nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/nvidia/cosmos/blob/main/cookbooks/cosmos3/reasoner/run_with_vllm.ipynb) | diff --git a/cookbooks/cosmos3/generator/action/run_fd_with_sglang.ipynb b/cookbooks/cosmos3/generator/action/run_fd_with_sglang.ipynb new file mode 100644 index 00000000..3c8ca66e --- /dev/null +++ b/cookbooks/cosmos3/generator/action/run_fd_with_sglang.ipynb @@ -0,0 +1,1317 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "license-header", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "fdvl-title", + "metadata": {}, + "source": [ + "# Cosmos3 Nano Action: Forward Dynamics with SGLang\n", + "\n", + "This notebook runs Cosmos3 Nano **action forward-dynamics** inference through the SGlang OpenAI-compatible video API:\n", + "\n", + "```text\n", + "POST /v1/videos\n", + "```\n", + "\n", + "Forward dynamics predicts future visual observations from an initial image and an action trajectory. This notebook contains separate AV and robotics sections that each build their own input spec, run inference, and visualize generated videos.\n", + "\n", + "Start the SGLang server:\n", + "\n", + "```bash\n", + "docker rm -f cosmos3-sglang-notebook 2>/dev/null || true\n", + "\n", + "docker run -d --name cosmos3-sglang-notebook \\\n", + " --runtime nvidia --gpus '\"device=0\"' \\\n", + " -e CUDA_DEVICE_ORDER=PCI_BUS_ID \\\n", + " -v \"~/.cache/huggingface:/root/.cache/huggingface\" \\\n", + " -v \"$PWD:/workspace\" \\\n", + " -p 30000:30000 --ipc=host \\\n", + " lmsysorg/sglang:dev \\\n", + " sglang serve \\\n", + " --model-path nvidia/Cosmos3-Nano\n", + "\n", + "# Wait until this returns model metadata before running the inference cell.\n", + "curl http://localhost:30000/v1/models\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "fdvl-vars-md", + "metadata": {}, + "source": [ + "## Configure Notebook Variables\n", + "\n", + "Run this cell after the SGLang server is available. It resolves local input/output paths and stores generated outputs under `outputs/cosmos3_action_sglang/` by default.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdvl-vars-code", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import os\n", + "\n", + "\n", + "def find_repo_root(start: Path) -> Path:\n", + " for path in [start, *start.parents]:\n", + " if (path / \"README.md\").exists() and (path / \"cookbooks\").exists():\n", + " return path\n", + "\n", + " return start\n", + "\n", + "\n", + "COSMOS_ROOT = find_repo_root(Path.cwd().resolve())\n", + "COSMOS3_REPO = Path(os.environ.get(\"COSMOS3_REPO\", COSMOS_ROOT / \"packages\" / \"cosmos3\")).resolve()\n", + "COSMOS3_OUTPUT_ROOT = Path(\n", + " os.environ.get(\"COSMOS3_SGLANG_OUTPUT_ROOT\", COSMOS_ROOT / \"outputs\" / \"cosmos3_action_sglang\")\n", + ").resolve()\n", + "COSMOS3_INPUT_DIR = COSMOS3_OUTPUT_ROOT / \"inputs\"\n", + "SGLANG_BASE_URL = os.environ.get(\"COSMOS3_SGLANG_BASE_URL\", \"http://localhost:30000\").rstrip(\"/\")\n", + "\n", + "\n", + "def resolve_input(rel_path: str) -> str:\n", + " path = (COSMOS_ROOT / rel_path).resolve()\n", + " assert path.exists(), f\"missing input: {path}\"\n", + " return str(path)\n", + "\n", + "\n", + "COSMOS3_OUTPUT_ROOT.mkdir(parents=True, exist_ok=True)\n", + "COSMOS3_INPUT_DIR.mkdir(parents=True, exist_ok=True)\n", + "\n", + "print(\"COSMOS_ROOT:\", COSMOS_ROOT)\n", + "print(\"COSMOS3_REPO:\", COSMOS3_REPO)\n", + "print(\"COSMOS3_INPUT_DIR:\", COSMOS3_INPUT_DIR)\n", + "print(\"COSMOS3_OUTPUT_ROOT:\", COSMOS3_OUTPUT_ROOT)\n", + "print(\"COSMOS3_SGLANG_BASE_URL:\", SGLANG_BASE_URL)\n" + ] + }, + { + "cell_type": "markdown", + "id": "fdvl-av-md", + "metadata": {}, + "source": [ + "## AV\n", + "\n", + "In this example, we show how to provide a set of ego poses of a autonomous vehicle and an image to generate driving videos using Cosmos3-Nano.\n" + ] + }, + { + "cell_type": "markdown", + "id": "fdvl-av-spec-md", + "metadata": {}, + "source": [ + "### Create the AV Forward-Dynamics Input Spec\n", + "\n", + "AV forward-dynamics inference is driven by a JSONL spec, one line per run. Each line shares the same start frame (`vision_path`) but uses a different ego trajectory (`action_path`), so we get one generated video per trajectory.\n", + "\n", + "The action input is prepared in a JSON file, which can be converted from camera poses (camera-to-world transformation, OpenCV convention, unit in meter) via `pose_abs_to_rel`:\n", + "\n", + "```python\n", + "if str(COSMOS3_REPO) not in sys.path:\n", + " sys.path.insert(0, str(COSMOS3_REPO))\n", + "from cosmos_framework.data.vfm.action.pose_utils import pose_abs_to_rel\n", + "\n", + "poses_abs = np.array([...]) # [T, 4, 4], camera-to-world transformation in opencv convention, unit in meter\n", + "poses_rel = pose_abs_to_rel(\n", + " poses_abs,\n", + " rotation_format=\"rot6d\",\n", + " pose_convention=\"backward_framewise\",\n", + " translation_scale=1.35,\n", + ") # [T-1, 9], translation(3), rot6d(6), framewise relative transformation\n", + "\n", + "with open(\"custom_traj.json\", \"w\") as f:\n", + " json.dump(poses_rel, f)\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdvl-av-spec-code", + "metadata": {}, + "outputs": [], + "source": [ + "# `resolve_input` and the COSMOS3_* paths come from the variables cell.\n", + "import json\n", + "\n", + "# Local AV inputs, relative to the cosmos repo root.\n", + "av_input_image = \"cookbooks/cosmos3/generator/action/assets/images/av_0.jpg\"\n", + "av_input_actions = {\n", + " \"av_forward\": \"cookbooks/cosmos3/generator/action/assets/actions/av_traj_forward.json\",\n", + " \"av_left\": \"cookbooks/cosmos3/generator/action/assets/actions/av_traj_left.json\",\n", + " \"av_right\": \"cookbooks/cosmos3/generator/action/assets/actions/av_traj_right.json\",\n", + "}\n", + "\n", + "av_vision_path = resolve_input(av_input_image)\n", + "av_records = [\n", + " {\n", + " \"action_chunk_size\": 60,\n", + " \"action_path\": resolve_input(action_rel),\n", + " \"domain_name\": \"av\",\n", + " \"fps\": 10,\n", + " \"image_size\": 480,\n", + " \"view_point\": \"ego_view\",\n", + " \"model_mode\": \"forward_dynamics\",\n", + " \"name\": name,\n", + " \"prompt\": \"You are an autonomous vehicle planning system.\",\n", + " \"seed\": 0,\n", + " \"vision_path\": av_vision_path,\n", + " }\n", + " for name, action_rel in av_input_actions.items()\n", + "]\n", + "\n", + "COSMOS3_INPUT_DIR.mkdir(parents=True, exist_ok=True)\n", + "av_fd_input_path = COSMOS3_INPUT_DIR / \"action_forward_dynamics_av_custom.jsonl\"\n", + "av_fd_input_path.write_text(\"\".join(json.dumps(r) + \"\\n\" for r in av_records))\n", + "av_fd_output_dir = COSMOS3_OUTPUT_ROOT / \"action_forward_dynamics_av_custom\"\n", + "\n", + "os.environ[\"COSMOS3_AV_FD_INPUT\"] = str(av_fd_input_path)\n", + "os.environ[\"COSMOS3_AV_FD_OUTPUT\"] = str(av_fd_output_dir)\n", + "\n", + "print(\"wrote AV spec:\", av_fd_input_path)\n", + "print(\"AV runs:\", list(av_input_actions))\n", + "print(av_fd_input_path.read_text())\n" + ] + }, + { + "cell_type": "markdown", + "id": "fdvl-av-traj-md", + "metadata": {}, + "source": [ + "### Visualize AV Input Trajectories\n", + "\n", + "Before generating any video, plot each input ego trajectory as a 3D camera path with frustums and a top-down bird's-eye view.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdvl-av-traj-code", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import json\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.collections import LineCollection\n", + "from mpl_toolkits.mplot3d.art3d import Line3DCollection\n", + "import os\n", + "\n", + "# The notebook kernel may differ from the framework venv, so put the repo on the\n", + "# path before importing `cosmos_framework`.\n", + "COSMOS3_FRAMEWORK_PATH = os.environ.get(\"COSMOS3_FRAMEWORK_PATH\")\n", + "if str(COSMOS3_FRAMEWORK_PATH) not in sys.path:\n", + " sys.path.insert(0, str(COSMOS3_FRAMEWORK_PATH))\n", + "from cosmos_framework.data.vfm.action.pose_utils import pose_rel_to_abs\n", + "\n", + "# frustum: apex + image-rectangle corners (camera +Z forward), and their edges\n", + "_FRUSTUM = np.array([[0, 0, 0], [-1, -1, 1], [1, -1, 1], [1, 1, 1], [-1, 1, 1]], float)\n", + "_EDGES = [(0, 1), (0, 2), (0, 3), (0, 4), (1, 2), (2, 3), (3, 4), (4, 1)]\n", + "\n", + "\n", + "def visualize_pose(poses_abs, *, n_frustums=20, scale_frac=0.03, aspect=16 / 9,\n", + " fov_deg=60.0, vertical_exaggeration=1.0, cmap=\"turbo\",\n", + " title=None, save_path=None, show=True):\n", + " \"\"\"3D camera trajectory (with frustums) + a top-down bird's-eye view.\"\"\"\n", + " poses_abs = np.asarray(poses_abs)\n", + " pos = poses_abs[:, :3, 3]\n", + " fwd = poses_abs[:, :3, 2]\n", + " T = len(pos)\n", + " colors = plt.get_cmap(cmap)(np.arange(T) / max(T - 1, 1))\n", + " scale = max(np.ptp(pos, axis=0).max() * scale_frac, 1e-3)\n", + " step = max(1, T // max(n_frustums, 1))\n", + " xzy = [0, 2, 1]\n", + "\n", + " fig = plt.figure(figsize=(14, 6))\n", + "\n", + " ax = fig.add_subplot(1, 2, 1, projection=\"3d\")\n", + " path = pos[:, xzy]\n", + " ax.plot(*path.T, color=\"0.6\", lw=1.0, alpha=0.7)\n", + " lines, lcolors, allpts = [], [], [path]\n", + " for i in range(0, T, step):\n", + " cw = ((_FRUSTUM * [aspect, 1, 1] * scale * np.tan(np.radians(fov_deg) / 2))\n", + " @ poses_abs[i, :3, :3].T + poses_abs[i, :3, 3])[:, xzy]\n", + " allpts.append(cw)\n", + " lines += [[cw[a], cw[b]] for a, b in _EDGES]\n", + " lcolors += [colors[i]] * len(_EDGES)\n", + " ax.add_collection3d(Line3DCollection(lines, colors=lcolors, linewidths=1.2))\n", + " ax.scatter(*path[0], color=\"lime\", s=80, edgecolor=\"k\", label=\"first frame\", zorder=5)\n", + " ax.scatter(*path[-1], color=\"red\", s=80, edgecolor=\"k\", label=\"last frame\", zorder=5)\n", + " rng = np.clip(np.ptp(np.concatenate(allpts), axis=0), 1e-9, None)\n", + " ax.set_box_aspect((rng[0], rng[1], rng[2] * vertical_exaggeration))\n", + " ax.set_xlabel(\"X (m)\", labelpad=12)\n", + " ax.set_ylabel(\"Z forward (m)\", labelpad=12)\n", + " ax.set_zlabel(\"Y up (m)\", labelpad=10)\n", + " ax.set_zticks([])\n", + " ax.set_title(title or f\"Camera trajectory + frustums ({T} frames)\")\n", + " ax.legend(loc=\"upper left\")\n", + " ax.view_init(elev=22, azim=-70)\n", + "\n", + " ax2 = fig.add_subplot(1, 2, 2)\n", + " seg = np.stack([pos[:-1, [0, 2]], pos[1:, [0, 2]]], axis=1)\n", + " lc = LineCollection(seg, cmap=cmap, norm=plt.Normalize(0, T - 1), linewidth=2.5)\n", + " lc.set_array(np.arange(T - 1))\n", + " ax2.add_collection(lc)\n", + " ax2.quiver(pos[::step, 0], pos[::step, 2], fwd[::step, 0], fwd[::step, 2],\n", + " color=colors[::step], angles=\"xy\", width=0.005, scale=22, zorder=3)\n", + " ax2.scatter(*pos[0, [0, 2]], color=\"lime\", s=80, edgecolor=\"k\", label=\"first frame\", zorder=5)\n", + " ax2.scatter(*pos[-1, [0, 2]], color=\"red\", s=80, edgecolor=\"k\", label=\"last frame\", zorder=5)\n", + " ax2.set_xlabel(\"X (m)\")\n", + " ax2.set_ylabel(\"Z forward (m)\")\n", + " ax2.set_title(\"Top-down (bird's-eye view)\")\n", + " ax2.set_aspect(\"equal\", adjustable=\"datalim\")\n", + " ax2.autoscale_view()\n", + " ax2.legend()\n", + " fig.colorbar(lc, ax=ax2, label=\"frame index\")\n", + "\n", + " plt.tight_layout(w_pad=6)\n", + " if save_path:\n", + " fig.savefig(save_path, dpi=120, bbox_inches=\"tight\")\n", + " print(\"saved\", save_path)\n", + " if show:\n", + " plt.show()\n", + "\n", + "\n", + "for record in av_records:\n", + " name = record[\"name\"]\n", + " with open(record[\"action_path\"]) as f:\n", + " poses_rel = np.array(json.load(f))\n", + "\n", + " # AV action convention: rot6d rotation, backward_framewise, translation_scale = 1.35.\n", + " poses_abs = pose_rel_to_abs(\n", + " poses_rel,\n", + " rotation_format=\"rot6d\",\n", + " pose_convention=\"backward_framewise\",\n", + " translation_scale=1.35,\n", + " )\n", + " print(name, poses_rel.shape, poses_abs.shape)\n", + " visualize_pose(poses_abs, title=f\"{name}: camera trajectory + frustums ({len(poses_abs)} frames)\", show=True)\n" + ] + }, + { + "cell_type": "markdown", + "id": "fdvl-av-run-md", + "metadata": {}, + "source": [ + "### Run AV Forward-Dynamics Inference\n", + "\n", + "Runs `Cosmos3-Nano` on every line of the AV spec through SGLang. Each run writes its video to:\n", + "\n", + "```text\n", + "/action_forward_dynamics_av_custom//vision.mp4\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdvl-av-run-code", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import mimetypes\n", + "import time\n", + "from pathlib import Path\n", + "from PIL import Image\n", + "\n", + "try:\n", + " import requests\n", + "except ImportError as exc:\n", + " raise RuntimeError(\"Install requests in this notebook kernel: pip install requests\") from exc\n", + "\n", + "\n", + "def check_sglang_server(timeout_s: int = 600, interval_s: int = 10) -> None:\n", + " deadline = time.time() + timeout_s\n", + " last_error: Exception | None = None\n", + " while time.time() < deadline:\n", + " try:\n", + " response = requests.get(f\"{SGLANG_BASE_URL}/v1/models\", timeout=10)\n", + " response.raise_for_status()\n", + " print(response.json())\n", + " return\n", + " except requests.RequestException as exc:\n", + " last_error = exc\n", + " print(f\"Waiting for SGLang server at {SGLANG_BASE_URL}: {exc}\")\n", + " time.sleep(interval_s)\n", + " raise RuntimeError(\n", + " f\"SGLang server did not become ready at {SGLANG_BASE_URL} within {timeout_s}s. \"\n", + " \"Check `docker logs -f cosmos3-sglang-omni-notebook`.\"\n", + " ) from last_error\n", + "\n", + "\n", + "def submit_forward_dynamics(record: dict, fd_output_dir: Path) -> dict:\n", + " run_dir = fd_output_dir / record[\"name\"]\n", + " run_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " vision_path = Path(record[\"vision_path\"])\n", + " input_width, input_height = Image.open(vision_path).size\n", + " mime_type = mimetypes.guess_type(vision_path.name)[0] or \"application/octet-stream\"\n", + " extra_params = {\n", + " \"action_mode\": \"forward_dynamics\",\n", + " \"domain_name\": record[\"domain_name\"],\n", + " \"action_view_point\": record[\"view_point\"],\n", + " \"action\": json.loads(Path(record[\"action_path\"]).read_text()),\n", + " \"guardrails\": False,\n", + " }\n", + " prompt = str(record.get(\"prompt\") or \"\").strip() or \"A robot manipulates an object.