Merge pull request #117 from borisdayma/fix-inference

tools/inference/inference_pipeline.ipynb

@@ -70,7 +70,7 @@
     "# Model references\n",
     "\n",
     "# dalle-mini\n",
-    "DALLE_MODEL = 'dalle-mini/dalle-mini/model-262fxth3:latest'  # can be wandb artifact or 🤗 Hub or local folder\n",
+    "DALLE_MODEL = 'dalle-mini/dalle-mini/model-3bqwu04f:latest'  # can be wandb artifact or 🤗 Hub or local folder\n",
     "DALLE_COMMIT_ID = None  # used only with 🤗 hub\n",
     "\n",
     "# VQGAN model\n",
@@ -92,7 +92,13 @@
     "import jax.numpy as jnp\n",
     "\n",
     "# type used for computation - use bfloat16 on TPU's\n",
-    "dtype = jnp.bfloat16 if jax.local_device_count() == 8 else jnp.float32"
+    "dtype = jnp.bfloat16 if jax.local_device_count() == 8 else jnp.float32\n",
+    "\n",
+    "# TODO:\n",
+    "# - we currently have an issue with model.generate() in bfloat16\n",
+    "# - https://github.com/google/jax/pull/9089 should fix it\n",
+    "# - remove below line and test on TPU with next release of JAX\n",
+    "dtype = jnp.float32"
    ]
   },
   {
@@ -281,7 +287,7 @@
    },
    "outputs": [],
    "source": [
-    "prompt = 'view of the beach during sunset'"
+    "prompt = 'a red T-shirt'"
    ]
   },
   {
@@ -292,7 +298,8 @@
    },
    "outputs": [],
    "source": [
-    "processed_prompt = text_normalizer(prompt) if model.config.normalize_text else prompt"
+    "processed_prompt = text_normalizer(prompt) if model.config.normalize_text else prompt\n",
+    "processed_prompt"
    ]
   },
   {
@@ -375,7 +382,7 @@
    "outputs": [],
    "source": [
     "# number of predictions\n",
-    "n_predictions = 8\n",
+    "n_predictions = 32\n",
     "\n",
     "# We can customize top_k/top_p used for generating samples\n",
     "gen_top_k = None\n",
@@ -431,7 +438,7 @@
     "# get clip scores\n",
     "clip_inputs = processor(text=[prompt] * jax.device_count(), images=images, return_tensors='np', padding='max_length', max_length=77, truncation=True).data\n",
     "logits = p_clip(shard(clip_inputs), clip_params)\n",
-    "logits = logits.squeeze()"
+    "logits = logits.squeeze().flatten()"
    ]
   },
   {

tools/inference/log_inference_samples.ipynb

@@ -1,434 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4ff2a984-b8b2-4a69-89cf-0d16da2393c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tempfile\n",
-    "from functools import partial\n",
-    "import random\n",
-    "import numpy as np\n",
-    "from PIL import Image\n",
-    "from tqdm.notebook import tqdm\n",
-    "import jax\n",
-    "import jax.numpy as jnp\n",
-    "from flax.training.common_utils import shard, shard_prng_key\n",
-    "from flax.jax_utils import replicate\n",
-    "import wandb\n",
-    "from dalle_mini.model import CustomFlaxBartForConditionalGeneration\n",
-    "from vqgan_jax.modeling_flax_vqgan import VQModel\n",
-    "from transformers import BartTokenizer, CLIPProcessor, FlaxCLIPModel\n",
-    "from dalle_mini.text import TextNormalizer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "92f4557c-fd7f-4edc-81c2-de0b0a10c270",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_ids = [\"63otg87g\"]\n",
-    "ENTITY, PROJECT = \"dalle-mini\", \"dalle-mini\"  # used only for training run\n",
-    "VQGAN_REPO, VQGAN_COMMIT_ID = (\n",
-    "    \"dalle-mini/vqgan_imagenet_f16_16384\",\n",
-    "    \"e93a26e7707683d349bf5d5c41c5b0ef69b677a9\",\n",
-    ")\n",
-    "latest_only = True  # log only latest or all versions\n",
-    "suffix = \"\"  # mainly for duplicate inference runs with a deleted version\n",
-    "add_clip_32 = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "71f27b96-7e6c-4472-a2e4-e99a8fb67a72",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# model.generate parameters - Not used yet\n",
-    "gen_top_k = None\n",
-    "gen_top_p = None\n",
-    "temperature = None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "93b2e24b-f0e5-4abe-a3ec-0aa834cc3bf3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batch_size = 8\n",
-    "num_images = 128\n",
-    "top_k = 8\n",
-    "text_normalizer = TextNormalizer()\n",
-    "padding_item = \"NONE\"\n",
-    "seed = random.randint(0, 2 ** 32 - 1)\n",
-    "key = jax.random.PRNGKey(seed)\n",
-    "api = wandb.Api()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c6a878fa-4bf5-4978-abb5-e235841d765b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vqgan = VQModel.from_pretrained(VQGAN_REPO, revision=VQGAN_COMMIT_ID)\n",
-    "vqgan_params = replicate(vqgan.params)\n",
-    "\n",
