reser

.gitmodules

@@ -1,3 +0,0 @@
-[submodule "cicdiff"]
-	path = color
-	url = https://github.com/ErwannMillon/Color-diffusion.git

app.py

@@ -1,53 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-from PLModel import PLColorDiff
-from dataset import ColorizationDataset
-from utils import lab_to_rgb, split_lab
-import torch
-import default_configs
-from icecream import ic
-
-from unet import SimpleUnet
-def get_image(image):
-    print(image)
-    dataset = ColorizationDataset([image], split="val", config=conf, size=128)
-    lab_img = dataset.get_tensor_from_path(image) 
-    batch = lab_img.unsqueeze(0)
-    # x_l, _ = split_lab(batch)
-    # bw = torch.cat((x_l, *[torch.zeros_like(x_l)] * 2), dim=1)
-    model.eval()
-    img = model.sample_plot_image(batch)
-    rgb_img = lab_to_rgb(*split_lab(img))
-    # model.test_step(batch)
-    return(rgb_img[0])
-conf = SimpleUnetConfig = dict (
-    # device = get_device(),
-    device = "mps",
-    pin_memory = torch.cuda.is_available(),
-    T=300,
-    lr=5e-4,
-    batch_size=64,
-    img_size = 128,
-    sample=False,
-    log=False,
-    should_log=False,
-    sample_fn = None,
-    val_every=20,
-    epochs=100,
-    using_cond=False
-)
-ckpt_path = "checkpoints/epoch=1-step=706.ckpt"
-ckpt = torch.load(ckpt_path, map_location=torch.device("mps"))
-unet = SimpleUnet()
-model = PLColorDiff(unet, None, None)
-ic.disable()
-model.load_state_dict(ckpt["state_dict"])
-
-demo = gr.Interface(
-    get_image,
-    inputs=gr.inputs.Image(label="Upload a black and white face", type="filepath"),
-    outputs="image",
-    title="Upload a black and white face and get a colorized image!",
-)
-
-demo.launch()

attn.py

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-color/attn.py

aws

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-color/aws

color

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-Subproject commit fd4b3285315dd8e0e612ffca7d7ecd02f6569f2d

requirements.txt

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-color/requirements.txt