\"\n", + " form = {\n", + " \"prompt\": prompt,\n", + " \"num_frames\": record[\"action_chunk_size\"] + 1,\n", + " \"fps\": record[\"fps\"],\n", + " \"size\": f\"{input_width}x{input_height}\",\n", + " \"num_inference_steps\": 30,\n", + " \"guidance_scale\": 1.0,\n", + " \"flow_shift\": 10.0,\n", + " \"seed\": record[\"seed\"],\n", + " \"extra_params\": json.dumps(extra_params),\n", + " }\n", + "\n", + " with vision_path.open(\"rb\") as image_file:\n", + " response = requests.post(\n", + " f\"{SGLANG_BASE_URL}/v1/videos\",\n", + " data={key: str(value) for key, value in form.items()},\n", + " files={\"input_reference\": (vision_path.name, image_file, mime_type)},\n", + " timeout=120,\n", + " )\n", + " if not response.ok:\n", + " (run_dir / \"error_response.txt\").write_text(response.text)\n", + " print(\"SGLang request failed:\", response.status_code)\n", + " print(response.text)\n", + " print(\"form:\", json.dumps(form, indent=2))\n", + " print(\"extra_params keys:\", sorted(extra_params))\n", + " print(\"action shape:\", [len(extra_params[\"action\"]), len(extra_params[\"action\"][0]) if extra_params[\"action\"] else 0])\n", + " response.raise_for_status()\n", + " initial = response.json()\n", + " (run_dir / \"response.json\").write_text(json.dumps(initial, indent=2))\n", + "\n", + " while True:\n", + " response = requests.get(f\"{SGLANG_BASE_URL}/v1/videos/{initial['id']}\", timeout=30)\n", + " response.raise_for_status()\n", + " final = response.json()\n", + " (run_dir / \"final.json\").write_text(json.dumps(final, indent=2))\n", + " print(initial[\"id\"], final.get(\"status\"), f\"{final.get('progress', 0)}%\")\n", + " if final.get(\"status\") == \"completed\":\n", + " break\n", + " if final.get(\"status\") in {\"failed\", \"cancelled\"}:\n", + " raise RuntimeError(json.dumps(final, indent=2))\n", + " time.sleep(2)\n", + "\n", + " response = requests.get(f\"{SGLANG_BASE_URL}/v1/videos/{initial['id']}/content\", timeout=300)\n", + " response.raise_for_status()\n", + " video_path = run_dir / \"vision.mp4\"\n", + " video_path.write_bytes(response.content)\n", + "\n", + " action = final.get(\"action\")\n", + " if action is not None:\n", + " (run_dir / \"action.json\").write_text(json.dumps(action, indent=2))\n", + "\n", + " print(\"saved\", video_path)\n", + " if action is not None:\n", + " print(\"action shape:\", action.get(\"shape\"), \"dtype:\", action.get(\"dtype\"))\n", + " return {\"record\": record, \"initial\": initial, \"final\": final, \"run_dir\": run_dir, \"video_path\": video_path, \"action\": action}\n", + "\n", + "\n", + "check_sglang_server()\n", + "av_results = []\n", + "for record in av_records:\n", + " print(f\"\\nSubmitting {record['name']}\")\n", + " av_results.append(submit_forward_dynamics(record, av_fd_output_dir))\n" + ] + }, + { + "cell_type": "markdown", + "id": "fdvl-av-preview-md", + "metadata": {}, + "source": [ + "### Visualize AV Generated Videos\n", + "\n", + "\n", + "`Video(..., embed=True)` base64-inlines a file into the notebook, and embedding full-resolution runs can freeze the front-end. This cell first transcodes each video to a small preview using the ffmpeg binary bundled with `imageio-ffmpeg`, then embeds the previews. The full-resolution `vision.mp4` files are left untouched.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdvl-av-preview-code", + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess\n", + "import imageio_ffmpeg\n", + "from IPython.display import Video, display\n", + "\n", + "FFMPEG = imageio_ffmpeg.get_ffmpeg_exe()\n", + "\n", + "\n", + "def make_preview(src: Path, crf: int = 28) -> Path:\n", + " \"\"\"Re-encode `src` to a compact, browser-friendly mp4 (cached).\"\"\"\n", + " preview = src.with_name(f\"{src.stem}_preview.mp4\")\n", + " if not preview.exists():\n", + " subprocess.run(\n", + " [FFMPEG, \"-y\", \"-loglevel\", \"error\", \"-i\", str(src),\n", + " \"-c:v\", \"libx264\", \"-crf\", str(crf),\n", + " \"-preset\", \"veryfast\", \"-an\", \"-pix_fmt\", \"yuv420p\", str(preview)],\n", + " check=True,\n", + " )\n", + " return preview\n", + "\n", + "\n", + "for record in av_records:\n", + " name = record[\"name\"]\n", + " src = av_fd_output_dir / name / \"vision.mp4\"\n", + " assert src.exists(), f\"missing: {src}\"\n", + " preview = make_preview(src)\n", + " print(f\"{name} ({src.stat().st_size // 1024} KB -> {preview.stat().st_size // 1024} KB preview)\")\n", + " display(Video(str(preview), embed=True))\n" + ] + }, + { + "cell_type": "markdown", + "id": "fdvl-robotics-md", + "metadata": {}, + "source": [ + "## Robotics\n", + "\n", + "In this example, we show how to start from a LeRobot dataset of DROID and run **multiview** generation for robotics manipulation **autoregressively**.\n" + ] + }, + { + "cell_type": "markdown", + "id": "fdvl-robotics-spec-md", + "metadata": {}, + "source": [ + "### Create the Robotics Autoregressive Forward-Dynamics Plan\n", + "\n", + "Robotics forward-dynamics runs autoregressively over five contiguous 16-action DROID chunks. This cell writes the GT first conditioning image for chunk 0 and one action JSON per chunk. Later chunks receive their conditioning image from the previous chunk's generated last frame during the inference loop.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdvl-robotics-spec-code", + "metadata": {}, + "outputs": [], + "source": [ + "# `resolve_input` and the COSMOS3_* paths come from the variables cell.\n", + "import json\n", + "import os\n", + "import sys\n", + "\n", + "from PIL import Image\n", + "\n", + "# The notebook kernel may differ from the framework venv, so put the repo on the\n", + "# path before importing `cosmos_framework`.\n", + "COSMOS3_FRAMEWORK_PATH = os.environ.get(\"COSMOS3_FRAMEWORK_PATH\")\n", + "if str(COSMOS3_FRAMEWORK_PATH) not in sys.path:\n", + " sys.path.insert(0, str(COSMOS3_FRAMEWORK_PATH))\n", + "\n", + "from cosmos_framework.data.vfm.action.datasets import DROIDLeRobotDataset\n", + "\n", + "import av\n", + "import torch\n", + "import numpy as np\n", + "import cosmos_framework.data.vfm.action.datasets.droid_lerobot_dataset as droid_ds\n", + "\n", + "def decode_video_frames_av(video_path, timestamps, tolerance_s, backend=None):\n", + " loaded_ts = []\n", + " loaded_frames = []\n", + "\n", + " with av.open(str(video_path)) as container:\n", + " stream = container.streams.video[0]\n", + " for frame in container.decode(stream):\n", + " ts = float(frame.pts * frame.time_base) if frame.pts is not None else float(frame.time)\n", + " loaded_ts.append(ts)\n", + " loaded_frames.append(frame.to_ndarray(format=\"rgb24\"))\n", + "\n", + " loaded_ts = torch.tensor(loaded_ts, dtype=torch.float32)\n", + " query_ts = torch.tensor([float(t) for t in timestamps], dtype=torch.float32)\n", + "\n", + " dist = torch.cdist(query_ts[:, None], loaded_ts[:, None], p=1)\n", + " min_dist, idx = dist.min(dim=1)\n", + " if not bool((min_dist < tolerance_s).all()):\n", + " raise ValueError(f\"No frame within tolerance: {min_dist.tolist()} > {tolerance_s}\")\n", + "\n", + " frames = np.stack([loaded_frames[int(i)] for i in idx], axis=0)\n", + " return torch.from_numpy(frames).permute(0, 3, 1, 2).float() / 255.0\n", + "\n", + "droid_ds.decode_video_frames = decode_video_frames_av\n", + "\n", + "robotics_dataset_root = resolve_input(\"cookbooks/cosmos3/generator/action/assets/droid_lerobot_example\")\n", + "robotics_dataset = DROIDLeRobotDataset(root=robotics_dataset_root)\n", + "robotics_num_chunks = 5\n", + "robotics_chunk_length = 16\n", + "robotics_chunk_starts = [chunk_idx * robotics_chunk_length for chunk_idx in range(robotics_num_chunks)]\n", + "assert robotics_chunk_starts[-1] < len(robotics_dataset)\n", + "\n", + "COSMOS3_INPUT_DIR.mkdir(parents=True, exist_ok=True)\n", + "robotics_initial_vision_path = COSMOS3_INPUT_DIR / \"robotics_droid_autoregressive_input_chunk_00.png\"\n", + "robotics_records = []\n", + "\n", + "for chunk_idx, sample_idx in enumerate(robotics_chunk_starts):\n", + " robotics_sample = robotics_dataset[sample_idx]\n", + " assert int(robotics_sample[\"action\"].shape[0]) == robotics_chunk_length\n", + "\n", + " chunk_name = f\"robotics_action_cond_chunk_{chunk_idx:02d}\"\n", + " robotics_action_path = COSMOS3_INPUT_DIR / f\"robotics_droid_action_chunk_{chunk_idx:02d}.json\"\n", + " robotics_action_path.write_text(json.dumps(robotics_sample[\"action\"].cpu().tolist()))\n", + "\n", + " if chunk_idx == 0:\n", + " first_frame = robotics_sample[\"video\"][:, 0].permute(1, 2, 0).cpu().numpy()\n", + " Image.fromarray(first_frame).save(robotics_initial_vision_path)\n", + " vision_path = robotics_initial_vision_path\n", + " else:\n", + " vision_path = COSMOS3_INPUT_DIR / f\"robotics_droid_autoregressive_input_chunk_{chunk_idx:02d}.png\"\n", + "\n", + " robotics_records.append(\n", + " {\n", + " \"action_chunk_size\": robotics_chunk_length,\n", + " \"action_path\": str(robotics_action_path),\n", + " \"domain_name\": \"droid_lerobot\",\n", + " \"fps\": int(robotics_sample[\"conditioning_fps\"]),\n", + " \"image_size\": 480,\n", + " \"view_point\": robotics_sample[\"viewpoint\"],\n", + " \"model_mode\": \"forward_dynamics\",\n", + " \"name\": chunk_name,\n", + " \"prompt\": robotics_sample[\"ai_caption\"],\n", + " \"seed\": 0,\n", + " \"vision_path\": str(vision_path),\n", + " }\n", + " )\n", + "\n", + "robotics_fd_input_path = COSMOS3_INPUT_DIR / \"action_forward_dynamics_robotics_custom.jsonl\"\n", + "robotics_fd_input_path.write_text(\"\".join(json.dumps(r) + \"\\n\" for r in robotics_records))\n", + "robotics_fd_output_dir = COSMOS3_OUTPUT_ROOT / \"action_forward_dynamics_robotics_custom\"\n", + "\n", + "os.environ[\"COSMOS3_ROBOTICS_FD_INPUT\"] = str(robotics_fd_input_path)\n", + "os.environ[\"COSMOS3_ROBOTICS_FD_OUTPUT\"] = str(robotics_fd_output_dir)\n", + "\n", + "print(\"loaded DROID samples from:\", robotics_dataset_root)\n", + "print(\"chunk starts:\", robotics_chunk_starts)\n", + "print(\"total action frames:\", robotics_num_chunks * robotics_chunk_length)\n", + "print(\"wrote GT initial frame:\", robotics_initial_vision_path)\n", + "print(\"wrote robotics autoregressive plan:\", robotics_fd_input_path)\n", + "print(robotics_fd_input_path.read_text())\n" + ] + }, + { + "cell_type": "markdown", + "id": "fdvl-robotics-run-md", + "metadata": {}, + "source": [ + "### Run Robotics Autoregressive Forward-Dynamics Inference\n", + "\n", + "Runs `Cosmos3-Nano` once per robotics chunk through SGLang. Chunk 0 uses the DROID GT first frame. After each chunk finishes, the cell extracts that chunk's last generated frame and uses it as the conditioning image for the next chunk. Guardrails are disabled for this robotics run via `extra_params={\"guardrails\": false}`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdvl-robotics-run-code", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import mimetypes\n", + "import subprocess\n", + "import time\n", + "from pathlib import Path\n", + "\n", + "import imageio_ffmpeg\n", + "from PIL import Image\n", + "\n", + "try:\n", + " import requests\n", + "except ImportError as exc:\n", + " raise RuntimeError(\"Install requests in this notebook kernel: pip install requests\") from exc\n", + "\n", + "FFMPEG = imageio_ffmpeg.get_ffmpeg_exe()\n", + "\n", + "\n", + "def check_sglang_server(timeout_s: int = 600, interval_s: int = 10) -> None:\n", + " deadline = time.time() + timeout_s\n", + " last_error: Exception | None = None\n", + " while time.time() < deadline:\n", + " try:\n", + " response = requests.get(f\"{SGLANG_BASE_URL}/v1/models\", timeout=10)\n", + " response.raise_for_status()\n", + " print(response.json())\n", + " return\n", + " except requests.RequestException as exc:\n", + " last_error = exc\n", + " print(f\"Waiting for SGLang server at {SGLANG_BASE_URL}: {exc}\")\n", + " time.sleep(interval_s)\n", + " raise RuntimeError(\n", + " f\"SGLang server did not become ready at {SGLANG_BASE_URL} within {timeout_s}s. \"\n", + " \"Check `docker logs -f cosmos3-sglang-omni-notebook`.\"\n", + " ) from last_error\n", + "\n", + "\n", + "def submit_forward_dynamics(record: dict, fd_output_dir: Path, *, disable_guardrails: bool = False) -> dict:\n", + " run_dir = fd_output_dir / record[\"name\"]\n", + " run_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " vision_path = Path(record[\"vision_path\"])\n", + " input_width, input_height = Image.open(vision_path).size\n", + " target_width = (input_width // 16) * 16\n", + " target_height = (input_height // 16) * 16\n", + " mime_type = mimetypes.guess_type(vision_path.name)[0] or \"application/octet-stream\"\n", + " extra_params = {\n", + " \"action_mode\": \"forward_dynamics\",\n", + " \"domain_name\": record[\"domain_name\"],\n", + " \"action_view_point\": record[\"view_point\"],\n", + " \"action\": json.loads(Path(record[\"action_path\"]).read_text()),\n", + " \"guardrails\": False,\n", + " }\n", + " if disable_guardrails:\n", + " extra_params[\"guardrails\"] = False\n", + "\n", + " prompt = str(record.get(\"prompt\") or \"\").strip() or \" \"\n", + " form = {\n", + " \"prompt\": prompt,\n", + " \"num_frames\": record[\"action_chunk_size\"] + 1,\n", + " \"fps\": record[\"fps\"],\n", + " \"size\": f\"{target_width}x{target_height}\",\n", + " \"num_inference_steps\": 30,\n", + " \"guidance_scale\": 1.0,\n", + " \"flow_shift\": 10.0,\n", + " \"seed\": record[\"seed\"],\n", + " \"extra_params\": json.dumps(extra_params),\n", + " }\n", + "\n", + " with vision_path.open(\"rb\") as image_file:\n", + " response = requests.post(\n", + " f\"{SGLANG_BASE_URL}/v1/videos\",\n", + " data={key: str(value) for key, value in form.items()},\n", + " files={\"input_reference\": (vision_path.name, image_file, mime_type)},\n", + " timeout=120,\n", + " )\n", + " if not response.ok:\n", + " (run_dir / \"error_response.txt\").write_text(response.text)\n", + " print(\"SGLang request failed:\", response.status_code)\n", + " print(response.text)\n", + " print(\"form:\", json.dumps(form, indent=2))\n", + " print(\"extra_params keys:\", sorted(extra_params))\n", + " print(\"action shape:\", [len(extra_params[\"action\"]), len(extra_params[\"action\"][0]) if extra_params[\"action\"] else 0])\n", + " response.raise_for_status()\n", + " initial = response.json()\n", + " (run_dir / \"response.json\").write_text(json.dumps(initial, indent=2))\n", + "\n", + " while True:\n", + " response = requests.get(f\"{SGLANG_BASE_URL}/v1/videos/{initial['id']}\", timeout=30)\n", + " response.raise_for_status()\n", + " final = response.json()\n", + " (run_dir / \"final.json\").write_text(json.dumps(final, indent=2))\n", + " print(initial[\"id\"], final.get(\"status\"), f\"{final.get('progress', 0)}%\")\n", + " if final.get(\"status\") == \"completed\":\n", + " break\n", + " if final.get(\"status\") in {\"failed\", \"cancelled\"}:\n", + " raise RuntimeError(json.dumps(final, indent=2))\n", + " time.sleep(2)\n", + "\n", + " response = requests.get(f\"{SGLANG_BASE_URL}/v1/videos/{initial['id']}/content\", timeout=300)\n", + " response.raise_for_status()\n", + " video_path = run_dir / \"vision.mp4\"\n", + " video_path.write_bytes(response.content)\n", + "\n", + " action = final.get(\"action\")\n", + " if action is not None:\n", + " (run_dir / \"action.json\").write_text(json.dumps(action, indent=2))\n", + "\n", + " print(\"saved\", video_path)\n", + " if action is not None:\n", + " print(\"action shape:\", action.get(\"shape\"), \"dtype:\", action.get(\"dtype\"))\n", + " return {\"record\": record, \"initial\": initial, \"final\": final, \"run_dir\": run_dir, \"video_path\": video_path, \"action\": action}\n", + "\n", + "\n", + "check_sglang_server()\n", + "robotics_results = []\n", + "robotics_actual_records = []\n", + "current_vision_path = Path(robotics_records[0][\"vision_path\"])\n", + "assert current_vision_path.exists(), f\"missing initial conditioning image: {current_vision_path}\"\n", + "\n", + "for chunk_idx, base_record in enumerate(robotics_records):\n", + " record = dict(base_record)\n", + " record[\"vision_path\"] = str(current_vision_path)\n", + " robotics_records[chunk_idx][\"vision_path\"] = str(current_vision_path)\n", + " robotics_actual_records.append(record)\n", + "\n", + " print(f\"\\nSubmitting {record['name']}\")\n", + " print(\"conditioning image:\", current_vision_path)\n", + " result = submit_forward_dynamics(record, robotics_fd_output_dir, disable_guardrails=True)\n", + " robotics_results.append(result)\n", + "\n", + " if chunk_idx + 1 < len(robotics_records):\n", + " next_vision_path = COSMOS3_INPUT_DIR / f\"robotics_droid_autoregressive_input_chunk_{chunk_idx + 1:02d}.png\"\n", + " subprocess.run(\n", + " [\n", + " FFMPEG,\n", + " \"-y\",\n", + " \"-loglevel\",\n", + " \"error\",\n", + " \"-i\",\n", + " str(result[\"video_path\"]),\n", + " \"-vf\",\n", + " fr\"select=eq(n\\,{record['action_chunk_size']})\",\n", + " \"-frames:v\",\n", + " \"1\",\n", + " str(next_vision_path),\n", + " ],\n", + " check=True,\n", + " )\n", + " assert next_vision_path.exists(), f\"failed to extract next conditioning image: {next_vision_path}\"\n", + " current_vision_path = next_vision_path\n", + "\n", + "robotics_fd_input_path.write_text(\"\".join(json.dumps(r) + \"\\n\" for r in robotics_actual_records))\n", + "print(\"wrote autoregressive run spec:\", robotics_fd_input_path)\n", + "print(\"completed chunks:\", [record[\"name\"] for record in robotics_actual_records])\n" + ] + }, + { + "cell_type": "markdown", + "id": "fdvl-robotics-stitch-md", + "metadata": {}, + "source": [ + "### Stitch Robotics Generated Chunks\n", + "\n", + "Each autoregressive chunk video includes its conditioning frame at frame 0. This cell drops that first frame from every chunk and concatenates the remaining 16 generated frames per chunk into one 80-frame video.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdvl-robotics-stitch-code", + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess\n", + "import imageio_ffmpeg\n", + "\n", + "FFMPEG = imageio_ffmpeg.get_ffmpeg_exe()\n", + "robotics_stitch_dir = robotics_fd_output_dir / \"_stitched_segments\"\n", + "robotics_stitch_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "segment_paths = []\n", + "for record in robotics_records:\n", + " src = robotics_fd_output_dir / record[\"name\"] / \"vision.mp4\"\n", + " assert src.exists(), f\"missing: {src}\"\n", + "\n", + " segment = robotics_stitch_dir / f\"{record['name']}_generated.mp4\"\n", + " subprocess.run(\n", + " [\n", + " FFMPEG,\n", + " \"-y\",\n", + " \"-loglevel\",\n", + " \"error\",\n", + " \"-i\",\n", + " str(src),\n", + " \"-vf\",\n", + " r\"select=gte(n\\,1),setpts=N/FRAME_RATE/TB\",\n", + " \"-an\",\n", + " \"-r\",\n", + " str(record[\"fps\"]),\n", + " \"-c:v\",\n", + " \"libx264\",\n", + " \"-crf\",\n", + " \"18\",\n", + " \"-preset\",\n", + " \"veryfast\",\n", + " \"-pix_fmt\",\n", + " \"yuv420p\",\n", + " str(segment),\n", + " ],\n", + " check=True,\n", + " )\n", + " segment_paths.append(segment)\n", + "\n", + "concat_file = robotics_stitch_dir / \"concat.txt\"\n", + "concat_file.write_text(\"\".join(f\"file '{path.as_posix()}'\\n\" for path in segment_paths))\n", + "\n", + "robotics_stitched_video_path = robotics_fd_output_dir / \"robotics_action_cond_stitched.mp4\"\n", + "subprocess.run(\n", + " [\n", + " FFMPEG,\n", + " \"-y\",\n", + " \"-loglevel\",\n", + " \"error\",\n", + " \"-f\",\n", + " \"concat\",\n", + " \"-safe\",\n", + " \"0\",\n", + " \"-i\",\n", + " str(concat_file),\n", + " \"-c\",\n", + " \"copy\",\n", + " str(robotics_stitched_video_path),\n", + " ],\n", + " check=True,\n", + ")\n", + "\n", + "print(\"stitched robotics video:\", robotics_stitched_video_path)\n", + "print(\"expected generated frames:\", len(robotics_records) * robotics_chunk_length)\n", + "print(\"size KB:\", robotics_stitched_video_path.stat().st_size // 1024)\n" + ] + }, + { + "cell_type": "markdown", + "id": "fdvl-robotics-preview-md", + "metadata": {}, + "source": [ + "### Visualize Robotics Generated Videos\n", + "\n", + "`Video(..., embed=True)` base64-inlines a file into the notebook, and embedding full-resolution runs can freeze the front-end. This cell first displays a compact preview of the stitched 80-frame video when available, then previews each per-chunk video. The full-resolution `vision.mp4` files and stitched mp4 are left untouched.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdvl-robotics-preview-code", + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess\n", + "import imageio_ffmpeg\n", + "from IPython.display import Video, display\n", + "\n", + "FFMPEG = imageio_ffmpeg.get_ffmpeg_exe()\n", + "\n", + "\n", + "def make_preview(src: Path, crf: int = 28) -> Path:\n", + " \"\"\"Re-encode `src` to a compact, browser-friendly mp4 (cached).\"\"\"\n", + " preview = src.with_name(f\"{src.stem}_preview.mp4\")\n", + " if not preview.exists() or preview.stat().st_mtime < src.stat().st_mtime:\n", + " subprocess.run(\n", + " [FFMPEG, \"-y\", \"-loglevel\", \"error\", \"-i\", str(src),\n", + " \"-c:v\", \"libx264\", \"-crf\", str(crf),\n", + " \"-preset\", \"veryfast\", \"-an\", \"-pix_fmt\", \"yuv420p\", str(preview)],\n", + " check=True,\n", + " )\n", + " return preview\n", + "\n", + "\n", + "if \"robotics_stitched_video_path\" in globals():\n", + " assert robotics_stitched_video_path.exists(), f\"missing: {robotics_stitched_video_path}\"\n", + " stitched_preview = make_preview(robotics_stitched_video_path)\n", + " print(\n", + " f\"stitched ({robotics_stitched_video_path.stat().st_size // 1024} KB -> \"\n", + " f\"{stitched_preview.stat().st_size // 1024} KB preview)\"\n", + " )\n", + " display(Video(str(stitched_preview), embed=True))\n", + "\n", + "for record in robotics_records:\n", + " name = record[\"name\"]\n", + " src = robotics_fd_output_dir / name / \"vision.mp4\"\n", + " assert src.exists(), f\"missing: {src}\"\n", + " preview = make_preview(src)\n", + " print(f\"{name} ({src.stat().st_size // 1024} KB -> {preview.stat().st_size // 1024} KB preview)\")\n", + " display(Video(str(preview), embed=True))\n" + ] + }, + { + "cell_type": "markdown", + "id": "bd2edde3", + "metadata": {}, + "source": [ + "## UMI\n", + "\n", + "This example runs UMI forward dynamics through SGLang autoregressively over all 16-action chunks in `assets/actions/umi.json`. The action file stores the raw UMI 10D action representation, so the setup cell validates the row dimension, writes one action JSON per chunk, and prepares a run plan." + ] + }, + { + "cell_type": "markdown", + "id": "5abea6a3", + "metadata": {}, + "source": [ + "### Create the UMI Autoregressive Forward-Dynamics Plan\n", + "\n", + "The UMI action file is stored as one JSON array with `16 * n` action rows. SGLang receives one request per 16-action chunk. Chunk 0 uses the checked-in UMI conditioning image; later chunks use conditioning images extracted from the previous generated video." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14cab52f", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from pathlib import Path\n", + "\n", + "umi_input_image = \"cookbooks/cosmos3/generator/action/assets/images/umi.png\"\n", + "umi_input_action = \"cookbooks/cosmos3/generator/action/assets/actions/umi.json\"\n", + "umi_prompt = \"mouse arrangement\"\n", + "umi_fps = 20\n", + "umi_action_chunk_size = 16\n", + "umi_raw_action_dim = 10\n", + "\n", + "umi_initial_vision_path = Path(resolve_input(umi_input_image))\n", + "umi_source_action_path = Path(resolve_input(umi_input_action))\n", + "umi_action = json.loads(umi_source_action_path.read_text())\n", + "assert len(umi_action) % umi_action_chunk_size == 0, (\n", + " f\"expected action count to be divisible by {umi_action_chunk_size}, got {len(umi_action)}\"\n", + ")\n", + "assert all(len(row) == umi_raw_action_dim for row in umi_action), \"UMI action rows must be 10D\"\n", + "\n", + "umi_num_chunks = len(umi_action) // umi_action_chunk_size\n", + "COSMOS3_INPUT_DIR.mkdir(parents=True, exist_ok=True)\n", + "umi_records = []\n", + "\n", + "for chunk_idx in range(umi_num_chunks):\n", + " chunk_name = f\"umi_action_cond_chunk_{chunk_idx:02d}\"\n", + " start = chunk_idx * umi_action_chunk_size\n", + " end = start + umi_action_chunk_size\n", + " action_chunk_10d = umi_action[start:end]\n", + " umi_action_path = COSMOS3_INPUT_DIR / f\"umi_action_chunk_{chunk_idx:02d}_10d.json\"\n", + " umi_action_path.write_text(json.dumps(action_chunk_10d, indent=2) + \"\\n\")\n", + "\n", + " if chunk_idx == 0:\n", + " vision_path = umi_initial_vision_path\n", + " else:\n", + " vision_path = COSMOS3_INPUT_DIR / f\"umi_autoregressive_input_chunk_{chunk_idx:02d}.png\"\n", + "\n", + " umi_records.append(\n", + " {\n", + " \"action_chunk_size\": umi_action_chunk_size,\n", + " \"action_path\": str(umi_action_path),\n", + " \"domain_name\": \"umi\",\n", + " \"fps\": umi_fps,\n", + " \"image_size\": 256,\n", + " \"view_point\": \"ego_view\",\n", + " \"model_mode\": \"forward_dynamics\",\n", + " \"name\": chunk_name,\n", + " \"prompt\": umi_prompt,\n", + " \"seed\": chunk_idx,\n", + " \"vision_path\": str(vision_path),\n", + " }\n", + " )\n", + "\n", + "umi_fd_input_path = COSMOS3_INPUT_DIR / \"action_forward_dynamics_umi_custom.jsonl\"\n", + "umi_fd_input_path.write_text(\"\".join(json.dumps(r) + \"\\n\" for r in umi_records))\n", + "umi_fd_output_dir = COSMOS3_OUTPUT_ROOT / \"action_forward_dynamics_umi_custom\"\n", + "\n", + "print(\"UMI chunks:\", umi_num_chunks)\n", + "print(\"wrote UMI spec:\", umi_fd_input_path)\n", + "print(umi_fd_input_path.read_text())" + ] + }, + { + "cell_type": "markdown", + "id": "50abf709", + "metadata": {}, + "source": [ + "### Run UMI Autoregressive Forward-Dynamics Inference\n", + "\n", + "Runs one SGLang video request per UMI action chunk. After each chunk completes, the cell extracts that chunk's last generated frame and uses it as the conditioning image for the next chunk." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d8f37f5", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import mimetypes\n", + "import subprocess\n", + "import time\n", + "from pathlib import Path\n", + "\n", + "import imageio_ffmpeg\n", + "\n", + "try:\n", + " import requests\n", + "except ImportError as exc:\n", + " raise RuntimeError(\"Install requests in this notebook kernel: pip install requests\") from exc\n", + "\n", + "FFMPEG = imageio_ffmpeg.get_ffmpeg_exe()\n", + "\n", + "\n", + "def check_sglang_server_for_umi(timeout_s: int = 600, interval_s: int = 10) -> None:\n", + " deadline = time.time() + timeout_s\n", + " last_error: Exception | None = None\n", + " while time.time() < deadline:\n", + " try:\n", + " response = requests.get(f\"{SGLANG_BASE_URL}/v1/models\", timeout=10)\n", + " response.raise_for_status()\n", + " print(response.json())\n", + " return\n", + " except requests.RequestException as exc:\n", + " last_error = exc\n", + " print(f\"Waiting for SGLang server at {SGLANG_BASE_URL}: {exc}\")\n", + " time.sleep(interval_s)\n", + " raise RuntimeError(\n", + " f\"SGLang server did not become ready at {SGLANG_BASE_URL} within {timeout_s}s. \"\n", + " \"Check `docker logs -f cosmos3-sglang-omni-notebook`.\"\n", + " ) from last_error\n", + "\n", + "\n", + "def submit_umi_forward_dynamics(record: dict, fd_output_dir: Path) -> dict:\n", + " run_dir = fd_output_dir / record[\"name\"]\n", + " run_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " vision_path = Path(record[\"vision_path\"])\n", + " input_width, input_height = Image.open(vision_path).size\n", + " mime_type = mimetypes.guess_type(vision_path.name)[0] or \"application/octet-stream\"\n", + " extra_params = {\n", + " \"action_mode\": \"forward_dynamics\",\n", + " \"domain_name\": record[\"domain_name\"],\n", + " \"action_view_point\": record[\"view_point\"],\n", + " \"action\": json.loads(Path(record[\"action_path\"]).read_text()),\n", + " \"guardrails\": False,\n", + " }\n", + " prompt = str(record.get(\"prompt\") or \"\").strip() or \"A robot manipulates an object.