-    "clip16 = FlaxCLIPModel.from_pretrained(\"openai/clip-vit-base-patch16\")\n",
-    "processor16 = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch16\")\n",
-    "clip16_params = replicate(clip16.params)\n",
-    "\n",
-    "if add_clip_32:\n",
-    "    clip32 = FlaxCLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
-    "    processor32 = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
-    "    clip32_params = replicate(clip32.params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a500dd07-dbc3-477d-80d4-2b73a3b83ef3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@partial(jax.pmap, axis_name=\"batch\")\n",
-    "def p_decode(indices, params):\n",
-    "    return vqgan.decode_code(indices, params=params)\n",
-    "\n",
-    "\n",
-    "@partial(jax.pmap, axis_name=\"batch\")\n",
-    "def p_clip16(inputs, params):\n",
-    "    logits = clip16(params=params, **inputs).logits_per_image\n",
-    "    return logits\n",
-    "\n",
-    "\n",
-    "if add_clip_32:\n",
-    "\n",
-    "    @partial(jax.pmap, axis_name=\"batch\")\n",
-    "    def p_clip32(inputs, params):\n",
-    "        logits = clip32(params=params, **inputs).logits_per_image\n",
-    "        return logits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e57797ab-0b3a-4490-be58-03d8d1c23fe9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"samples.txt\", encoding=\"utf8\") as f:\n",
-    "    samples = [l.strip() for l in f.readlines()]\n",
-    "    # make list multiple of batch_size by adding elements\n",
-    "    samples_to_add = [padding_item] * (-len(samples) % batch_size)\n",
-    "    samples.extend(samples_to_add)\n",
-    "    # reshape\n",
-    "    samples = [samples[i : i + batch_size] for i in range(0, len(samples), batch_size)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f3e02d9d-4ee1-49e7-a7bc-4d8b139e9614",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_artifact_versions(run_id, latest_only=False):\n",
-    "    try:\n",
-    "        if latest_only:\n",
-    "            return [\n",
-    "                api.artifact(\n",
-    "                    type=\"bart_model\", name=f\"{ENTITY}/{PROJECT}/model-{run_id}:latest\"\n",
-    "                )\n",
-    "            ]\n",
-    "        else:\n",
-    "            return api.artifact_versions(\n",
-    "                type_name=\"bart_model\",\n",
-    "                name=f\"{ENTITY}/{PROJECT}/model-{run_id}\",\n",
-    "                per_page=10000,\n",
-    "            )\n",
-    "    except:\n",
-    "        return []"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f0d7ed17-7abb-4a31-ab3c-a12b9039a570",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_training_config(run_id):\n",
-    "    training_run = api.run(f\"{ENTITY}/{PROJECT}/{run_id}\")\n",
-    "    config = training_run.config\n",
-    "    return config"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7e784a43-626d-4e8d-9e47-a23775b2f35f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# retrieve inference run details\n",
-    "def get_last_inference_version(run_id):\n",
-    "    try:\n",
-    "        inference_run = api.run(f\"dalle-mini/dalle-mini/{run_id}-clip16{suffix}\")\n",
-    "        return inference_run.summary.get(\"version\", None)\n",
-    "    except:\n",
-    "        return None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d1cc9993-1bfc-4ec6-a004-c056189c42ac",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# compile functions - needed only once per run\n",
-    "def pmap_model_function(model):\n",
-    "    @partial(jax.pmap, axis_name=\"batch\")\n",
-    "    def _generate(tokenized_prompt, key, params):\n",
-    "        return model.generate(\n",
-    "            **tokenized_prompt,\n",
-    "            do_sample=True,\n",
-    "            num_beams=1,\n",
-    "            prng_key=key,\n",
-    "            params=params,\n",
-    "            top_k=gen_top_k,\n",
-    "            top_p=gen_top_p\n",
-    "        )\n",
-    "\n",
-    "    return _generate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "23b2444c-67a9-44d7-abd1-187ed83a9431",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_id = run_ids[0]\n",
-    "# TODO: loop over runs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bba70f33-af8b-4eb3-9973-7be672301a0b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "artifact_versions = get_artifact_versions(run_id, latest_only)\n",
-    "last_inference_version = get_last_inference_version(run_id)\n",
-    "training_config = get_training_config(run_id)\n",
