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Added caching to image sizes so we dont do it every time.

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This commit is contained in:
Jaret Burkett
2024-07-15 19:07:41 -06:00
parent e4558dff4b
commit 58dffd43a8
7 changed files with 90 additions and 34 deletions
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14
testing/merge_in_text_encoder_adapter.py
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@@ -11,10 +11,10 @@ import json
# te_path = "google/flan-t5-xl"
# te_aug_path = "/mnt/Train/out/ip_adapter/t5xx_sd15_v1/t5xx_sd15_v1_000032000.safetensors"
# output_path = "/home/jaret/Dev/models/hf/kl-f16-d42_sd15_t5xl_raw"
model_path = "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS"
te_path = "google/flan-t5-large"
te_aug_path = "/home/jaret/Dev/models/tmp/pixart_sigma_t5l_000034000.safetensors"
output_path = "/home/jaret/Dev/models/hf/PixArt-Sigma-XL-2-512_MS_t5large_raw"
model_path = "/home/jaret/Dev/models/hf/objective-reality-16ch"
te_path = "google/flan-t5-xl"
te_aug_path = "/mnt/Train2/out/ip_adapter/t5xl-sd15-16ch_v1/t5xl-sd15-16ch_v1_000115000.safetensors"
output_path = "/home/jaret/Dev/models/hf/t5xl-sd15-16ch_sd15_v1"
print("Loading te adapter")
@@ -28,13 +28,13 @@ is_pixart = "pixart" in model_path.lower()
pipeline_class = StableDiffusionPipeline
transformer = PixArtTransformer2DModel.from_pretrained('PixArt-alpha/PixArt-Sigma-XL-2-512-MS', subfolder='transformer', torch_dtype=torch.float16)
# transformer = PixArtTransformer2DModel.from_pretrained('PixArt-alpha/PixArt-Sigma-XL-2-512-MS', subfolder='transformer', torch_dtype=torch.float16)
if is_pixart:
pipeline_class = PixArtSigmaPipeline
if is_diffusers:
sd = pipeline_class.from_pretrained(model_path, transformer=transformer, torch_dtype=torch.float16)
sd = pipeline_class.from_pretrained(model_path, torch_dtype=torch.float16)
else:
sd = pipeline_class.from_single_file(model_path, torch_dtype=torch.float16)
@@ -50,7 +50,7 @@ if is_pixart:
unet = sd.transformer
unet_sd = sd.transformer.state_dict()
else:
unet = sd.transformer
unet = sd.unet
unet_sd = sd.unet.state_dict()

6
testing/test_bucket_dataloader.py
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@@ -187,7 +187,7 @@ for epoch in range(args.epochs):
batch: 'DataLoaderBatchDTO'
img_batch = batch.tensor
img_batch = color_block_imgs(img_batch, neg1_1=True)
# img_batch = color_block_imgs(img_batch, neg1_1=True)
chunks = torch.chunk(img_batch, batch_size, dim=0)
# put them so they are size by side
@@ -208,9 +208,9 @@ for epoch in range(args.epochs):
# convert to image
img = transforms.ToPILImage()(big_img)
show_img(img)
# show_img(img)
time.sleep(1.0)
# time.sleep(1.0)
# if not last epoch
if epoch < args.epochs - 1:
trigger_dataloader_setup_epoch(dataloader)