\"\n", + " form = {\n", + " \"prompt\": prompt,\n", + " \"num_frames\": record[\"action_chunk_size\"] + 1,\n", + " \"fps\": record[\"fps\"],\n", + " \"size\": f\"{input_width}x{input_height}\",\n", + " \"num_inference_steps\": 30,\n", + " \"guidance_scale\": 1.0,\n", + " \"flow_shift\": 10.0,\n", + " \"seed\": record[\"seed\"],\n", + " \"extra_params\": json.dumps(extra_params),\n", + " }\n", + "\n", + " with vision_path.open(\"rb\") as image_file:\n", + " response = requests.post(\n", + " f\"{SGLANG_BASE_URL}/v1/videos\",\n", + " data={key: str(value) for key, value in form.items()},\n", + " files={\"input_reference\": (vision_path.name, image_file, mime_type)},\n", + " timeout=120,\n", + " )\n", + " if not response.ok:\n", + " (run_dir / \"error_response.txt\").write_text(response.text)\n", + " print(\"SGLang request failed:\", response.status_code)\n", + " print(response.text)\n", + " print(\"form:\", json.dumps(form, indent=2))\n", + " print(\"extra_params keys:\", sorted(extra_params))\n", + " print(\"action shape:\", [len(extra_params[\"action\"]), len(extra_params[\"action\"][0]) if extra_params[\"action\"] else 0])\n", + " response.raise_for_status()\n", + "\n", + " initial = response.json()\n", + " (run_dir / \"response.json\").write_text(json.dumps(initial, indent=2))\n", + "\n", + " while True:\n", + " response = requests.get(f\"{SGLANG_BASE_URL}/v1/videos/{initial['id']}\", timeout=30)\n", + " response.raise_for_status()\n", + " final = response.json()\n", + " (run_dir / \"final.json\").write_text(json.dumps(final, indent=2))\n", + " print(initial[\"id\"], final.get(\"status\"), f\"{final.get('progress', 0)}%\")\n", + " if final.get(\"status\") == \"completed\":\n", + " break\n", + " if final.get(\"status\") in {\"failed\", \"cancelled\"}:\n", + " raise RuntimeError(json.dumps(final, indent=2))\n", + " time.sleep(2)\n", + "\n", + " response = requests.get(f\"{SGLANG_BASE_URL}/v1/videos/{initial['id']}/content\", timeout=300)\n", + " response.raise_for_status()\n", + " video_path = run_dir / \"vision.mp4\"\n", + " video_path.write_bytes(response.content)\n", + " print(\"saved\", video_path)\n", + " return {\"record\": record, \"initial\": initial, \"final\": final, \"run_dir\": run_dir, \"video_path\": video_path}\n", + "\n", + "\n", + "check_sglang_server_for_umi()\n", + "umi_results = []\n", + "umi_actual_records = []\n", + "current_vision_path = Path(umi_records[0][\"vision_path\"])\n", + "assert current_vision_path.exists(), f\"missing initial conditioning image: {current_vision_path}\"\n", + "\n", + "for chunk_idx, base_record in enumerate(umi_records):\n", + " record = dict(base_record)\n", + " record[\"vision_path\"] = str(current_vision_path)\n", + " umi_records[chunk_idx][\"vision_path\"] = str(current_vision_path)\n", + " umi_actual_records.append(record)\n", + "\n", + " print(f\"\\nSubmitting {record['name']}\")\n", + " print(\"conditioning image:\", current_vision_path)\n", + " result = submit_umi_forward_dynamics(record, umi_fd_output_dir)\n", + " umi_results.append(result)\n", + "\n", + " if chunk_idx + 1 < len(umi_records):\n", + " next_vision_path = COSMOS3_INPUT_DIR / f\"umi_autoregressive_input_chunk_{chunk_idx + 1:02d}.png\"\n", + " subprocess.run(\n", + " [\n", + " FFMPEG,\n", + " \"-y\",\n", + " \"-loglevel\",\n", + " \"error\",\n", + " \"-i\",\n", + " str(result[\"video_path\"]),\n", + " \"-vf\",\n", + " fr\"select=eq(n\\,{record['action_chunk_size']})\",\n", + " \"-frames:v\",\n", + " \"1\",\n", + " str(next_vision_path),\n", + " ],\n", + " check=True,\n", + " )\n", + " assert next_vision_path.exists(), f\"failed to extract next conditioning image: {next_vision_path}\"\n", + " current_vision_path = next_vision_path\n", + "\n", + "umi_fd_input_path.write_text(\"\".join(json.dumps(r) + \"\\n\" for r in umi_actual_records))\n", + "print(\"wrote autoregressive UMI run spec:\", umi_fd_input_path)\n", + "print(\"completed UMI chunks:\", [record[\"name\"] for record in umi_actual_records])" + ] + }, + { + "cell_type": "markdown", + "id": "cc77c77a", + "metadata": {}, + "source": [ + "### Stitch and Visualize UMI Generated Chunks\n", + "\n", + "Each autoregressive chunk video includes its conditioning frame at frame 0. This cell drops that first frame from every chunk, concatenates the generated frames into one rollout video, transcodes a compact preview, and embeds it in the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93e49e34", + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess\n", + "\n", + "import imageio_ffmpeg\n", + "from IPython.display import Video, display\n", + "\n", + "FFMPEG = imageio_ffmpeg.get_ffmpeg_exe()\n", + "umi_video_paths = [umi_fd_output_dir / record[\"name\"] / \"vision.mp4\" for record in umi_records]\n", + "for path in umi_video_paths:\n", + " assert path.exists(), f\"missing UMI chunk video: {path}\"\n", + "\n", + "umi_stitch_dir = umi_fd_output_dir / \"_stitched_segments\"\n", + "umi_stitch_dir.mkdir(parents=True, exist_ok=True)\n", + "segment_paths = []\n", + "for record, src in zip(umi_records, umi_video_paths, strict=True):\n", + " segment = umi_stitch_dir / f\"{record['name']}_generated.mp4\"\n", + " subprocess.run(\n", + " [\n", + " FFMPEG,\n", + " \"-y\",\n", + " \"-loglevel\",\n", + " \"error\",\n", + " \"-i\",\n", + " str(src),\n", + " \"-vf\",\n", + " r\"select=gte(n\\,1),setpts=N/FRAME_RATE/TB\",\n", + " \"-an\",\n", + " \"-r\",\n", + " str(record[\"fps\"]),\n", + " \"-c:v\",\n", + " \"libx264\",\n", + " \"-crf\",\n", + " \"18\",\n", + " \"-preset\",\n", + " \"veryfast\",\n", + " \"-pix_fmt\",\n", + " \"yuv420p\",\n", + " str(segment),\n", + " ],\n", + " check=True,\n", + " )\n", + " segment_paths.append(segment)\n", + "\n", + "concat_file = umi_stitch_dir / \"umi_concat.txt\"\n", + "concat_file.write_text(\"\".join(f\"file '{path.as_posix()}'\\n\" for path in segment_paths))\n", + "umi_stitched_video_path = umi_fd_output_dir / \"umi_action_cond_stitched.mp4\"\n", + "subprocess.run(\n", + " [\n", + " FFMPEG,\n", + " \"-y\",\n", + " \"-loglevel\",\n", + " \"error\",\n", + " \"-f\",\n", + " \"concat\",\n", + " \"-safe\",\n", + " \"0\",\n", + " \"-i\",\n", + " str(concat_file),\n", + " \"-c\",\n", + " \"copy\",\n", + " str(umi_stitched_video_path),\n", + " ],\n", + " check=True,\n", + ")\n", + "\n", + "\n", + "def make_umi_preview(src: Path, crf: int = 28) -> Path:\n", + " preview = src.with_name(f\"{src.stem}_preview.mp4\")\n", + " if not preview.exists() or preview.stat().st_mtime < src.stat().st_mtime:\n", + " subprocess.run(\n", + " [\n", + " FFMPEG,\n", + " \"-y\",\n", + " \"-loglevel\",\n", + " \"error\",\n", + " \"-i\",\n", + " str(src),\n", + " \"-c:v\",\n", + " \"libx264\",\n", + " \"-crf\",\n", + " str(crf),\n", + " \"-preset\",\n", + " \"veryfast\",\n", + " \"-an\",\n", + " \"-pix_fmt\",\n", + " \"yuv420p\",\n", + " str(preview),\n", + " ],\n", + " check=True,\n", + " )\n", + " return preview\n", + "\n", + "umi_preview_path = make_umi_preview(umi_stitched_video_path)\n", + "print(\"stitched UMI video:\", umi_stitched_video_path)\n", + "print(\"expected generated frames:\", len(umi_records) * umi_action_chunk_size)\n", + "print(f\"UMI preview: {umi_preview_path}\")\n", + "display(Video(str(umi_preview_path), embed=True))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "668f11b6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/cookbooks/cosmos3/generator/action/run_id_with_sglang.ipynb b/cookbooks/cosmos3/generator/action/run_id_with_sglang.ipynb new file mode 100644 index 00000000..a56fe885 --- /dev/null +++ b/cookbooks/cosmos3/generator/action/run_id_with_sglang.ipynb @@ -0,0 +1,448 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "license-header", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cosmos3 Nano Action: Inverse Dynamics with SGLang\n", + "\n", + "This notebook runs Cosmos3 Nano **action inverse-dynamics** inference through the SGLang OpenAI-compatible video API:\n", + "\n", + "```text\n", + "POST /v1/videos\n", + "```\n", + "\n", + "Inverse dynamics is the reverse of forward dynamics: given a video, it predicts the ego-motion (action) trajectory that produced it. This notebook builds the same custom input spec as [`run_id_with_cosmos_framework.ipynb`](./run_id_with_cosmos_framework.ipynb), keeps the same input-video preview and predicted-trajectory visualization, and only changes the environment setup plus the inference call.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Start SGLang Server\n", + "\n", + "Start the server in a terminal from the `cosmos` repo root.\n", + "\n", + "```bash\n", + "docker rm -f cosmos3-sglang-omni-notebook 2>/dev/null || true\n", + "\n", + "docker run -d --name cosmos3-sglang-notebook \\\n", + " --runtime nvidia --gpus '\"device=0\"' \\\n", + " -e CUDA_DEVICE_ORDER=PCI_BUS_ID \\\n", + " -v \"~/.cache/huggingface:/root/.cache/huggingface\" \\\n", + " -v \"$PWD:/workspace\" \\\n", + " -p 30000:30000 --ipc=host \\\n", + " lmsysorg/sglang:dev\n", + " sglang serve \\\n", + " --model-path nvidia/Cosmos3-Nano\n", + "\n", + "# Wait until this returns model metadata before running the inference cell.\n", + "curl http://localhost:8001/v1/models\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import os\n", + "\n", + "\n", + "def find_repo_root(start: Path) -> Path:\n", + " for path in [start, *start.parents]:\n", + " if (path / \"README.md\").exists() and (path / \"cookbooks\").exists():\n", + " return path\n", + " return start\n", + "\n", + "\n", + "COSMOS_ROOT = find_repo_root(Path.cwd().resolve())\n", + "COSMOS3_REPO = Path(os.environ.get(\"COSMOS3_REPO\", COSMOS_ROOT / \"packages\" / \"cosmos3\")).resolve()\n", + "COSMOS3_OUTPUT_ROOT = Path(\n", + " os.environ.get(\"COSMOS3_SGLANG_OUTPUT_ROOT\", COSMOS_ROOT / \"outputs\" / \"cosmos3_action_sglang\")\n", + ").resolve()\n", + "COSMOS3_INPUT_DIR = COSMOS3_OUTPUT_ROOT / \"inputs\"\n", + "SGLANG_BASE_URL = os.environ.get(\"COSMOS3_SGLANG_BASE_URL\", \"http://localhost:30000\").rstrip(\"/\")\n", + "\n", + "COSMOS3_OUTPUT_ROOT.mkdir(parents=True, exist_ok=True)\n", + "COSMOS3_INPUT_DIR.mkdir(parents=True, exist_ok=True)\n", + "\n", + "print(\"COSMOS_ROOT:\", COSMOS_ROOT)\n", + "print(\"COSMOS3_REPO:\", COSMOS3_REPO)\n", + "print(\"COSMOS3_INPUT_DIR:\", COSMOS3_INPUT_DIR)\n", + "print(\"COSMOS3_OUTPUT_ROOT:\", COSMOS3_OUTPUT_ROOT)\n", + "print(\"COSMOS3_SGLANG_BASE_URL:\", SGLANG_BASE_URL)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the Inverse-Dynamics Input Spec\n", + "\n", + "Inverse-dynamics inference is driven by a JSONL spec, one line per run. Unlike forward dynamics, each line provides only an input video (`vision_path`) and **no** `action_path` — the action is what the model predicts.\n", + "\n", + "This cell builds that spec from local AV videos, writing it under:\n", + "\n", + "```text\n", + "outputs/cosmos3_action_sglang/inputs/action_inverse_dynamics_av_custom.jsonl\n", + "```\n", + "\n", + "It mirrors the native PyTorch notebook's spec format. The `vision_path` is written as an absolute path, because the SGLang request cell reads the spec records directly.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd4b3ff8", + "metadata": {}, + "outputs": [], + "source": [ + "# `os` and the COSMOS3_* paths come from the configuration cell.\n", + "import json\n", + "\n", + "# Local inputs, relative to the cosmos repo root.\n", + "input_videos = {\n", + " \"av_inverse_0\": \"cookbooks/cosmos3/generator/action/assets/videos/av_0.mp4\",\n", + " \"av_inverse_1\": \"cookbooks/cosmos3/generator/action/assets/videos/av_1.mp4\",\n", + "}\n", + "\n", + "def resolve_input(rel_path: str) -> str:\n", + " path = (COSMOS_ROOT / rel_path).resolve()\n", + " assert path.exists(), f\"missing input: {path}\"\n", + " return str(path)\n", + "\n", + "records = [\n", + " {\n", + " \"action_chunk_size\": 60,\n", + " \"domain_name\": \"av\",\n", + " \"fps\": 10,\n", + " \"image_size\": 480,\n", + " \"view_point\": \"ego_view\",\n", + " \"model_mode\": \"inverse_dynamics\",\n", + " \"name\": name,\n", + " \"prompt\": \"You are an autonomous vehicle planning system.\",\n", + " \"seed\": 0,\n", + " \"vision_path\": resolve_input(video_rel),\n", + " }\n", + " for name, video_rel in input_videos.items()\n", + "]\n", + "\n", + "COSMOS3_INPUT_DIR.mkdir(parents=True, exist_ok=True)\n", + "id_input_path = COSMOS3_INPUT_DIR / \"action_inverse_dynamics_av_custom.jsonl\"\n", + "id_input_path.write_text(\"\".join(json.dumps(r) + \"\\n\" for r in records))\n", + "id_output_dir = COSMOS3_OUTPUT_ROOT / \"action_inverse_dynamics_av_custom\"\n", + "\n", + "# The bash inference cell can only see the environment, so export the paths it needs.\n", + "os.environ[\"COSMOS3_ID_INPUT\"] = str(id_input_path)\n", + "os.environ[\"COSMOS3_ID_OUTPUT\"] = str(id_output_dir)\n", + "\n", + "print(\"wrote spec:\", id_input_path)\n", + "print(\"runs:\", list(input_videos))\n", + "print(id_input_path.read_text())" + ] + }, + { + "cell_type": "markdown", + "id": "0f17af65", + "metadata": {}, + "source": [ + "## Preview the Input Video(s)\n", + "\n", + "Preview each input video before running inference. `Video(..., embed=True)` base64-inlines the file, and these AV clips are several MB each, so we first transcode a small preview (~150 KB) with the ffmpeg binary bundled in `imageio-ffmpeg` (installed by `uv sync`), then embed it. The original input videos are left untouched." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "293b1dfb", + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess\n", + "import imageio_ffmpeg\n", + "from IPython.display import Video, display\n", + "\n", + "FFMPEG = imageio_ffmpeg.get_ffmpeg_exe()\n", + "\n", + "def make_preview(src: Path, dst: Path, crf: int = 28) -> Path:\n", + " \"\"\"Re-encode `src` to a compact, browser-friendly mp4 (cached).\"\"\"\n", + " if not dst.exists():\n", + " subprocess.run(\n", + " [FFMPEG, \"-y\", \"-loglevel\", \"error\", \"-i\", str(src),\n", + " \"-c:v\", \"libx264\", \"-crf\", str(crf),\n", + " \"-preset\", \"veryfast\", \"-an\", \"-pix_fmt\", \"yuv420p\", str(dst)],\n", + " check=True,\n", + " )\n", + " return dst\n", + "\n", + "# `records` comes from the prepare cell; preview each input video.\n", + "for record in records:\n", + " name = record[\"name\"]\n", + " src = Path(record[\"vision_path\"])\n", + " preview = make_preview(src, COSMOS3_INPUT_DIR / f\"{name}_input_preview.mp4\")\n", + " print(f\"{name} ({src.stat().st_size // 1024} KB -> {preview.stat().st_size // 1024} KB preview)\")\n", + " display(Video(str(preview), embed=True))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run Inverse-Dynamics Inference\n", + "\n", + "Runs `Cosmos3-Nano` on every line of the spec through SGLang. Inverse dynamics predicts an action, and this cell writes a PyTorch-compatible result file for each run:\n", + "\n", + "```text\n", + "//sample_outputs.json\n", + "```\n", + "\n", + "The predicted action trajectory is stored under `outputs[0].content[\"action\"]`, matching the native notebook's visualization cell. SGLang also returns `response.json`, `final.json`, and `action.json` for API debugging.