-    "run = None\n",
-    "p_generate = None\n",
-    "model_files = [\n",
-    "    \"config.json\",\n",
-    "    \"flax_model.msgpack\",\n",
-    "    \"merges.txt\",\n",
-    "    \"special_tokens_map.json\",\n",
-    "    \"tokenizer.json\",\n",
-    "    \"tokenizer_config.json\",\n",
-    "    \"vocab.json\",\n",
-    "]\n",
-    "for artifact in artifact_versions:\n",
-    "    print(f\"Processing artifact: {artifact.name}\")\n",
-    "    version = int(artifact.version[1:])\n",
-    "    results16, results32 = [], []\n",
-    "    columns = [\"Caption\"] + [f\"Image {i+1}\" for i in range(top_k)]\n",
-    "\n",
-    "    if latest_only:\n",
-    "        assert last_inference_version is None or version > last_inference_version\n",
-    "    else:\n",
-    "        if last_inference_version is None:\n",
-    "            # we should start from v0\n",
-    "            assert version == 0\n",
-    "        elif version <= last_inference_version:\n",
-    "            print(\n",
-    "                f\"v{version} has already been logged (versions logged up to v{last_inference_version}\"\n",
-    "            )\n",
-    "        else:\n",
-    "            # check we are logging the correct version\n",
-    "            assert version == last_inference_version + 1\n",
-    "\n",
-    "    # start/resume corresponding run\n",
-    "    if run is None:\n",
-    "        run = wandb.init(\n",
-    "            job_type=\"inference\",\n",
-    "            entity=\"dalle-mini\",\n",
-    "            project=\"dalle-mini\",\n",
-    "            config=training_config,\n",
-    "            id=f\"{run_id}-clip16{suffix}\",\n",
-    "            resume=\"allow\",\n",
-    "        )\n",
-    "\n",
-    "    # work in temporary directory\n",
-    "    with tempfile.TemporaryDirectory() as tmp:\n",
-    "\n",
-    "        # download model files\n",
-    "        artifact = run.use_artifact(artifact)\n",
-    "        for f in model_files:\n",
-    "            artifact.get_path(f).download(tmp)\n",
-    "\n",
-    "        # load tokenizer and model\n",
-    "        tokenizer = BartTokenizer.from_pretrained(tmp)\n",
-    "        model = CustomFlaxBartForConditionalGeneration.from_pretrained(tmp)\n",
-    "        model_params = replicate(model.params)\n",
-    "\n",
-    "        # pmap model function needs to happen only once per model config\n",
-    "        if p_generate is None:\n",
-    "            p_generate = pmap_model_function(model)\n",
-    "\n",
-    "        # process one batch of captions\n",
-    "        for batch in tqdm(samples):\n",
-    "            processed_prompts = (\n",
-    "                [text_normalizer(x) for x in batch]\n",
-    "                if model.config.normalize_text\n",
-    "                else list(batch)\n",
-    "            )\n",
-    "\n",
-    "            # repeat the prompts to distribute over each device and tokenize\n",
-    "            processed_prompts = processed_prompts * jax.device_count()\n",
-    "            tokenized_prompt = tokenizer(\n",
-    "                processed_prompts,\n",
-    "                return_tensors=\"jax\",\n",
-    "                padding=\"max_length\",\n",
-    "                truncation=True,\n",
-    "                max_length=128,\n",
-    "            ).data\n",
-    "            tokenized_prompt = shard(tokenized_prompt)\n",
-    "\n",
-    "            # generate images\n",
-    "            images = []\n",
-    "            pbar = tqdm(\n",
-    "                range(num_images // jax.device_count()),\n",
-    "                desc=\"Generating Images\",\n",
-    "                leave=True,\n",
-    "            )\n",
-    "            for i in pbar:\n",
-    "                key, subkey = jax.random.split(key)\n",
-    "                encoded_images = p_generate(\n",
-    "                    tokenized_prompt, shard_prng_key(subkey), model_params\n",
-    "                )\n",
-    "                encoded_images = encoded_images.sequences[..., 1:]\n",
-    "                decoded_images = p_decode(encoded_images, vqgan_params)\n",
-    "                decoded_images = decoded_images.clip(0.0, 1.0).reshape(\n",
-    "                    (-1, 256, 256, 3)\n",
-    "                )\n",
-    "                for img in decoded_images:\n",
-    "                    images.append(\n",
-    "                        Image.fromarray(np.asarray(img * 255, dtype=np.uint8))\n",
-    "                    )\n",
-    "\n",
