generate_images_stream and generate_images

README.rst

@@ -1,102 +0,0 @@
-min(DALL·E)
-===========
-
-|Colab|   |Replicate|   |Discord|
-
-This is a fast, minimal port of Boris Dayma’s `DALL·E
-Mega <https://github.com/borisdayma/dalle-mini>`__. It has been stripped
-down for inference and converted to PyTorch. The only third party
-dependencies are numpy, requests, pillow and torch.
-
-To generate a 4x4 grid of DALL·E Mega images it takes: - 89 sec with a
-T4 in Colab - 48 sec with a P100 in Colab - 14 sec with an A100 on
-Replicate
-
-The flax model and code for converting it to torch can be found
-`here <https://github.com/kuprel/min-dalle-flax>`__.
-
-Install
--------
-
-.. code:: bash
-
-   $ pip install min-dalle
-
-Usage
------
-
-Load the model parameters once and reuse the model to generate multiple
-images.
-
-.. code:: python
-
-   from min_dalle import MinDalle
-
-   model = MinDalle(
-       is_mega=True, 
-       is_reusable=True,
-       models_root='./pretrained'
-   )
-
-The required models will be downloaded to ``models_root`` if they are
-not already there. Once everything has finished initializing, call
-``generate_image`` with some text as many times as you want. Use a
-positive ``seed`` for reproducible results. Higher values for
-``log2_supercondition_factor`` result in better agreement with the text
-but a narrower variety of generated images. Every image token is sampled
-from the top-:math:`k` most probable tokens.
-
-.. code:: python
-
-   image = model.generate_image(
-       text='Nuclear explosion broccoli',
-       seed=-1,
-       grid_size=4,
-       log2_k=6,
-       log2_supercondition_factor=5,
-       is_verbose=False
-   )
-
-   display(image)
-
-Interactive
-~~~~~~~~~~~
-
-If the model is being used interactively (e.g. in a notebook)
-``generate_image_stream`` can be used to generate a stream of images as
-the model is decoding. The detokenizer adds a slight delay for each
-image. Setting ``log2_mid_count`` to 3 results in a total of
-``2 ** 3 = 8`` generated images. The only valid values for
-``log2_mid_count`` are 0, 1, 2, 3, and 4. This is implemented in the
-colab.
-
-.. code:: python
-
-   image_stream = model.generate_image_stream(
-       text='Dali painting of WALL·E',
-       seed=-1,
-       grid_size=3,
-       log2_mid_count=3,
-       log2_k=6,
-       log2_supercondition_factor=3,
-       is_verbose=False
-   )
-
-   for image in image_stream:
-       display(image)
-
-Command Line
-~~~~~~~~~~~~
-
-Use ``image_from_text.py`` to generate images from the command line.
-
-.. code:: bash
-
-   $ python image_from_text.py --text='artificial intelligence' --no-mega
-
-.. |Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
-   :target: https://colab.research.google.com/github/kuprel/min-dalle/blob/main/min_dalle.ipynb
-.. |Replicate| image:: https://replicate.com/kuprel/min-dalle/badge
-   :target: https://replicate.com/kuprel/min-dalle
-.. |Discord| image:: https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white
-   :target: https://discord.com/channels/823813159592001537/912729332311556136

cog.yaml

@@ -6,7 +6,7 @@ build:
     - "libgl1-mesa-glx"
     - "libglib2.0-0"
   python_packages:
-    - "min-dalle==0.2.36"
+    - "min-dalle==0.3.1"
   run:
     - pip install torch==1.11.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
 

min_dalle/min_dalle.py

@@ -1,7 +1,9 @@
 import os
 from PIL import Image
+from matplotlib.pyplot import grid
 import numpy
 from torch import LongTensor
+from math import sqrt
 import torch
 import json
 import requests
@@ -142,25 +144,29 @@ class MinDalle:
         if torch.cuda.is_available(): self.detokenizer = self.detokenizer.cuda()
 
