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fix individual images

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This commit is contained in:
Brett Kuprel 2022-07-17 08:34:32 -04:00
parent 798e6ac5a3
commit f0c4fc7350
5 changed files with 34 additions and 76 deletions
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4
README.md vendored
View File

@ -62,7 +62,7 @@ display(image)
Credit to [@hardmaru](https://twitter.com/hardmaru) for the [example](https://twitter.com/hardmaru/status/1544354119527596034)
<!-- ### Saving Individual Images
### Saving Individual Images
The images can also be generated as a `FloatTensor` in case you want to process them manually.
```python
@ -85,7 +85,7 @@ Then image $i$ can be coverted to a PIL.Image and saved
```python
image = Image.fromarray(images[i])
image.save('image_{}.png'.format(i))
``` -->
```
### Progressive Outputs

96
min_dalle/min_dalle.py
View File

@ -171,7 +171,7 @@ class MinDalle:
return images
def generate_image_stream(
def generate_raw_image_stream(
self,
text: str,
seed: int,
@ -182,7 +182,7 @@ class MinDalle:
top_k: int = 256,
supercondition_factor: int = 16,
is_verbose: bool = False
) -> Iterator[Image.Image]:
) -> Iterator[FloatTensor]:
image_count = grid_size ** 2
if is_verbose: print("tokenizing text")
tokens = self.tokenizer.tokenize(text, is_verbose=is_verbose)
@ -249,84 +249,40 @@ class MinDalle:
with torch.cuda.amp.autocast(dtype=torch.float32):
if ((i + 1) % 32 == 0 and progressive_outputs) or i + 1 == 256:
image = self.image_grid_from_tokens(
yield self.image_grid_from_tokens(
image_tokens=image_tokens[1:].T,
is_seamless=is_seamless,
is_verbose=is_verbose
)
def generate_image_stream(self, *args, **kwargs) -> Iterator[Image.Image]:
image_stream = self.generate_raw_image_stream(*args, **kwargs)
for image in image_stream:
image = image.to(torch.uint8).to('cpu').numpy()
yield Image.fromarray(image)
def generate_image(
self,
text: str,
seed: int = -1,
grid_size: int = 1,
temperature: float = 1,
top_k: int = 1024,
supercondition_factor: int = 16,
is_verbose: bool = False
) -> Image.Image:
def generate_images_stream(self, *args, **kwargs) -> Iterator[FloatTensor]:
image_stream = self.generate_raw_image_stream(*args, **kwargs)
for image in image_stream:
grid_size = kwargs['grid_size']
image = image.view([grid_size * 256, grid_size, 256, 3])
image = image.transpose(1, 0)
image = image.reshape([grid_size ** 2, 2 ** 8, 2 ** 8, 3])
yield image
def generate_image(self, *args, **kwargs) -> Image.Image:
image_stream = self.generate_image_stream(
text=text,
seed=seed,
grid_size=grid_size,
progressive_outputs=False,
temperature=temperature,
top_k=top_k,
supercondition_factor=supercondition_factor,
is_verbose=is_verbose
*args, **kwargs,
progressive_outputs=False
)
return next(image_stream)
# def images_from_image(image: Image.Image) -> FloatTensor:
# pass
# def generate_images_stream(
# self,
# text: str,
# seed: int,
# grid_size: int,
# progressive_outputs: bool = False,
# temperature: float = 1,
# top_k: int = 256,
# supercondition_factor: int = 16,
# is_verbose: bool = False
# ) -> Iterator[FloatTensor]:
# image_stream = self.generate_image_stream(
# text=text,
# seed=seed,
# image_count=grid_size ** 2,
# progressive_outputs=progressive_outputs,
# is_seamless=False,
# temperature=temperature,
# top_k=top_k,
# supercondition_factor=supercondition_factor,
# is_verbose=is_verbose
# )
# for image in image_stream:
# yield self.images_from_image(image)
# def generate_images(
# self,
# text: str,
# seed: int = -1,
# image_count: int = 1,
# temperature: float = 1,
# top_k: int = 1024,
# supercondition_factor: int = 16,
# is_verbose: bool = False
# ) -> FloatTensor:
# images_stream = self.generate_images_stream(
# text=text,
# seed=seed,
# image_count=image_count,
# temperature=temperature,
# progressive_outputs=False,
# top_k=top_k,
# supercondition_factor=supercondition_factor,
# is_verbose=is_verbose
# )
# return next(images_stream)
def generate_images(self, *args, **kwargs) -> Image.Image:
images_stream = self.generate_images_stream(
*args, **kwargs,
progressive_outputs=False
)
return next(images_stream)

2
replicate_predictor.py
View File

@ -20,6 +20,7 @@ class ReplicatePredictor(BasePredictor):
text: str = Input(default='Dali painting of WALL·E'),
save_as_png: bool = Input(default=False),
progressive_outputs: bool = Input(default=True),
seamless: bool = Input(default=False),
grid_size: int = Input(ge=1, le=9, default=5),
temperature: str = Input(
choices=(
@ -45,6 +46,7 @@ class ReplicatePredictor(BasePredictor):
seed = -1,
grid_size = grid_size,
progressive_outputs = progressive_outputs,
is_seamless=seamless,
temperature = eval(temperature),
supercondition_factor = float(supercondition_factor),
top_k = top_k,

2
setup.py
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@ -5,7 +5,7 @@ setuptools.setup(
name='min-dalle',
description = 'min(DALL·E)',
# long_description=(Path(__file__).parent / "README.rst").read_text(),
version='0.3.15',
version='0.3.16',
author='Brett Kuprel',
author_email='brkuprel@gmail.com',
url='https://github.com/kuprel/min-dalle',

2
tkinter_ui.py
View File

@ -101,7 +101,7 @@ def generate():
label_image.update()
def save():
final_image.save('out.png')
final_image.save('generated/out.png')
frm = ttk.Frame(root, padding=16)
frm.grid()