-    "            def add_clip_results(results, processor, p_clip, clip_params):\n",
-    "                clip_inputs = processor(\n",
-    "                    text=batch,\n",
-    "                    images=images,\n",
-    "                    return_tensors=\"np\",\n",
-    "                    padding=\"max_length\",\n",
-    "                    max_length=77,\n",
-    "                    truncation=True,\n",
-    "                ).data\n",
-    "                # each shard will have one prompt, images need to be reorganized to be associated to the correct shard\n",
-    "                images_per_prompt_indices = np.asarray(\n",
-    "                    range(0, len(images), batch_size)\n",
-    "                )\n",
-    "                clip_inputs[\"pixel_values\"] = jnp.concatenate(\n",
-    "                    list(\n",
-    "                        clip_inputs[\"pixel_values\"][images_per_prompt_indices + i]\n",
-    "                        for i in range(batch_size)\n",
-    "                    )\n",
-    "                )\n",
-    "                clip_inputs = shard(clip_inputs)\n",
-    "                logits = p_clip(clip_inputs, clip_params)\n",
-    "                logits = logits.reshape(-1, num_images)\n",
-    "                top_scores = logits.argsort()[:, -top_k:][..., ::-1]\n",
-    "                logits = jax.device_get(logits)\n",
-    "                # add to results table\n",
-    "                for i, (idx, scores, sample) in enumerate(\n",
-    "                    zip(top_scores, logits, batch)\n",
-    "                ):\n",
-    "                    if sample == padding_item:\n",
-    "                        continue\n",
-    "                    cur_images = [images[x] for x in images_per_prompt_indices + i]\n",
-    "                    top_images = [\n",
-    "                        wandb.Image(cur_images[x], caption=f\"Score: {scores[x]:.2f}\")\n",
-    "                        for x in idx\n",
-    "                    ]\n",
-    "                    results.append([sample] + top_images)\n",
-    "\n",
-    "            # get clip scores\n",
-    "            pbar.set_description(\"Calculating CLIP 16 scores\")\n",
-    "            add_clip_results(results16, processor16, p_clip16, clip16_params)\n",
-    "\n",
-    "            # get clip 32 scores\n",
-    "            if add_clip_32:\n",
-    "                pbar.set_description(\"Calculating CLIP 32 scores\")\n",
-    "                add_clip_results(results32, processor32, p_clip32, clip32_params)\n",
-    "\n",
-    "            pbar.close()\n",
-    "\n",
-    "    # log results\n",
-    "    table = wandb.Table(columns=columns, data=results16)\n",
-    "    run.log({\"Samples\": table, \"version\": version})\n",
-    "    wandb.finish()\n",
-    "\n",
-    "    if add_clip_32:\n",
-    "        run = wandb.init(\n",
-    "            job_type=\"inference\",\n",
-    "            entity=\"dalle-mini\",\n",
-    "            project=\"dalle-mini\",\n",
-    "            config=training_config,\n",
-    "            id=f\"{run_id}-clip32{suffix}\",\n",
-    "            resume=\"allow\",\n",
-    "        )\n",
-    "        table = wandb.Table(columns=columns, data=results32)\n",
-    "        run.log({\"Samples\": table, \"version\": version})\n",
-    "        wandb.finish()\n",
-    "        run = None  # ensure we don't log on this run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "415d3f54-7226-43de-9eea-4283a948dc93",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

tools/inference/samples.txt

@@ -1,124 +0,0 @@
-t-shirt, size M
-flower dress, size M
-white snow covered mountain under blue sky during daytime
-aerial view of the beach during daytime
-aerial view of the beach at night
-a beautiful sunset at a beach with a shell on the shore
-a farmhouse surrounded by beautiful flowers
-sunset over green mountains
-a photo of san francisco golden gate bridge
-painting of an oniric forest glade surrounded by tall trees
-a graphite sketch of a gothic cathedral
-a graphite sketch of Elon Musk
-still life in the style of Kandinsky
-still life in the style of Picasso
-a colorful stairway to heaven
-a background consisting of colors blue, green, and red
-Mohammed Ali and Mike Tyson in a match
-Pele and Maradona in a match
-view of Mars from space
-a picture of the Eiffel tower on the moon
-a picture of the Eiffel tower on the moon, Earth is in the background
-watercolor of the Eiffel tower on the moon
-the moon is a skull
-epic sword fight