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import time\n", + "from pathlib import Path\n", + "\n", + "try:\n", + " import requests\n", + "except ImportError as exc:\n", + " raise RuntimeError(\"Install requests in this notebook kernel: pip install requests\") from exc\n", + "\n", + "\n", + "def check_sglang_server() -> None:\n", + " response = requests.get(f\"{SGLANG_BASE_URL}/v1/models\", timeout=10)\n", + " response.raise_for_status()\n", + " print(response.json())\n", + "\n", + "\n", + "def submit_inverse_dynamics(record: dict) -> dict:\n", + " run_dir = id_output_dir / record[\"name\"]\n", + " run_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " video_path = Path(record[\"vision_path\"])\n", + " extra_params = {\n", + " \"action_mode\": \"inverse_dynamics\",\n", + " \"domain_name\": record[\"domain_name\"],\n", + " \"view_point\": record[\"view_point\"],\n", + " \"raw_action_dim\": 9,\n", + " \"guardrails\": False,\n", + " }\n", + " form = {\n", + " \"prompt\": record[\"prompt\"],\n", + " \"num_frames\": record[\"action_chunk_size\"] + 1,\n", + " \"fps\": record[\"fps\"],\n", + " \"num_inference_steps\": 30,\n", + " \"guidance_scale\": 1.0,\n", + " \"flow_shift\": 10.0,\n", + " \"seed\": record[\"seed\"],\n", + " \"extra_params\": json.dumps(extra_params),\n", + " }\n", + "\n", + " with video_path.open(\"rb\") as video_file:\n", + " response = requests.post(\n", + " f\"{SGLANG_BASE_URL}/v1/videos\",\n", + " data={key: str(value) for key, value in form.items()},\n", + " files={\"input_reference\": (video_path.name, video_file, \"video/mp4\")},\n", + " timeout=120,\n", + " )\n", + " response.raise_for_status()\n", + " initial = response.json()\n", + " (run_dir / \"response.json\").write_text(json.dumps(initial, indent=2))\n", + "\n", + " while True:\n", + " response = requests.get(f\"{SGLANG_BASE_URL}/v1/videos/{initial['id']}\", timeout=30)\n", + " response.raise_for_status()\n", + " final = response.json()\n", + " (run_dir / \"final.json\").write_text(json.dumps(final, indent=2))\n", + " print(initial[\"id\"], final.get(\"status\"), f\"{final.get('progress', 0)}%\")\n", + " if final.get(\"status\") == \"completed\":\n", + " break\n", + " if final.get(\"status\") in {\"failed\", \"cancelled\"}:\n", + " raise RuntimeError(json.dumps(final, indent=2))\n", + " time.sleep(2)\n", + "\n", + " action = final.get(\"action\")\n", + " if not action or \"data\" not in action:\n", + " raise RuntimeError(f\"SGLang response did not include action data: {json.dumps(final, indent=2)}\")\n", + " (run_dir / \"action.json\").write_text(json.dumps(action, indent=2))\n", + "\n", + " sample_outputs = {\"outputs\": [{\"content\": {\"action\": action[\"data\"]}}]}\n", + " (run_dir / \"sample_outputs.json\").write_text(json.dumps(sample_outputs, indent=2))\n", + "\n", + " print(\"saved\", run_dir / \"sample_outputs.json\")\n", + " print(\"action shape:\", action.get(\"shape\"), \"dtype:\", action.get(\"dtype\"))\n", + " return {\"record\": record, \"initial\": initial, \"final\": final, \"run_dir\": run_dir, \"action\": action}\n", + "\n", + "\n", + "check_sglang_server()\n", + "results = []\n", + "for record in records:\n", + " print(f\"\\nSubmitting {record['name']}\")\n", + " results.append(submit_inverse_dynamics(record))\n" + ] + }, + { + "cell_type": "markdown", + "id": "324e6378", + "metadata": {}, + "source": [ + "## Visualize the Predicted Action\n", + "\n", + "Plot the action the model predicted from each input video, as a 3D camera path (with frustums) and a top-down bird's-eye view. The action is read from each run's `sample_outputs.json` and interpreted with the AV pose convention." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7808372", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import json\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.collections import LineCollection\n", + "from mpl_toolkits.mplot3d.art3d import Line3DCollection\n", + "\n", + "# The notebook kernel may differ from the framework venv, so put the repo on the\n", + "# path before importing `cosmos_framework`.\n", + "COSMOS3_FRAMEWORK_PATH = os.environ.get(\"COSMOS3_FRAMEWORK_PATH\")\n", + "if str(COSMOS3_FRAMEWORK_PATH) not in sys.path:\n", + " sys.path.insert(0, str(COSMOS3_FRAMEWORK_PATH))\n", + "from cosmos_framework.data.vfm.action.pose_utils import pose_rel_to_abs\n", + "\n", + "# frustum: apex + image-rectangle corners (camera +Z forward), and their edges\n", + "_FRUSTUM = np.array([[0, 0, 0], [-1, -1, 1], [1, -1, 1], [1, 1, 1], [-1, 1, 1]], float)\n", + "_EDGES = [(0, 1), (0, 2), (0, 3), (0, 4), (1, 2), (2, 3), (3, 4), (4, 1)]\n", + "\n", + "def visualize_pose(poses_abs, *, n_frustums=20, scale_frac=0.03, aspect=16 / 9,\n", + " fov_deg=60.0, vertical_exaggeration=1.0, cmap=\"turbo\",\n", + " title=None, save_path=None, show=True):\n", + " \"\"\"3D camera trajectory (with frustums) + a top-down bird's-eye view.\n", + "\n", + " AV convention: world Y is up, world +Z is the heading. `vertical_exaggeration`\n", + " stretches only the up-axis box (uniform world scaling, so frustums never skew);\n", + " 1.0 = geometrically faithful. The 3D plot reorders world (X, Y, Z) -> (X, Z, Y)\n", + " so Y points up on screen.\n", + " \"\"\"\n", + " poses_abs = np.asarray(poses_abs)\n", + " pos = poses_abs[:, :3, 3] # camera centers [T, 3]\n", + " fwd = poses_abs[:, :3, 2] # heading (+Z) [T, 3]\n", + " T = len(pos)\n", + " colors = plt.get_cmap(cmap)(np.arange(T) / max(T - 1, 1))\n", + " scale = max(np.ptp(pos, axis=0).max() * scale_frac, 1e-3)\n", + " step = max(1, T // max(n_frustums, 1))\n", + " xzy = [0, 2, 1] # world (X,Y,Z) -> plot (X, Z, Y-up)\n", + "\n", + " fig = plt.figure(figsize=(14, 6))\n", + "\n", + " # (1) 3D perspective with frustums\n", + " ax = fig.add_subplot(1, 2, 1, projection=\"3d\")\n", + " path = pos[:, xzy]\n", + " ax.plot(*path.T, color=\"0.6\", lw=1.0, alpha=0.7)\n", + " lines, lcolors, allpts = [], [], [path]\n", + " for i in range(0, T, step):\n", + " cw = ((_FRUSTUM * [aspect, 1, 1] * scale * np.tan(np.radians(fov_deg) / 2))\n", + " @ poses_abs[i, :3, :3].T + poses_abs[i, :3, 3])[:, xzy] # frustum in plot coords\n", + " allpts.append(cw)\n", + " lines += [[cw[a], cw[b]] for a, b in _EDGES]\n", + " lcolors += [colors[i]] * len(_EDGES)\n", + " ax.add_collection3d(Line3DCollection(lines, colors=lcolors, linewidths=1.2))\n", + " ax.scatter(*path[0], color=\"lime\", s=80, edgecolor=\"k\", label=\"first frame\", zorder=5)\n", + " ax.scatter(*path[-1], color=\"red\", s=80, edgecolor=\"k\", label=\"last frame\", zorder=5)\n", + " rng = np.clip(np.ptp(np.concatenate(allpts), axis=0), 1e-9, None)\n", + " ax.set_box_aspect((rng[0], rng[1], rng[2] * vertical_exaggeration))\n", + " ax.set_xlabel(\"X (m)\", labelpad=12); ax.set_ylabel(\"Z forward (m)\", labelpad=12)\n", + " ax.set_zlabel(\"Y up (m)\", labelpad=10); ax.set_zticks([])\n", + " ax.set_title(title or f\"Camera trajectory + frustums ({T} frames)\")\n", + " ax.legend(loc=\"upper left\"); ax.view_init(elev=22, azim=-70)\n", + "\n", + " # (2) top-down bird's-eye view (X-Z ground plane)\n", + " ax2 = fig.add_subplot(1, 2, 2)\n", + " seg = np.stack([pos[:-1, [0, 2]], pos[1:, [0, 2]]], axis=1)\n", + " lc = LineCollection(seg, cmap=cmap, norm=plt.Normalize(0, T - 1), linewidth=2.5)\n", + " lc.set_array(np.arange(T - 1)); ax2.add_collection(lc)\n", + " ax2.quiver(pos[::step, 0], pos[::step, 2], fwd[::step, 0], fwd[::step, 2],\n", + " color=colors[::step], angles=\"xy\", width=0.005, scale=22, zorder=3)\n", + " ax2.scatter(*pos[0, [0, 2]], color=\"lime\", s=80, edgecolor=\"k\", label=\"first frame\", zorder=5)\n", + " ax2.scatter(*pos[-1, [0, 2]], color=\"red\", s=80, edgecolor=\"k\", label=\"last frame\", zorder=5)\n", + " ax2.set_xlabel(\"X (m)\"); ax2.set_ylabel(\"Z forward (m)\")\n", + " ax2.set_title(\"Top-down (bird's-eye view)\")\n", + " ax2.set_aspect(\"equal\", adjustable=\"datalim\"); ax2.autoscale_view(); ax2.legend()\n", + " fig.colorbar(lc, ax=ax2, label=\"frame index\")\n", + "\n", + " plt.tight_layout(w_pad=6)\n", + " if save_path:\n", + " fig.savefig(save_path, dpi=120, bbox_inches=\"tight\"); print(\"saved\", save_path)\n", + " if show:\n", + " plt.show()\n", + "\n", + "# `records` and `id_output_dir` come from the prepare cell; read each run's\n", + "# predicted action from its sample_outputs.json.\n", + "for record in records:\n", + " name = record[\"name\"]\n", + " outputs = json.loads((id_output_dir / name / \"sample_outputs.json\").read_text())\n", + " poses_rel = np.array(outputs[\"outputs\"][0][\"content\"][\"action\"][0]) # [T-1, 9] = [translation(3), rot6d(6)]\n", + "\n", + " # AV action convention (see cosmos_framework/data/vfm/action/av_dataset.py):\n", + " # rot6d rotation, backward_framewise, translation_scale = 1.35.\n", + " poses_abs = pose_rel_to_abs(\n", + " poses_rel,\n", + " rotation_format=\"rot6d\",\n", + " pose_convention=\"backward_framewise\",\n", + " translation_scale=1.35,\n", + " ) # [T, 4, 4] camera-to-world\n", + " print(name, poses_rel.shape, poses_abs.shape)\n", + " visualize_pose(poses_abs, title=f\"{name}: predicted camera trajectory + frustums ({len(poses_abs)} frames)\", show=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ed5e28f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/cookbooks/cosmos3/generator/action/run_policy_with_sglang.ipynb b/cookbooks/cosmos3/generator/action/run_policy_with_sglang.ipynb new file mode 100644 index 00000000..4e15fb5e --- /dev/null +++ b/cookbooks/cosmos3/generator/action/run_policy_with_sglang.ipynb @@ -0,0 +1,523 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "license-header", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "policy-title", + "metadata": {}, + "source": [ + "# Cosmos3 Nano Action: Policy with SGLang\n", + "\n", + "## Prerequisites\n", + "\n", + "Generator requires the Guardrail. Request access to the gated [nvidia/Cosmos-1.0-Guardrail](https://huggingface.co/nvidia/Cosmos-1.0-Guardrail) HF repository before running guarded examples. This notebook disables guardrails for the policy request with `guardrails: false` in `extra_params`.\n", + "\n", + "## Overview\n", + "\n", + "This notebook runs Cosmos3 Nano **action policy** inference through SGLang using the checked-in DROID LeRobot sample under `assets/droid_lerobot_example`.\n", + "\n", + "It sends `POST /v1/videos` requests with a first frame and instruction, then retrieves a rollout video plus top-level `action` metadata." + ] + }, + { + "cell_type": "markdown", + "id": "policy-server-md", + "metadata": {}, + "source": [ + "## Start SGLang Policy Server\n", + "\n", + "Start the server in a terminal from the `cosmos` repo root.\n", + "\n", + "```bash\n", + "docker rm -f cosmos3-vllm-omni-policy-notebook 2>/dev/null || true\n", + "\n", + "docker run -d --name cosmos3-vllm-omni-policy-notebook \\\n", + " --runtime nvidia --gpus '\"device=0\"' \\\n", + " -e CUDA_DEVICE_ORDER=PCI_BUS_ID \\\n", + " -e PYTHONPATH=/workspace/cosmos-framework \\\n", + " -v \"~/.cache/huggingface:/root/.cache/huggingface\" \\\n", + " -v \"$PWD:/workspace\" \\\n", + " -p 30000:30000 --ipc=host \\\n", + " lmsysorg/sglang:dev \\\n", + " sglang serve \\\n", + " --model-path nvidia/Cosmos3-Nano-Policy-DROID \\\n", + "\n", + "# Wait until this returns model metadata before running the inference cells.\n", + "curl http://localhost:30000/v1/models\n", + "```\n", + "\n", + "To inspect startup logs:\n", + "\n", + "```bash\n", + "docker logs -f cosmos3-vllm-omni-policy-notebook\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "policy-vars-code", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "COSMOS_ROOT: /home/kediw/cosmos\n", + "DROID_ASSET_ROOT: /home/kediw/cosmos/cookbooks/cosmos3/generator/action/assets/droid_lerobot_example\n", + "COSMOS3_INPUT_DIR: /home/kediw/cosmos/outputs/cosmos3_action_sglang/inputs\n", + "COSMOS3_POLICY_OUTPUT_DIR: /home/kediw/cosmos/outputs/cosmos3_action_sglang/action_policy_droid\n", + "COSMOS3_SGLANG_BASE_URL: http://localhost:30000\n", + "COSMOS3_SGLANG_MODEL: nvidia/Cosmos3-Nano-Policy-DROID\n" + ] + } + ], + "source": [ + "from pathlib import Path\n", + "import os\n", + "\n", + "\n", + "def find_repo_root(start: Path) -> Path:\n", + " for path in [start, *start.parents]:\n", + " if (path / \"README.md\").exists() and (path / \"cookbooks\").exists():\n", + " return path\n", + " return start\n", + "COSMOS_ROOT = find_repo_root(Path.cwd().resolve())\n", + "COSMOS3_OUTPUT_ROOT = Path(\n", + " os.environ.get(\"COSMOS3_SGLANG_OUTPUT_ROOT\", COSMOS_ROOT / \"outputs\" / \"cosmos3_action_sglang\")\n", + ").resolve()\n", + "COSMOS3_INPUT_DIR = COSMOS3_OUTPUT_ROOT / \"inputs\"\n", + "COSMOS3_POLICY_OUTPUT_DIR = COSMOS3_OUTPUT_ROOT / \"action_policy_droid\"\n", + "COSMOS3_ACTION_ROOT = COSMOS_ROOT / \"cookbooks\" / \"cosmos3\" / \"generator\" / \"action\"\n", + "DROID_ASSET_ROOT = COSMOS3_ACTION_ROOT / \"assets\" / \"droid_lerobot_example\"\n", + "SGLANG_BASE_URL = os.environ.get(\"COSMOS3_SGLANG_BASE_URL\", \"http://localhost:30000\").rstrip(\"/\")\n", + "SGLANG_MODEL = os.environ.get(\"COSMOS3_SGLANG_MODEL\", \"nvidia/Cosmos3-Nano-Policy-DROID\")\n", + "\n", + "COSMOS3_INPUT_DIR.mkdir(parents=True, exist_ok=True)\n", + "COSMOS3_POLICY_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n", + "\n", + "print(\"COSMOS_ROOT:\", COSMOS_ROOT)\n", + "print(\"DROID_ASSET_ROOT:\", DROID_ASSET_ROOT)\n", + "print(\"COSMOS3_INPUT_DIR:\", COSMOS3_INPUT_DIR)\n", + "print(\"COSMOS3_POLICY_OUTPUT_DIR:\", COSMOS3_POLICY_OUTPUT_DIR)\n", + "print(\"COSMOS3_SGLANG_BASE_URL:\", SGLANG_BASE_URL)\n", + "print(\"COSMOS3_SGLANG_MODEL:\", SGLANG_MODEL)" + ] + }, + { + "cell_type": "markdown", + "id": "policy-input-md", + "metadata": {}, + "source": [ + "## Prepare a DROID Policy Input\n", + "\n", + "This cell extracts the first frame from each checked-in DROID camera video and creates a 640x540 multiview conditioning image for the video API." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "policy-input-code", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "policy prompt: Pick up the object and place it in the target container.\n", + "video API conditioning image: /home/kediw/cosmos/outputs/cosmos3_action_sglang/inputs/droid_policy_first_frame.png (640, 540)\n" + ] + }, + { + "data": { + "image/jpeg": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import subprocess\n", + "\n", + "import numpy as np\n", + "from PIL import Image, ImageOps\n", + "from IPython.display import display\n", + "\n", + "try:\n", + " import imageio_ffmpeg\n", + "except ImportError as exc:\n", + " raise RuntimeError(\"Install imageio-ffmpeg in this notebook kernel: pip install imageio-ffmpeg\") from exc\n", + "\n", + "FFMPEG = imageio_ffmpeg.get_ffmpeg_exe()\n", + "CAMERA_VIDEO_PATHS = {\n", + " \"observation/wrist_image_left\": DROID_ASSET_ROOT / \"videos\" / \"observation.image.wrist_image_left\" / \"chunk-000\" / \"file-000.mp4\",\n", + " \"observation/exterior_image_1_left\": DROID_ASSET_ROOT / \"videos\" / \"observation.image.exterior_image_1_left\" / \"chunk-000\" / \"file-000.mp4\",\n", + " \"observation/exterior_image_2_left\": DROID_ASSET_ROOT / \"videos\" / \"observation.image.exterior_image_2_left\" / \"chunk-000\" / \"file-000.mp4\",\n", + "}\n", + "for key, video_path in CAMERA_VIDEO_PATHS.items():\n", + " assert video_path.exists(), f\"missing {key}: {video_path}\"\n", + "\n", + "\n", + "def extract_first_frame(video_path: Path, out_path: Path) -> Path:\n", + " if not out_path.exists() or out_path.stat().st_mtime < video_path.stat().st_mtime:\n", + " subprocess.run(\n", + " [FFMPEG, \"-y\", \"-loglevel\", \"error\", \"-i\", str(video_path), \"-frames:v\", \"1\", str(out_path)],\n", + " check=True,\n", + " )\n", + " return out_path\n", + "\n", + "\n", + "frame_paths = {\n", + " key: extract_first_frame(video_path, COSMOS3_INPUT_DIR / f\"policy_{key.split('/')[-1]}.png\")\n", + " for key, video_path in CAMERA_VIDEO_PATHS.items()\n", + "}\n", + "frames = {key: Image.open(path).convert(\"RGB\") for key, path in frame_paths.items()}\n", + "\n", + "# DROID policy uses a concatenated multi-view frame for the /v1/videos path.