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README.md

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+---
+title: Paligemma2
+emoji: 🌖
+colorFrom: pink
+colorTo: green
+sdk: gradio
+sdk_version: 5.6.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import gradio as gr
+import PIL.Image
+import transformers
+from transformers import PaliGemmaForConditionalGeneration, PaliGemmaProcessor
+import torch
+import string
+import functools
+import re
+import flax.linen as nn
+import jax
+import jax.numpy as jnp
+import numpy as np
+import spaces
+
+
+model_id = "gv-hf/paligemma2-10b-mix-448"
+COLORS = ['#4285f4', '#db4437', '#f4b400', '#0f9d58', '#e48ef1']
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = PaliGemmaForConditionalGeneration.from_pretrained(model_id).eval().to(device)
+processor = PaliGemmaProcessor.from_pretrained(model_id)
+
+###### Transformers Inference
+@spaces.GPU
+def infer(
+    image: PIL.Image.Image,
+    text: str,
+    max_new_tokens: int
+) -> str:
+    inputs = processor(text=text, images=image, return_tensors="pt").to(device)
+    with torch.inference_mode():
+      generated_ids = model.generate(
+          **inputs,
+          max_new_tokens=max_new_tokens,
+          do_sample=False
+      )
+    result = processor.batch_decode(generated_ids, skip_special_tokens=True)
+    return result[0][len(text):].lstrip("\n")
+
+##### Parse segmentation output tokens into masks
+##### Also returns bounding boxes with their labels
+
+def parse_segmentation(input_image, input_text):
+  out = infer(input_image, input_text, max_new_tokens=100)
+  objs = extract_objs(out.lstrip("\n"), input_image.size[0], input_image.size[1], unique_labels=True)
+  labels = set(obj.get('name') for obj in objs if obj.get('name'))
+  color_map = {l: COLORS[i % len(COLORS)] for i, l in enumerate(labels)}
+  highlighted_text = [(obj['content'], obj.get('name')) for obj in objs]
+  annotated_img = (
+    input_image,
+    [
+        (
+            obj['mask'] if obj.get('mask') is not None else obj['xyxy'],
+            obj['name'] or '',
+        )
+        for obj in objs
+        if 'mask' in obj or 'xyxy' in obj
+    ],
+)
+  has_annotations = bool(annotated_img[1])
+  return annotated_img
+
+
+
+######## Demo
+
+INTRO_TEXT = """## PaliGemma 2 demo\n\n
+| [Github](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/paligemma/README.md) 
+| [Blogpost](https://huggingface.co/blog/paligemma) 
+|\n\n
+PaliGemma 2 is an open vision-language model by Google, inspired by [PaLI-3](https://arxiv.org/abs/2310.09199) and 
+built with open components such as the [SigLIP](https://arxiv.org/abs/2303.15343) 
+vision model and the [Gemma 2](https://arxiv.org/abs/2408.00118) language model. PaliGemma 2 is designed as a versatile 
+model for transfer to a wide range of vision-language tasks such as image and short video caption, visual question 
+answering, text reading, object detection and object segmentation.
+\n\n
+This space includes models fine-tuned on a mix of downstream tasks, **inferred via 🤗 transformers**.
+See the [Blogpost](https://huggingface.co/blog/paligemma2) and 
+[README](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/paligemma/README.md) 
+for detailed information how to use and fine-tune PaliGemma models.
+\n\n
+**This is an experimental research model.** Make sure to add appropriate guardrails when using the model for applications.
+"""
+
+
+with gr.Blocks(css="style.css") as demo:
+  gr.Markdown(INTRO_TEXT)
+  with gr.Tab("Text Generation"):
+    with gr.Column():
+        image = gr.Image(type="pil")
+        text_input = gr.Text(label="Input Text")
+
+        text_output = gr.Text(label="Text Output")
+        chat_btn = gr.Button()