 
-    def image_from_tokens(
+    def images_from_tokens(
         self,
-        grid_size: int,
         image_tokens: LongTensor,
         is_verbose: bool = False
-    ) -> Image.Image:
+    ) -> LongTensor:
         if not self.is_reusable: del self.decoder
         if torch.cuda.is_available(): torch.cuda.empty_cache()
         if not self.is_reusable: self.init_detokenizer()
         if is_verbose: print("detokenizing image")
         images = self.detokenizer.forward(image_tokens).to(torch.uint8)
         if not self.is_reusable: del self.detokenizer
+        return images
+
+
+    def grid_from_images(self, images: LongTensor) -> Image.Image:
+        grid_size = int(sqrt(images.shape[0]))
         images = images.reshape([grid_size] * 2 + list(images.shape[1:]))
         image = images.flatten(1, 2).transpose(0, 1).flatten(1, 2)
         image = Image.fromarray(image.to('cpu').detach().numpy())
         return image
 
 
-    def generate_image_stream(
+    def generate_images_stream(
         self, 
         text: str, 
         seed: int,
@@ -169,7 +175,7 @@ class MinDalle:
         log2_k: int = 6,
         log2_supercondition_factor: int = 3,
         is_verbose: bool = False
-    ) -> Iterator[Image.Image]:
+    ) -> Iterator[LongTensor]:
         assert(log2_mid_count in range(5))
         if is_verbose: print("tokenizing text")
         tokens = self.tokenizer.tokenize(text, is_verbose=is_verbose)
@@ -219,8 +225,53 @@ class MinDalle:
             with torch.cuda.amp.autocast(dtype=torch.float32):
                 if ((row_index + 1) * (2 ** log2_mid_count)) % row_count == 0:
                     tokens = image_tokens[:, 1:]
-                    image = self.image_from_tokens(grid_size, tokens, is_verbose)
-                    yield image
+                    images = self.images_from_tokens(tokens, is_verbose)
+                    yield images
+
+
+    def generate_image_stream(
+        self, 
+        text: str, 
+        seed: int,
+        grid_size: int,
+        log2_mid_count: int,
+        log2_k: int = 6,
+        log2_supercondition_factor: int = 3,
+        is_verbose: bool = False
+    ) -> Iterator[Image.Image]:
+        images_stream = self.generate_images_stream(
+            text, 
+            seed,
+            grid_size,
+            log2_mid_count,
+            log2_k,
+            log2_supercondition_factor,
+            is_verbose
+        )
+        for images in images_stream:
+            yield self.grid_from_images(images)
+
+
+    def generate_images(
+        self, 
+        text: str,
+        seed: int = -1,
+        grid_size: int = 1,
+        log2_k: int = 6,
+        log2_supercondition_factor: int = 3,
+        is_verbose: bool = False
+    ) -> LongTensor:
+        log2_mid_count = 0
+        images_stream = self.generate_images_stream(
+            text,
+            seed,
+            grid_size,
+            log2_mid_count,
+            log2_k,
+            log2_supercondition_factor,
+            is_verbose
+        )
+        return next(images_stream)
 
 
     def generate_image(

replicate_predictor.py

@@ -1,5 +1,6 @@
 from min_dalle import MinDalle
 import tempfile
+import torch
 from typing import Iterator
 from cog import BasePredictor, Path, Input
 
@@ -53,5 +54,6 @@ class ReplicatePredictor(BasePredictor):
         except:
             print("An error occured, deleting model")
             del self.model
+            torch.cuda.empty_cache()
             self.setup()
             raise Exception("There was an error, please try again")

setup.py

@@ -5,7 +5,7 @@ setuptools.setup(
     name='min-dalle',
     description = 'min(DALL·E)',
     # long_description=(Path(__file__).parent / "README.rst").read_text(),
-    version='0.2.36',
+    version='0.3.1',
     author='Brett Kuprel',
     author_email='brkuprel@gmail.com',
     url='https://github.com/kuprel/min-dalle',