\n", + "target_w, target_h = 640, 540\n", + "top_h = target_h // 2\n", + "bottom_h = target_h - top_h\n", + "half_w = target_w // 2\n", + "wrist = ImageOps.fit(frames[\"observation/wrist_image_left\"], (target_w, top_h), method=Image.Resampling.BICUBIC)\n", + "left = ImageOps.fit(frames[\"observation/exterior_image_1_left\"], (half_w, bottom_h), method=Image.Resampling.BICUBIC)\n", + "right = ImageOps.fit(frames[\"observation/exterior_image_2_left\"], (half_w, bottom_h), method=Image.Resampling.BICUBIC)\n", + "policy_image = Image.new(\"RGB\", (target_w, target_h))\n", + "policy_image.paste(wrist, (0, 0))\n", + "policy_image.paste(left, (0, top_h))\n", + "policy_image.paste(right, (half_w, top_h))\n", + "policy_image_path = COSMOS3_INPUT_DIR / \"droid_policy_first_frame.png\"\n", + "policy_image.save(policy_image_path)\n", + "\n", + "policy_prompt = os.environ.get(\n", + " \"COSMOS3_POLICY_PROMPT\",\n", + " \"Pick up the object and place it in the target container.\",\n", + ")\n", + "print(\"policy prompt:\", policy_prompt)\n", + "print(\"video API conditioning image:\", policy_image_path, policy_image.size)\n", + "display(policy_image)" + ] + }, + { + "cell_type": "markdown", + "id": "policy-video-md", + "metadata": {}, + "source": [ + "## Run Policy Inference Through `/v1/videos`\n", + "\n", + "This path behaves like the forward/inverse vLLM action notebooks: it sends a multipart request to the OpenAI-compatible video API, polls the async job, writes the generated rollout video, and saves the predicted action from the response metadata." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "policy-video-code", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'object': 'list', 'data': [{'id': '/sgl-workspace/hf_model', 'object': 'model', 'created': 1782409969, 'owned_by': 'sglang', 'root': '/sgl-workspace/hf_model', 'parent': None, 'max_model_len': None, 'num_gpus': 2, 'task_type': 'TI2V', 'dit_precision': 'bf16', 'vae_precision': 'bf16', 'pipeline_name': 'Cosmos3OmniDiffusersPipeline', 'pipeline_class': 'Cosmos3Pipeline'}]}\n", + "92560e33-6bdb-411d-a49e-bf3a0516a375 queued 0%\n", + "92560e33-6bdb-411d-a49e-bf3a0516a375 queued 0%\n", + "92560e33-6bdb-411d-a49e-bf3a0516a375 queued 0%\n", + "92560e33-6bdb-411d-a49e-bf3a0516a375 queued 0%\n", + "92560e33-6bdb-411d-a49e-bf3a0516a375 queued 0%\n", + "92560e33-6bdb-411d-a49e-bf3a0516a375 queued 0%\n", + "92560e33-6bdb-411d-a49e-bf3a0516a375 completed 100%\n", + "saved /home/kediw/cosmos/outputs/cosmos3_action_sglang/action_policy_droid/video_api/policy_rollout.mp4\n", + "saved /home/kediw/cosmos/outputs/cosmos3_action_sglang/action_policy_droid/video_api/action.json\n", + "action shape: [1, 16, 10] dtype: float32 domain_id: None\n" + ] + } + ], + "source": [ + "import json\n", + "import time\n", + "from pathlib import Path\n", + "\n", + "try:\n", + " import requests\n", + "except ImportError as exc:\n", + " raise RuntimeError(\"Install requests in this notebook kernel: pip install requests\") from exc\n", + "\n", + "\n", + "def check_sglang_server(timeout_s: int = 600, interval_s: int = 10) -> None:\n", + " deadline = time.time() + timeout_s\n", + " last_error: Exception | None = None\n", + " while time.time() < deadline:\n", + " try:\n", + " response = requests.get(f\"{SGLANG_BASE_URL}/v1/models\", timeout=10)\n", + " response.raise_for_status()\n", + " print(response.json())\n", + " return\n", + " except requests.RequestException as exc:\n", + " last_error = exc\n", + " print(f\"Waiting for SGLang server at {SGLANG_BASE_URL}: {exc}\")\n", + " time.sleep(interval_s)\n", + " raise RuntimeError(\n", + " f\"SGLang server did not become ready at {SGLANG_BASE_URL} within {timeout_s}s. \"\n", + " \"Check `docker logs -f cosmos3-sglang-omni-policy-notebook`.\"\n", + " ) from last_error\n", + "\n", + "\n", + "ACTION_VIDEO_RES_SIZE_INFO = {\n", + " \"480\": {\n", + " \"1,1\": (640, 640),\n", + " \"4,3\": (736, 544),\n", + " \"3,4\": (544, 736),\n", + " \"16,9\": (832, 480),\n", + " \"9,16\": (480, 832),\n", + " }\n", + "}\n", + "\n", + "\n", + "def closest_action_size(height: int, width: int, resolution: str = \"480\") -> tuple[int, int]:\n", + " input_ratio = height / width\n", + " candidates = ACTION_VIDEO_RES_SIZE_INFO[resolution].values()\n", + " return min(candidates, key=lambda size: abs(input_ratio - size[1] / size[0]))\n", + "\n", + "\n", + "def submit_policy_video() -> dict:\n", + " run_dir = COSMOS3_POLICY_OUTPUT_DIR / \"video_api\"\n", + " run_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " input_width, input_height = Image.open(policy_image_path).size\n", + " target_width, target_height = closest_action_size(input_height, input_width)\n", + " extra_params = {\n", + " \"action_mode\": \"policy\",\n", + " \"domain_name\": \"droid_lerobot\",\n", + " \"raw_action_dim\": 10,\n", + " \"action_view_point\": \"concat_view\",\n", + " \"guardrails\": False,\n", + " }\n", + " form = {\n", + " \"prompt\": policy_prompt,\n", + " \"num_frames\": 17,\n", + " \"fps\": 15,\n", + " \"size\": f\"{target_width}x{target_height}\",\n", + " \"num_inference_steps\": 30,\n", + " \"guidance_scale\": 1.0,\n", + " \"flow_shift\": 5.0,\n", + " \"seed\": 0,\n", + " \"extra_params\": json.dumps(extra_params),\n", + " }\n", + "\n", + " with policy_image_path.open(\"rb\") as image_file:\n", + " response = requests.post(\n", + " f\"{SGLANG_BASE_URL}/v1/videos\",\n", + " data={key: str(value) for key, value in form.items()},\n", + " files={\"input_reference\": (policy_image_path.name, image_file, \"image/png\")},\n", + " timeout=120,\n", + " )\n", + " if not response.ok:\n", + " (run_dir / \"error_response.txt\").write_text(response.text)\n", + " print(\"SGLang request failed:\", response.status_code)\n", + " print(response.text)\n", + " print(\"form:\", json.dumps(form, indent=2))\n", + " response.raise_for_status()\n", + "\n", + " initial = response.json()\n", + " (run_dir / \"response.json\").write_text(json.dumps(initial, indent=2))\n", + "\n", + " while True:\n", + " response = requests.get(f\"{SGLANG_BASE_URL}/v1/videos/{initial['id']}\", timeout=30)\n", + " response.raise_for_status()\n", + " final = response.json()\n", + " (run_dir / \"final.json\").write_text(json.dumps(final, indent=2))\n", + " print(initial[\"id\"], final.get(\"status\"), f\"{final.get('progress', 0)}%\")\n", + " if final.get(\"status\") == \"completed\":\n", + " break\n", + " if final.get(\"status\") in {\"failed\", \"cancelled\"}:\n", + " raise RuntimeError(json.dumps(final, indent=2))\n", + " time.sleep(2)\n", + "\n", + " action = final.get(\"action\")\n", + " if not action or \"data\" not in action:\n", + " raise RuntimeError(f\"SGLang response did not include action data: {json.dumps(final, indent=2)}\")\n", + " (run_dir / \"action.json\").write_text(json.dumps(action, indent=2))\n", + " sample_outputs = {\"outputs\": [{\"content\": {\"action\": action[\"data\"]}}]}\n", + " (run_dir / \"sample_outputs.json\").write_text(json.dumps(sample_outputs, indent=2))\n", + "\n", + " content_response = requests.get(f\"{SGLANG_BASE_URL}/v1/videos/{initial['id']}/content\", timeout=300)\n", + " content_response.raise_for_status()\n", + " video_path = run_dir / \"policy_rollout.mp4\"\n", + " if content_response.content:\n", + " video_path.write_bytes(content_response.content)\n", + " print(\"saved\", video_path)\n", + " else:\n", + " video_path = None\n", + " print(\"video content endpoint returned an empty body\")\n", + "\n", + " print(\"saved\", run_dir / \"action.json\")\n", + " print(\"action shape:\", action.get(\"shape\"), \"dtype:\", action.get(\"dtype\"), \"domain_id:\", action.get(\"domain_id\"))\n", + " return {\"initial\": initial, \"final\": final, \"run_dir\": run_dir, \"video_path\": video_path, \"action\": action}\n", + "\n", + "\n", + "check_sglang_server()\n", + "policy_video_result = submit_policy_video()" + ] + }, + { + "cell_type": "markdown", + "id": "policy-preview-md", + "metadata": {}, + "source": [ + "## Inspect Video API Outputs\n", + "\n", + "Preview the rollout video if the server returned one, and print the first few predicted action rows." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "policy-preview-code", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "action array: (1, 16, 10) float32\n", + "[[[-0.06542969 0.03515625 0.05322266 1. 0.02246094\n", + " -0.02294922 -0.02429199 0.9921875 -0.03417969 -0.16210938]\n", + " [-0.09179688 -0.00585938 0.06640625 0.984375 0.07275391\n", + " -0.10546875 -0.06738281 0.984375 -0.00512695 -0.20117188]\n", + " [-0.09960938 -0.09375 0.06298828 0.93359375 0.12890625\n", + " -0.22460938 -0.12451172 0.97265625 0.04980469 -0.25585938]\n", + " [-0.13085938 -0.19140625 0.05712891 0.859375 0.19335938\n", + " -0.34375 -0.19140625 0.92578125 0.10253906 -0.28710938]\n", + " [-0.18164062 -0.2890625 0.10253906 0.77734375 0.25390625\n", + " -0.38867188 -0.24023438 0.87109375 0.12402344 -0.42773438]\n", + " [-0.23730469 -0.375 0.13769531 0.73828125 0.28515625\n", + " -0.4140625 -0.28515625 0.84375 0.1640625 -0.41601562]\n", + " [-0.29101562 -0.43164062 0.20703125 0.73046875 0.30859375\n", + " -0.40820312 -0.296875 0.8359375 0.17089844 -0.38671875]\n", + " [-0.32421875 -0.453125 0.296875 0.75 0.2890625\n", + " -0.39648438 -0.27734375 0.8515625 0.15234375 -0.4140625 ]\n", + " [-0.3125 -0.46484375 0.39257812 0.765625 0.24609375\n", + " -0.40039062 -0.23828125 0.89453125 0.13476562 -0.46289062]\n", + " [-0.2734375 -0.4921875 0.515625 0.80859375 0.19824219\n", + " -0.40039062 -0.19433594 0.9375 0.09667969 -0.47070312]\n", + " [-0.19140625 -0.4609375 0.6796875 0.81640625 0.15234375\n", + " -0.41015625 -0.13867188 0.9609375 0.04614258 -0.4921875 ]\n", + " [-0.08203125 -0.3984375 0.8125 0.84375 0.10742188\n", + " -0.41796875 -0.09277344 0.98046875 -0.00878906 -0.5078125 ]\n", + " [ 0.00878906 -0.31640625 0.86328125 0.875 0.06884766\n", + " -0.39648438 -0.05737305 0.9921875 -0.01281738 -0.51953125]\n", + " [ 0.05639648 -0.22851562 0.84765625 0.90625 0.01074219\n", + " -0.34765625 -0.00756836 1.0078125 -0.00390625 -0.51953125]\n", + " [ 0.06445312 -0.16015625 0.796875 0.9453125 -0.015625\n", + " -0.28515625 0.01367188 1.0078125 0.03417969 -0.52734375]\n", + " [ 0.07568359 -0.12109375 0.71875 0.9375 -0.04467773\n", + " -0.23632812 0.05175781 1. 0.05786133 -0.546875 ]]]\n", + "preview: /home/kediw/cosmos/outputs/cosmos3_action_sglang/action_policy_droid/video_api/policy_rollout_preview.mp4\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import subprocess\n", + "\n", + "import imageio_ffmpeg\n", + "from IPython.display import Video, display\n", + "\n", + "FFMPEG = imageio_ffmpeg.get_ffmpeg_exe()\n", + "\n", + "\n", + "def make_preview(src: Path, crf: int = 28) -> Path:\n", + " preview = src.with_name(f\"{src.stem}_preview.mp4\")\n", + " if not preview.exists() or preview.stat().st_mtime < src.stat().st_mtime:\n", + " subprocess.run(\n", + " [\n", + " FFMPEG,\n", + " \"-y\",\n", + " \"-loglevel\",\n", + " \"error\",\n", + " \"-i\",\n", + " str(src),\n", + " \"-c:v\",\n", + " \"libx264\",\n", + " \"-crf\",\n", + " str(crf),\n", + " \"-preset\",\n", + " \"veryfast\",\n", + " \"-an\",\n", + " \"-pix_fmt\",\n", + " \"yuv420p\",\n", + " str(preview),\n", + " ],\n", + " check=True,\n", + " )\n", + " return preview\n", + "\n", + "\n", + "action = policy_video_result[\"action\"]\n", + "action_array = np.asarray(action[\"data\"], dtype=np.float32)\n", + "print(\"action array:\", action_array.shape, action_array.dtype)\n", + "print(action_array[: min(5, len(action_array))])\n", + "\n", + "video_path = policy_video_result.get(\"video_path\")\n", + "if video_path is not None:\n", + " preview = make_preview(video_path)\n", + " print(f\"preview: {preview}\")\n", + " display(Video(str(preview), embed=True))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b987850", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/cookbooks/cosmos3/generator/audiovisual/run_with_sglang.ipynb b/cookbooks/cosmos3/generator/audiovisual/run_with_sglang.ipynb new file mode 100644 index 00000000..0fa75fb1 --- /dev/null +++ b/cookbooks/cosmos3/generator/audiovisual/run_with_sglang.ipynb @@ -0,0 +1,1130 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "license-header", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "d88fe9a8", + "metadata": {}, + "source": [ + "# Cosmos3 Generator Audiovisual with SGLang\n", + "\n", + "This notebook calls already-running SGLang Cosmos3 servers with direct `curl` requests from Python.\n", + "\n", + "The examples are split into Cosmos3-Nano and Cosmos3-Super sections. Each section is self-contained, so you can run just one. Each section targets the matching model endpoint.\n" + ] + }, + { + "cell_type": "markdown", + "id": "49df4e61", + "metadata": {}, + "source": [ + "## 1. Prerequisites\n", + "\n", + "Use a running SGLang server and set endpoint environment variables before the setup cell if you are not using the local default. Text-to-image uses `/v1/images/generations`; video modes use `/v1/videos`.\n", + "\n", + "```bash\n", + "export COSMOS3_SGLANG_BASE_URL=http://localhost:30000\n", + "export COSMOS3_SGLANG_NANO_BASE_URL=http://localhost:30000\n", + "export COSMOS3_SGLANG_SUPER_BASE_URL=http://localhost:30000\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "26776c50", + "metadata": {}, + "source": [ + "## 2. Start the Server\n", + "\n", + "Run the SGLang server before running the request cells. Use the Docker image for every modality on this page. Mount any directory that contains local media or action files you want the server to read.\n", + "\n", + "### Docker Image: Cosmos3-Nano\n", + "\n", + "```bash\n", + "docker run --runtime nvidia --gpus all \\\n", + " -v ~/.cache/huggingface:/root/.cache/huggingface \\\n", + " -v \"$(pwd):/workspace\" \\\n", + " -p 30000:30000 \\\n", + " --ipc=host \\\n", + " lmsysorg/sglang:dev \\\n", + " sglang serve \\\n", + " --model-path nvidia/Cosmos3-Nano\n", + "```\n", + "\n", + "### Docker Image: Cosmos3-Super\n", + "\n", + "`Cosmos3-Super` is the larger 64B model, so it usually needs more GPU memory than `Cosmos3-Nano`. `--tp-size` splits model weights across multiple GPUs and reduces per-GPU memory use. Use `--performance-mode memory` preset to preserve memory.