+        tokens = gr.Slider(
+            label="Max New Tokens",
+            info="Set to larger for longer generation.",
+            minimum=10,
+            maximum=100,
+            value=20,
+            step=10,
+        )
+
+    chat_inputs = [
+        image,
+        text_input,
+        tokens
+        ]
+    chat_outputs = [
+        text_output
+    ]
+    chat_btn.click(
+        fn=infer,
+        inputs=chat_inputs,
+        outputs=chat_outputs,
+    )
+    
+    examples = [["./examples/password.jpg", "What is the password?"],
+                ["./examples/howto.jpg", "What does this image show?"],
+                ["./examples/billard.jpg", "How many red balls are there?"],
+               ["./examples/bowie.jpg", "Who is this?"],
+               ["./examples/emu.jpg", "What animal is this?"],
+               ["./examples/bee.jpg", "What is on the flower?"],
+               ["./examples/ulges.jpg", "Who is the author of this book?"]]
+    gr.Markdown("Example images are licensed CC0 by [akolesnikoff@](https://github.com/akolesnikoff), [mbosnjak@](https://github.com/mbosnjak), [maximneumann@](https://github.com/maximneumann) and [merve](https://huggingface.co/merve).")
+
+    gr.Examples(
+        examples=examples,
+        inputs=chat_inputs,
+    )
+  with gr.Tab("Segment/Detect"):
+    image = gr.Image(type="pil")
+    seg_input = gr.Text(label="Entities to Segment/Detect")
+    seg_btn = gr.Button("Submit")
+    annotated_image = gr.AnnotatedImage(label="Output")
+    
+    examples = [["./examples/cats.png", "segment cats"],
+                ["./examples/bee.jpg", "detect bee"],
+               ["./examples/barsik.jpg", "segment cat"],
+               ["./examples/bird.jpg", "segment bird ; bird ; plant"]]
+    gr.Markdown("Example images are licensed CC0 by [akolesnikoff@](https://github.com/akolesnikoff), [mbosnjak@](https://github.com/mbosnjak), [maximneumann@](https://github.com/maximneumann) and [merve](https://huggingface.co/merve).")
+    gr.Examples(
+        examples=examples,
+        inputs=[image, seg_input],
+    )
+
+    seg_inputs = [
+        image,
+        seg_input
+        ]
+    seg_outputs = [
+        annotated_image
+    ]
+    seg_btn.click(
+        fn=parse_segmentation,
+        inputs=seg_inputs,
+        outputs=seg_outputs,
+    )
+
+
+
+
+
+### Postprocessing Utils for Segmentation Tokens
+### Segmentation tokens are passed to another VAE which decodes them to a mask
+
+_MODEL_PATH = 'vae-oid.npz'
+
+_SEGMENT_DETECT_RE = re.compile(
+    r'(.*?)' +
+    r'<loc(\d{4})>' * 4 + r'\s*' +
+    '(?:%s)?' % (r'<seg(\d{3})>' * 16) +
+    r'\s*([^;<>]+)? ?(?:; )?',
+)
+
+
+def _get_params(checkpoint):
+  """Converts PyTorch checkpoint to Flax params."""
+
+  def transp(kernel):
+    return np.transpose(kernel, (2, 3, 1, 0))
+
+  def conv(name):
+    return {
+        'bias': checkpoint[name + '.bias'],
+        'kernel': transp(checkpoint[name + '.weight']),
+    }
+
+  def resblock(name):
+    return {
+        'Conv_0': conv(name + '.0'),
+        'Conv_1': conv(name + '.2'),
+        'Conv_2': conv(name + '.4'),
+    }
+
+  return {
+      '_embeddings': checkpoint['_vq_vae._embedding'],
+      'Conv_0': conv('decoder.0'),
+      'ResBlock_0': resblock('decoder.2.net'),
+      'ResBlock_1': resblock('decoder.3.net'),
+      'ConvTranspose_0': conv('decoder.4'),
+      'ConvTranspose_1': conv('decoder.6'),
+      'ConvTranspose_2': conv('decoder.8'),
+      'ConvTranspose_3': conv('decoder.10'),
+      'Conv_1': conv('decoder.12'),
+  }
+
+
+def _quantized_values_from_codebook_indices(codebook_indices, embeddings):
+  batch_size, num_tokens = codebook_indices.shape
+  assert num_tokens == 16, codebook_indices.shape