-underwater cathedral
-a photo of a fantasy version of New York City
-a picture of fantasy kingdoms
-a volcano erupting next to San Francisco golden gate bridge
-Paris in a far future, futuristic Paris
-real painting of an alien from Monet
-the communist statue of liberty
-robots taking control over humans
-illustration of an astronaut in a space suit playing guitar
-a clown wearing a spacesuit floating in space
-a dog playing with a ball
-a cat sits on top of an alligator
-a very cute cat laying by a big bike
-a rat holding a red lightsaber in a white background
-a very cute giraffe making a funny face
-A unicorn is passing by a rainbow in a field of flowers
-an elephant made of carrots
-an elephant on a unicycle during a circus
-photography of a penguin watching television
-a penguin is walking on the Moon, Earth is in the background
-a penguin standing on a tower of books holds onto a rope from a helicopter
-rat wearing a crown
-looking into the sky, 10 airplanes are seen overhead
-shelves filled with books and alchemy potion bottles
-this is a detailed high-resolution scan of a human brain
-a restaurant menu
-a bottle of coca-cola on a table
-a peanut
-a cross-section view of a walnut
-a living room with two white armchairs and a painting of the collosseum. The painting is mounted above a modern fireplace.
-a long line of alternating green and red blocks
-a long line of green blocks on a beach at subset
-a long line of peaches on a beach at sunset
-a picture of a castle from minecraft
-a cute pikachu teapot
-an illustration of pikachu sitting on a bench eating an ice cream
-mario is jumping over a zebra
-famous anime hero
-star wars concept art
-Cartoon of a carrot with big eyes
-a cartoon of a superhero bear
-an illustration of a cute skeleton wearing a blue hoodie
-illustration of a baby shark swimming around corals
-an illustration of an avocado in a beanie riding a motorcycle
-logo of a robot wearing glasses and reading a book
-illustration of a cactus lifting weigths
-logo of a cactus lifting weights
-a photo of a camera from the future
-a skeleton with the shape of a spider
-a collection of glasses is sitting on a table
-a painting of a capybara sitting on a mountain during fall in surrealist style
-a pentagonal green clock
-a small red block sitting on a large green block
-a storefront that has the word 'openai' written on it
-a tatoo of a black broccoli
-a variety of clocks is sitting on a table
-a table has a train model on it with other cars and things
-a pixel art illustration of an eagle sitting in a field in the afternoon
-an emoji of a baby fox wearing a blue hat, green gloves, red shirt, and yellow pants
-an emoji of a baby penguin wearing a blue hat, blue gloves, red shirt, and green pants
-an extreme close-up view of a capybara sitting in a field
-an illustration of a baby cucumber with a mustache playing chess
-an illustration of a baby daikon radish in a tutu walking a dog
-an illustration of a baby hedgehog in a cape staring at its reflection in a mirror
-an illustration of a baby panda with headphones holding an umbrella in the rain
-urinals are lined up in a jungle
-a muscular banana sitting upright on a bench smoking watching a banana on television, high definition photography
-a human face
-a person is holding a phone and a waterbottle, running a marathon
-a child eating a birthday cake near some balloons
-Young woman riding her bike through the forest
-the best soccer team of the world
-the best football team of the world
-the best basketball team of the world
-happy, happiness
-sad, sadness
-the representation of infinity
-the end of the world
-the last sunrise on earth
-a portrait of a nightmare creature watching at you
-an avocado armchair
-an armchair in the shape of an avocado
-illustration of an avocado armchair
-illustration of an armchair in the shape of an avocado
-logo of an avocado armchair
-an avocado armchair flying into space
-a cute avocado armchair singing karaoke on stage in front of a crowd of strawberry shaped lamps
-an illustration of an avocado in a christmas sweater staring at its reflection in a mirror
-illustration of an avocado armchair getting married to a pineapple
-half human half cat
-half human half dog
-half human half pen
-half human half garbage
-half human half avocado
-half human half Eiffel tower
-a propaganda poster for transhumanism
-a propaganda poster for building a space elevator
-a beautiful epic fantasy painting of a space elevator
-a transformer architecture
-a transformer in real life