\n", + "\n", + "For example, on four GPUs:\n", + "\n", + "```bash\n", + "docker run --runtime nvidia --gpus all \\\n", + " -v ~/.cache/huggingface:/root/.cache/huggingface \\\n", + " -v \"$(pwd):/workspace\" \\\n", + " -p 30000:30000 \\\n", + " --ipc=host \\\n", + " lmsysorg/sglang:dev \\\n", + " sglang serve \\\n", + " --model-path nvidia/Cosmos3-Super \\\n", + " --num-gpus 4 \\\n", + " --performance-mode memory\n", + "```\n", + "\n", + "### CFG Parallel\n", + "\n", + "Use `--cfg-parallel-size 2` to run the positive and negative CFG branches in parallel on two GPUs:\n", + "\n", + "```bash\n", + "sglang serve \n", + " --model-path nvidia/Cosmos3-Nano \\\n", + " --num-gpus 2 \\\n", + " --enable-cfg-parallel \\\n", + " --cfg-parallel-size 2\n", + "```\n", + "\n", + "For Cosmos3, set CFG strength with the request-level `guidance_scale` field. Do not use `true_cfg_scale` for CFG Parallel with these Cosmos3 examples.\n" + ] + }, + { + "cell_type": "markdown", + "id": "4412f2f9", + "metadata": {}, + "source": [ + "## 3. Configure Paths and Endpoints\n", + "\n", + "This setup cell only configures repo/output paths and vLLM endpoint settings.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23f04a90", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import os\n", + "\n", + "\n", + "def find_repo_root(start: Path) -> Path:\n", + " for path in [start, *start.parents]:\n", + " if (path / \"README.md\").exists() and (path / \"cookbooks\").exists():\n", + " return path\n", + " return start\n", + "\n", + "\n", + "COSMOS_ROOT = find_repo_root(Path.cwd().resolve())\n", + "COSMOS3_AUDIOVISUAL_ROOT = COSMOS_ROOT / \"cookbooks\" / \"cosmos3\" / \"generator\" / \"audiovisual\"\n", + "COSMOS3_AUDIOVISUAL_OUTPUT_ROOT = Path(\n", + " os.environ.get(\"COSMOS3_AUDIOVISUAL_OUTPUT_ROOT\", COSMOS3_AUDIOVISUAL_ROOT / \"outputs\" / \"notebooks\")\n", + ").resolve()\n", + "DEFAULT_SGLANG_BASE_URL = os.environ.get(\"COSMOS3_SGLANG_BASE_URL\", \"http://localhost:30000\")\n", + "SGLANG_ENDPOINTS = {\n", + " \"Cosmos3-Nano\": os.environ.get(\"COSMOS3_SGLANG_NANO_BASE_URL\", DEFAULT_SGLANG_BASE_URL),\n", + " \"Cosmos3-Super\": os.environ.get(\"COSMOS3_SGLANG_SUPER_BASE_URL\", DEFAULT_SGLANG_BASE_URL),\n", + "}\n", + "\n", + "os.environ[\"COSMOS3_AUDIOVISUAL_OUTPUT_ROOT\"] = str(COSMOS3_AUDIOVISUAL_OUTPUT_ROOT)\n", + "os.environ.setdefault(\"COSMOS3_SGLANG_API_KEY\", \"\")\n", + "\n", + "print(\"COSMOS_ROOT:\", COSMOS_ROOT)\n", + "print(\"COSMOS3_AUDIOVISUAL_OUTPUT_ROOT:\", COSMOS3_AUDIOVISUAL_OUTPUT_ROOT)\n", + "for model, endpoint in SGLANG_ENDPOINTS.items():\n", + " print(f\"{model} endpoint: {endpoint}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "73369e7f", + "metadata": {}, + "source": [ + "## 4. Verify Endpoint Configuration\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c50e183", + "metadata": {}, + "outputs": [], + "source": [ + "from urllib.parse import urlparse\n", + "\n", + "for model, base_url in SGLANG_ENDPOINTS.items():\n", + " api_root = base_url.rstrip(\"/\")\n", + " if not api_root.endswith(\"/v1\"):\n", + " api_root = f\"{api_root}/v1\"\n", + " parsed = urlparse(api_root)\n", + " print(model)\n", + " print(\" api root:\", api_root)\n", + " print(\" images generations:\", f\"{api_root}/images/generations\")\n", + " print(\" videos async:\", f\"{api_root}/videos\")\n", + " print(\" scheme:\", parsed.scheme)\n", + " print(\" host:\", parsed.netloc)\n" + ] + }, + { + "cell_type": "markdown", + "id": "c1d161a6", + "metadata": {}, + "source": [ + "## 5. Preview Available Inputs\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "973ea472", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import json\n", + "from IPython.display import Image, display\n", + "\n", + "assets_dir = COSMOS3_AUDIOVISUAL_ROOT / \"assets\"\n", + "for prompt_dir in sorted((assets_dir / \"prompts\").iterdir()):\n", + " if not prompt_dir.is_dir():\n", + " continue\n", + " print(f\"{prompt_dir.relative_to(assets_dir)}:\")\n", + " for prompt_path in sorted(prompt_dir.glob(\"*.json\")):\n", + " data = json.loads(prompt_path.read_text())\n", + " caption = (\n", + " data.get(\"temporal_caption\")\n", + " or data.get(\"comprehensive_t2i_caption\")\n", + " or data.get(\"extra\", {}).get(\"prompt\", \"\")\n", + " )\n", + " print(f\" {prompt_path.name}: {caption[:180]}{'...' if len(caption) > 180 else ''}\")\n", + " print()\n", + "\n", + "for image_dir in sorted((assets_dir / \"images\").iterdir()):\n", + " if not image_dir.is_dir():\n", + " continue\n", + " print(f\"{image_dir.relative_to(assets_dir)}:\")\n", + " for image_path in sorted(image_dir.iterdir()):\n", + " if image_path.suffix.lower() in {\".jpg\", \".jpeg\", \".png\", \".webp\", \".bmp\"}:\n", + " print(f\" {image_path.name}\")\n", + " display(Image(filename=str(image_path), width=420))\n" + ] + }, + { + "cell_type": "markdown", + "id": "cfa34351", + "metadata": {}, + "source": [ + "## 6. Define Asset Sets, Payload Helpers, Request Helpers, and Viewer Helpers\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af5dd1c8", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "from pathlib import Path\n", + "from IPython.display import Image, display\n", + "\n", + "IMAGE_EXTENSIONS = {\".jpg\", \".jpeg\", \".png\", \".webp\", \".bmp\"}\n", + "\n", + "FIXED_SAMPLING = {\n", + " \"num_steps\": 35,\n", + " \"guidance\": 6.0,\n", + " \"shift\": 10.0,\n", + " \"fps\": 24,\n", + " \"num_frames\": 189,\n", + " \"resolution\": \"720\",\n", + " \"aspect_ratio\": \"16,9\",\n", + " \"seed\": 0,\n", + "}\n", + "\n", + "# All asset paths are repo-relative under cookbooks/cosmos3/generator/audiovisual.\n", + "# Model and sound choices live in this manifest; folders are organized only by modality.\n", + "ASSET_SETS = {\n", + " \"t2i\": {\n", + " \"model\": \"Cosmos3-Nano\",\n", + " \"mode\": \"text2image\",\n", + " \"prompt\": \"assets/prompts/text2image/robot_draping.json\",\n", + " \"enable_sound\": False,\n", + " },\n", + " \"t2i_super\": {\n", + " \"model\": \"Cosmos3-Super\",\n", + " \"mode\": \"text2image\",\n", + " \"prompt\": \"assets/prompts/text2image/robot_draping.json\",\n", + " \"enable_sound\": False,\n", + " },\n", + " \"t2v_nano_noaudio\": {\n", + " \"model\": \"Cosmos3-Nano\",\n", + " \"mode\": \"text2video\",\n", + " \"prompt\": \"assets/prompts/text2video/robot_kitchen.json\",\n", + " \"enable_sound\": False,\n", + " },\n", + " \"t2vs\": {\n", + " \"model\": \"Cosmos3-Nano\",\n", + " \"mode\": \"text2video\",\n", + " \"prompt\": \"assets/prompts/text2video/robot_pouring_water_audio.json\",\n", + " \"enable_sound\": True,\n", + " },\n", + " \"i2v_nano_noaudio\": {\n", + " \"model\": \"Cosmos3-Nano\",\n", + " \"mode\": \"image2video\",\n", + " \"prompt\": \"assets/prompts/image2video/car_driving.json\",\n", + " \"image\": \"assets/images/image2video/car_driving.jpg\",\n", + " \"enable_sound\": False,\n", + " },\n", + " \"i2vs\": {\n", + " \"model\": \"Cosmos3-Nano\",\n", + " \"mode\": \"image2video\",\n", + " \"prompt\": \"assets/prompts/image2video/coastal_road_audio.json\",\n", + " \"image\": \"assets/images/image2video/coastal_road_audio.jpg\",\n", + " \"enable_sound\": True,\n", + " },\n", + " \"t2v_super_noaudio\": {\n", + " \"model\": \"Cosmos3-Super\",\n", + " \"mode\": \"text2video\",\n", + " \"prompt\": \"assets/prompts/text2video/robot_kitchen.json\",\n", + " \"enable_sound\": False,\n", + " },\n", + " \"i2v_super_noaudio\": {\n", + " \"model\": \"Cosmos3-Super\",\n", + " \"mode\": \"image2video\",\n", + " \"prompt\": \"assets/prompts/image2video/car_driving.json\",\n", + " \"image\": \"assets/images/image2video/car_driving.jpg\",\n", + " \"enable_sound\": False,\n", + " },\n", + "}\n", + "\n", + "\n", + "def asset_path(relative_path: str) -> Path:\n", + " path = COSMOS3_AUDIOVISUAL_ROOT / relative_path\n", + " if not path.exists():\n", + " raise FileNotFoundError(path)\n", + " return path.resolve()\n", + "\n", + "\n", + "def compact_json_file(path: Path) -> str:\n", + " return json.dumps(json.loads(path.read_text()), ensure_ascii=True, separators=(\",\", \":\"))\n", + "\n", + "\n", + "def normalize_negative_prompt(value) -> str:\n", + " if value is None:\n", + " return \"\"\n", + " if isinstance(value, str):\n", + " return value\n", + " if isinstance(value, dict):\n", + " parts = []\n", + " for v in value.values():\n", + " if isinstance(v, str):\n", + " parts.append(v)\n", + " elif isinstance(v, list):\n", + " parts.extend(str(x) for x in v)\n", + " return \"\\n\".join(parts)\n", + " return str(value)\n", + "\n", + "\n", + "def payload_dimensions(payload: dict) -> tuple[int, int]:\n", + " if payload.get(\"resolution\") == \"720\" and payload.get(\"aspect_ratio\") == \"16,9\":\n", + " return 720, 1280\n", + " if payload.get(\"resolution\") == \"256\" and payload.get(\"aspect_ratio\") == \"16,9\":\n", + " return 192, 320\n", + " raise ValueError(f\"Unsupported payload resolution/aspect ratio: {payload.get('resolution')} {payload.get('aspect_ratio')}\")\n", + "\n", + "\n", + "def resolve_payload_path(payload_path: Path, value: str) -> Path:\n", + " path = Path(value)\n", + " if path.is_absolute():\n", + " return path\n", + " return (payload_path.parent / path).resolve()\n", + "\n", + "\n", + "def create_payload(use_case: str, *, backend: str) -> tuple[Path, Path, str]:\n", + " spec = ASSET_SETS[use_case]\n", + " payload_dir = Path(os.environ[\"COSMOS3_AUDIOVISUAL_OUTPUT_ROOT\"]) / backend / \"payloads\" / use_case\n", + " output_dir = Path(os.environ[\"COSMOS3_AUDIOVISUAL_OUTPUT_ROOT\"]) / backend / use_case\n", + " payload_dir.mkdir(parents=True, exist_ok=True)\n", + " output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " prompt_path = asset_path(spec[\"prompt\"])\n", + " negative_prompt = \"\"\n", + " if spec[\"mode\"] != \"text2image\":\n", + " negative_prompt_path = asset_path(f\"assets/negative_prompts/{spec['mode']}/neg_prompt.json\")\n", + " negative_prompt = normalize_negative_prompt(negative_prompt_path)\n", + " payload_path = payload_dir / f\"{use_case}.json\"\n", + " payload = {\n", + " \"model_mode\": spec[\"mode\"],\n", + " \"name\": use_case,\n", + " \"prompt\": compact_json_file(prompt_path),\n", + " \"negative_prompt\": negative_prompt,\n", + " \"enable_sound\": spec[\"enable_sound\"],\n", + " **FIXED_SAMPLING,\n", + " }\n", + " if spec[\"mode\"] == \"image2video\":\n", + " image_path = asset_path(spec[\"image\"])\n", + " payload[\"vision_path\"] = os.path.relpath(image_path, payload_path.parent)\n", + "\n", + " payload_path.write_text(json.dumps(payload, indent=2) + \"\\n\")\n", + "\n", + " os.environ[f\"COSMOS3_{backend.upper()}_{use_case.upper()}_INPUT\"] = str(payload_path)\n", + " os.environ[f\"COSMOS3_{backend.upper()}_{use_case.upper()}_OUTPUT\"] = str(output_dir)\n", + "\n", + " print(f\"model: {spec['model']}\")\n", + " print(f\"payload: {payload_path}\")\n", + " print(f\"output: {output_dir}\")\n", + " print(f\"prompt: {prompt_path.relative_to(COSMOS_ROOT)}\")\n", + " if \"vision_path\" in payload:\n", + " image_display_path = resolve_payload_path(payload_path, payload[\"vision_path\"])\n", + " print(f\"image: {image_display_path.relative_to(COSMOS_ROOT)}\")\n", + " display(Image(filename=str(image_display_path), width=420))\n", + " print(json.dumps({k: payload[k] for k in [\"model_mode\", \"name\", \"enable_sound\", \"num_steps\", \"guidance\", \"shift\", \"fps\", \"num_frames\", \"resolution\", \"aspect_ratio\", \"seed\"]}, indent=2))\n", + " return payload_path, output_dir, spec[\"model\"]\n", + "\n", + "\n", + "import base64\n", + "import html\n", + "import json\n", + "import os\n", + "import subprocess\n", + "import time\n", + "from pathlib import Path\n", + "from IPython.display import HTML, display\n", + "\n", + "\n", + "def api_root_url(base_url: str) -> str:\n", + " normalized = base_url.rstrip(\"/\")\n", + " if not normalized.endswith(\"/v1\"):\n", + " normalized = f\"{normalized}/v1\"\n", + " return normalized\n", + "\n", + "\n", + "def video_api_url(base_url: str) -> str:\n", + " return f\"{api_root_url(base_url)}/videos\"\n", + "\n", + "\n", + "def image_api_url(base_url: str) -> str:\n", + " return f\"{api_root_url(base_url)}/images/generations\"\n", + "\n", + "def build_sglang_video_form(payload: dict) -> dict[str, str]:\n", + " height, width = payload_dimensions(payload)\n", + " extra_params = {\n", + " \"use_resolution_template\": False,\n", + " \"use_duration_template\": False,\n", + " \"guardrails\": True,\n", + " }\n", + " form = {\n", + " \"prompt\": payload[\"prompt\"],\n", + " \"negative_prompt\": payload[\"negative_prompt\"],\n", + " \"size\": f\"{width}x{height}\",\n", + " \"num_frames\": str(payload[\"num_frames\"]),\n", + " \"fps\": str(payload[\"fps\"]),\n", + " \"num_inference_steps\": str(payload[\"num_steps\"]),\n", + " \"guidance_scale\": str(payload[\"guidance\"]),\n", + " \"flow_shift\": str(payload[\"shift\"]),\n", + " \"seed\": str(payload[\"seed\"]),\n", + " \"extra_params\": json.dumps(extra_params, separators=(\",\", \":\")),\n", + " }\n", + " if payload[\"enable_sound\"]:\n", + " form[\"generate_sound\"] = \"true\"\n", + " form[\"sound_duration\"] = f\"{payload['num_frames'] / payload['fps']:.3f}\"\n", + " return form\n", + "\n", + "\n", + "def build_sglang_image_body(payload: dict) -> dict:\n", + " height, width = payload_dimensions(payload)\n", + " return {\n", + " \"prompt\": payload[\"prompt\"],\n", + " \"negative_prompt\": payload.get(\"negative_prompt\", \"\"),\n", + " \"size\": f\"{width}x{height}\",\n", + " \"n\": 1,\n", + " \"num_inference_steps\": payload[\"num_steps\"],\n", + " \"guidance_scale\": payload[\"guidance\"],\n", + " \"flow_shift\": payload[\"shift\"],\n", + " \"seed\": payload[\"seed\"],\n", + " \"response_format\": \"b64_json\",\n", + " \"extra_params\": {\n", + " \"use_resolution_template\": False,\n", + " \"guardrails\": True,\n", + " },\n", + " }\n", + "\n", + "\n", + "def post_video(*, payload_path: Path, payload: dict, output_path: Path, model: str) -> None:\n", + " url = video_api_url(SGLANG_ENDPOINTS[model])\n", + " api_key = os.environ.get(\"COSMOS3_VLLM_API_KEY\") or None\n", + " tmp_path = Path(f\"{output_path}.tmp\")\n", + " error_path = Path(f\"{output_path}.error.txt\")\n", + " if tmp_path.exists():\n", + " tmp_path.unlink()\n", + " if error_path.exists():\n", + " error_path.unlink()\n", + "\n", + " cmd = [\n", + " \"curl\",\n", + " \"-sS\",\n", + " \"--fail-with-body\",\n", + " \"-X\",\n", + " \"POST\",\n", + " url,\n", + " \"-H\",\n", + " \"Accept: video/mp4\",\n", + " ]\n", + " if api_key is not None:\n", + " cmd += [\"-H\", f\"Authorization: Bearer {api_key}\"]\n", + "\n", + " for key, value in build_sglang_video_form(payload).items():\n", + " cmd += [\"--form-string\", f\"{key}={value}\"]\n", + "\n", + " if payload[\"model_mode\"] == \"image2video\":\n", + " image_path = resolve_payload_path(payload_path, payload[\"vision_path\"])\n", + " cmd += [\"-F\", f\"input_reference=@{image_path}\"]\n", + "\n", + " if payload[\"model_mode\"] == \"video2video\":\n", + " video_path = resolve_payload_path(payload_path, payload[\"vision_path\"])\n", + " cmd += [\"-F\", f\"video_reference=@{video_path};type=video/mp4\"]\n", + "\n", + " result = subprocess.run(cmd, text=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", + " if result.returncode != 0:\n", + " error_path.write_text((result.stdout or \"\") + (result.stderr or \"\"))\n", + " raise RuntimeError(f\"SGLang request failed with exit code {result.returncode}; see {error_path}\")\n", + "\n", + " # poll output video\n", + " initial = json.loads(result.stdout)\n", + " video_id = initial[\"id\"]\n", + "\n", + " while True:\n", + " poll = subprocess.run(\n", + " [\"curl\", \"-sS\", \"--fail-with-body\", f\"{url}/{video_id}\"],\n", + " text=True,\n", + " stdout=subprocess.PIPE,\n", + " stderr=subprocess.PIPE,\n", + " )\n", + " if poll.returncode != 0:\n", + " error_path.write_text((poll.stdout or \"\") + (poll.stderr or \"\"))\n", + " raise RuntimeError(f\"SGLang video poll failed; see {error_path}\")\n", + "\n", + " final = json.loads(poll.stdout)\n", + " if final.get(\"status\") == \"completed\":\n", + " break\n", + " if final.get(\"status\") in {\"failed\", \"cancelled\"}:\n", + " error_path.write_text(json.dumps(final, indent=2))\n", + " raise RuntimeError(f\"SGLang video failed; see {error_path}\")\n", + " time.sleep(5)\n", + "\n", + " download = subprocess.run(\n", + " [\"curl\", \"-sS\", \"--fail-with-body\", f\"{url}/{video_id}/content\", \"-o\", str(tmp_path)],\n", + " text=True,\n", + " stdout=subprocess.PIPE,\n", + " stderr=subprocess.PIPE,\n", + " )\n", + " if download.returncode != 0:\n", + " error_path.write_text(json.dumps(final, indent=2) + \"\\n\" + (download.stderr or \"\"))\n", + " raise RuntimeError(f\"SGLang video download failed; see {error_path}\")\n", + "\n", + " tmp_path.replace(output_path)\n", + "\n", + "\n", + "def post_image(*, payload: dict, output_path: Path, model: str) -> None:\n", + " url = image_api_url(SGLANG_ENDPOINTS[model])\n", + " api_key = os.environ.get(\"COSMOS3_SGLANG_API_KEY\") or None\n", + " tmp_path = Path(f\"{output_path}.tmp\")\n", + " error_path = Path(f\"{output_path}.error.txt\")\n", + " if tmp_path.exists():\n", + " tmp_path.unlink()\n", + " if error_path.exists():\n", + " error_path.unlink()\n", + "\n", + " cmd = [\n", + " \"curl\",\n", + " \"-sS\",\n", + " \"--fail-with-body\",\n", + " \"-X\",\n", + " \"POST\",\n", + " url,\n", + " \"-H\",\n", + " \"Content-Type: application/json\",\n", + " ]\n", + " if api_key is not None:\n", + " cmd += [\"-H\", f\"Authorization: Bearer {api_key}\"]\n", + " cmd += [\"-d\", json.dumps(build_sglang_image_body(payload), separators=(\",\", \":\"))]\n", + "\n", + " result = subprocess.run(cmd, text=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", + " if result.returncode != 0:\n", + " error_path.write_text((result.stdout or \"\") + (result.stderr or \"\"))\n", + " raise RuntimeError(f\"SGLang image request failed with exit code {result.returncode}; see {error_path}\")\n", + " try:\n", + " response = json.loads(result.stdout)\n", + " b64_json = response[\"data\"][0][\"b64_json\"]\n", + " tmp_path.write_bytes(base64.b64decode(b64_json))\n", + " except Exception as exc:\n", + " error_path.write_text((result.stdout or \"\") + (result.stderr or \"\"))\n", + " raise RuntimeError(f\"Could not decode SGLang image response; see {error_path}\") from exc\n", + " tmp_path.replace(output_path)\n", + "\n", + "\n", + "def run_sglang_payload(payload_path: Path, output_dir: str | Path, *, model: str) -> Path:\n", + " payload_path = Path(payload_path)\n", + " output_dir = Path(output_dir)\n", + " output_dir.mkdir(parents=True, exist_ok=True)\n", + " payload = json.loads(payload_path.read_text())\n", + " output_ext = \".png\" if payload[\"model_mode\"] == \"text2image\" else \".mp4\"\n", + " output_path = output_dir / f\"{payload['name']}{output_ext}\"\n", + " endpoint = image_api_url(SGLANG_ENDPOINTS[model]) if payload[\"model_mode\"] == \"text2image\" else video_api_url(SGLANG_ENDPOINTS[model])\n", + " print(\"endpoint:\", endpoint)\n", + " print(\"payload:\", payload_path)\n", + " print(\"output:\", output_path)\n", + " if payload[\"model_mode\"] == \"image2video\":\n", + " print(\"input image:\", resolve_payload_path(payload_path, payload[\"vision_path\"]))\n", + " t0 = time.time()\n", + " if payload[\"model_mode\"] == \"text2image\":\n", + " post_image(payload=payload, output_path=output_path, model=model)\n", + " else:\n", + " post_video(payload_path=payload_path, payload=payload, output_path=output_path, model=model)\n", + " print(f\"wrote {output_path} in {time.time() - t0:.1f}s\")\n", + " return output_path\n", + "\n", + "\n", + "def display_video(path: Path, *, width: int = 720) -> None:\n", + " data = base64.b64encode(path.read_bytes()).decode(\"ascii\")\n", + " label = html.escape(str(path))\n", + " markup = f\"\"\"\n", + "\n", + "
{label}
\n", + "\"\"\"\n", + " display(HTML(markup))\n", + "\n", + "\n", + "def view_run(output_dir: str | Path) -> None:\n", + " output_dir = Path(output_dir)\n", + " videos = [\n", + " path\n", + " for path in sorted(output_dir.rglob(\"*.mp4\"))\n", + " if not path.name.endswith((\"_preview.mp4\", \"_browser.mp4\"))\n", + " ]\n", + " images = sorted(output_dir.rglob(\"*.png\"))\n", + " if not videos and not images:\n", + " print(f\"No generated media found under {output_dir}\")\n", + " return\n", + " for src in videos:\n", + " print(f\"source: {src} ({src.stat().st_size // 1024} KB)\")\n", + " display_video(src)\n", + " for src in images:\n", + " print(f\"source: {src} ({src.stat().st_size // 1024} KB)\")\n", + " display(Image(filename=str(src), width=720))\n" + ] + }, + { + "cell_type": "markdown", + "id": "9ffaab0e", + "metadata": {}, + "source": [ + "Run each use case top-to-bottom: create the JSON payload, run inference, then view the generated media. The examples are grouped by model size below. The Cosmos3-Nano and Cosmos3-Super sections are independent, so you can run just one.\n" + ] + }, + { + "cell_type": "markdown", + "id": "e7d284db", + "metadata": {}, + "source": [ + "# Cosmos3-Nano Examples\n", + "\n", + "Use cases for the `Cosmos3-Nano` model. This section is self-contained; you can run it without the Cosmos3-Super section below.\n" + ] + }, + { + "cell_type": "markdown", + "id": "27bf6dce", + "metadata": {}, + "source": [ + "## Nano: Text to Image\n", + "\n", + "Nano text-to-image generation using a structured JSON prompt.\n", + "\n", + "### Create Payload\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "383f6351", + "metadata": {}, + "outputs": [], + "source": [ + "t2i_payload, t2i_output, t2i_model = create_payload(\"t2i\", backend=\"sglang\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "d3c59500", + "metadata": {}, + "source": [ + "### Run\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97b4b312", + "metadata": {}, + "outputs": [], + "source": [ + "run_sglang_payload(t2i_payload, t2i_output, model=\"Cosmos3-Nano\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c027b956", + "metadata": {}, + "source": [ + "### View Results\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fe6e3ed", + "metadata": {}, + "outputs": [], + "source": [ + "view_run(t2i_output)\n" + ] + }, + { + "cell_type": "markdown", + "id": "0e7d3092", + "metadata": {}, + "source": [ + "## Nano: Text to Video Without Audio\n", + "\n", + "Nano text-to-video generation with audio disabled.\n", + "\n", + "### Create Payload\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2e85424", + "metadata": {}, + "outputs": [], + "source": [ + "t2v_nano_noaudio_payload, t2v_nano_noaudio_output, t2v_nano_noaudio_model = create_payload(\"t2v_nano_noaudio\", backend=\"sglang\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "e4fa0cdd", + "metadata": {}, + "source": [ + "### Run\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9db2a1a5", + "metadata": {}, + "outputs": [], + "source": [ + "run_sglang_payload(t2v_nano_noaudio_payload, t2v_nano_noaudio_output, model=\"Cosmos3-Nano\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "1bc8a331", + "metadata": {}, + "source": [ + "### View Results\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87917755", + "metadata": {}, + "outputs": [], + "source": [ + "view_run(t2v_nano_noaudio_output)\n" + ] + }, + { + "cell_type": "markdown", + "id": "816e9b09", + "metadata": {}, + "source": [ + "## Nano: Text to Video with Audio\n", + "\n", + "Nano text-to-video generation with generated audio.\n", + "\n", + "### Create Payload\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8450feb", + "metadata": {}, + "outputs": [], + "source": [ + "t2vs_payload, t2vs_output, t2vs_model = create_payload(\"t2vs\", backend=\"sglang\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "b6bd6ec8", + "metadata": {}, + "source": [ + "### Run\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "685d18d6", + "metadata": {}, + "outputs": [], + "source": [ + "run_sglang_payload(t2vs_payload, t2vs_output, model=\"Cosmos3-Nano\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "93743159", + "metadata": {}, + "source": [ + "### View Results\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4bc049a", + "metadata": {}, + "outputs": [], + "source": [ + "view_run(t2vs_output)\n" + ] + }, + { + "cell_type": "markdown", + "id": "1cf05248", + "metadata": {}, + "source": [ + "## Nano: Image to Video Without Audio\n", + "\n", + "Nano image-to-video generation using its paired image asset, with audio disabled.\n", + "\n", + "### Create Payload\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91e28e16", + "metadata": {}, + "outputs": [], + "source": [ + "i2v_nano_noaudio_payload, i2v_nano_noaudio_output, i2v_nano_noaudio_model = create_payload(\"i2v_nano_noaudio\", backend=\"sglang\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "ef42f84d", + "metadata": {}, + "source": [ + "### Run\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ec848e8", + "metadata": {}, + "outputs": [], + "source": [ + "run_sglang_payload(i2v_nano_noaudio_payload, i2v_nano_noaudio_output, model=\"Cosmos3-Nano\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "dea78891", + "metadata": {}, + "source": [ + "### View Results\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae7a16ed", + "metadata": {}, + "outputs": [], + "source": [ + "view_run(i2v_nano_noaudio_output)\n" + ] + }, + { + "cell_type": "markdown", + "id": "1860e911", + "metadata": {}, + "source": [ + "## Nano: Image to Video with Audio\n", + "\n", + "Nano image-to-video generation using its paired image asset and generated audio.\n", + "\n", + "### Create Payload\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37c40d8e", + "metadata": {}, + "outputs": [], + "source": [ + "i2vs_payload, i2vs_output, i2vs_model = create_payload(\"i2vs\", backend=\"sglang\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "637e5e87", + "metadata": {}, + "source": [ + "### Run\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4a4c308", + "metadata": {}, + "outputs": [], + "source": [ + "run_sglang_payload(i2vs_payload, i2vs_output, model=\"Cosmos3-Nano\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "62994915", + "metadata": {}, + "source": [ + "### View Results\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58a28e11", + "metadata": {}, + "outputs": [], + "source": [ + "view_run(i2vs_output)\n" + ] + }, + { + "cell_type": "markdown", + "id": "fe62931e", + "metadata": {}, + "source": [ + "# Cosmos3-Super Examples\n", + "\n", + "The same use cases for the larger `Cosmos3-Super` model. This section is self-contained; you can run it without the Cosmos3-Nano section above.\n" + ] + }, + { + "cell_type": "markdown", + "id": "466c6bac", + "metadata": {}, + "source": [ + "## Super: Text to Image\n", + "\n", + "Super text-to-image generation using the same structured JSON prompt.\n", + "\n", + "### Create Payload\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "575653c1", + "metadata": {}, + "outputs": [], + "source": [ + "t2i_super_payload, t2i_super_output, t2i_super_model = create_payload(\"t2i_super\", backend=\"sglang\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "42776c8b", + "metadata": {}, + "source": [ + "### Run\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "824783c8", + "metadata": {}, + "outputs": [], + "source": [ + "run_sglang_payload(t2i_super_payload, t2i_super_output, model=\"Cosmos3-Super\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "1ebf0b15", + "metadata": {}, + "source": [ + "### View Results\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0fba2bb", + "metadata": {}, + "outputs": [], + "source": [ + "view_run(t2i_super_output)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f608eaa7", + "metadata": {}, + "source": [ + "## Super: Text to Video Without Audio\n", + "\n", + "Super text-to-video generation with audio disabled.\n", + "\n", + "### Create Payload\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t2v_super_noaudio_payload, t2v_super_noaudio_output, t2v_super_noaudio_model = create_payload(\"t2v_super_noaudio\", backend=\"sglang\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_sglang_payload(t2v_super_noaudio_payload, t2v_super_noaudio_output, model=\"Cosmos3-Super\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View Results\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "view_run(t2v_super_noaudio_output)\n" + ] + }, + { + "cell_type": "markdown", + "id": "367f4161", + "metadata": {}, + "source": [ + "## Super: Image to Video Without Audio\n", + "\n", + "Super image-to-video generation using its paired image asset, with audio disabled.\n", + "\n", + "### Create Payload\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i2v_super_noaudio_payload, i2v_super_noaudio_output, i2v_super_noaudio_model = create_payload(\"i2v_super_noaudio\", backend=\"sglang\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_sglang_payload(i2v_super_noaudio_payload, i2v_super_noaudio_output, model=\"Cosmos3-Super\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View Results\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "view_run(i2v_super_noaudio_output)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}