+  unused_num_embeddings, embedding_dim = embeddings.shape
+
+  encodings = jnp.take(embeddings, codebook_indices.reshape((-1)), axis=0)
+  encodings = encodings.reshape((batch_size, 4, 4, embedding_dim))
+  return encodings
+
+
+@functools.cache
+def _get_reconstruct_masks():
+  """Reconstructs masks from codebook indices.
+  Returns:
+    A function that expects indices shaped `[B, 16]` of dtype int32, each
+    ranging from 0 to 127 (inclusive), and that returns a decoded masks sized
+    `[B, 64, 64, 1]`, of dtype float32, in range [-1, 1].
+  """
+
+  class ResBlock(nn.Module):
+    features: int
+
+    @nn.compact
+    def __call__(self, x):
+      original_x = x
+      x = nn.Conv(features=self.features, kernel_size=(3, 3), padding=1)(x)
+      x = nn.relu(x)
+      x = nn.Conv(features=self.features, kernel_size=(3, 3), padding=1)(x)
+      x = nn.relu(x)
+      x = nn.Conv(features=self.features, kernel_size=(1, 1), padding=0)(x)
+      return x + original_x
+
+  class Decoder(nn.Module):
+    """Upscales quantized vectors to mask."""
+
+    @nn.compact
+    def __call__(self, x):
+      num_res_blocks = 2
+      dim = 128
+      num_upsample_layers = 4
+
+      x = nn.Conv(features=dim, kernel_size=(1, 1), padding=0)(x)
+      x = nn.relu(x)
+
+      for _ in range(num_res_blocks):
+        x = ResBlock(features=dim)(x)
+
+      for _ in range(num_upsample_layers):
+        x = nn.ConvTranspose(
+            features=dim,
+            kernel_size=(4, 4),
+            strides=(2, 2),
+            padding=2,
+            transpose_kernel=True,
+        )(x)
+        x = nn.relu(x)
+        dim //= 2
+
+      x = nn.Conv(features=1, kernel_size=(1, 1), padding=0)(x)
+
+      return x
+
+  def reconstruct_masks(codebook_indices):
+    quantized = _quantized_values_from_codebook_indices(
+        codebook_indices, params['_embeddings']
+    )
+    return Decoder().apply({'params': params}, quantized)
+
+  with open(_MODEL_PATH, 'rb') as f:
+    params = _get_params(dict(np.load(f)))
+
+  return jax.jit(reconstruct_masks, backend='cpu')
+def extract_objs(text, width, height, unique_labels=False):
+  """Returns objs for a string with "<loc>" and "<seg>" tokens."""
+  objs = []
+  seen = set()
+  while text:
+    m = _SEGMENT_DETECT_RE.match(text)
+    if not m:
+      break
+    print("m", m)
+    gs = list(m.groups())
+    before = gs.pop(0)
+    name = gs.pop()
+    y1, x1, y2, x2 = [int(x) / 1024 for x in gs[:4]]
+    
+    y1, x1, y2, x2 = map(round, (y1*height, x1*width, y2*height, x2*width))
+    seg_indices = gs[4:20]
+    if seg_indices[0] is None:
+      mask = None
+    else:
+      seg_indices = np.array([int(x) for x in seg_indices], dtype=np.int32)
+      m64, = _get_reconstruct_masks()(seg_indices[None])[..., 0]
+      m64 = np.clip(np.array(m64) * 0.5 + 0.5, 0, 1)
+      m64 = PIL.Image.fromarray((m64 * 255).astype('uint8'))
+      mask = np.zeros([height, width])
+      if y2 > y1 and x2 > x1:
+        mask[y1:y2, x1:x2] = np.array(m64.resize([x2 - x1, y2 - y1])) / 255.0
+
+    content = m.group()
+    if before:
+      objs.append(dict(content=before))
+      content = content[len(before):]
+    while unique_labels and name in seen:
+      name = (name or '') + "'"
+    seen.add(name)
+    objs.append(dict(
+        content=content, xyxy=(x1, y1, x2, y2), mask=mask, name=name))
+    text = text[len(before) + len(content):]
+
+  if text:
+    objs.append(dict(content=text))
+
+  return objs
+
+#########
+
+if __name__ == "__main__":
+    demo.queue(max_size=10).launch(debug=True)

requirements.txt

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+torch
+jax
+flax
+spaces
+transformers

vae-oid.npz

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+size 8479556