74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
from .instruct_ir_model import InstructIRModel
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from PIL import Image
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import numpy as np
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import torch
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def pil2tensor(image):
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return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
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def tensor2pil(image):
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return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
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class LoadInstructIRModel:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"device": (["cpu", "cuda", ], {"default": "cuda"}),
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}
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}
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RETURN_TYPES = ("INSTRUCTIR_MODEL",)
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FUNCTION = "get_model"
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CATEGORY = "fofo"
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def get_model(self, device):
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return (InstructIRModel(device=device),)
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class InstructIRProcess:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": ("INSTRUCTIR_MODEL", ),
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"image": ("IMAGE", "IMAGE_URL"),
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"prompt": ([
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"please I want this image for my photo album, can you edit it as a photographer",
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"my image is too dark, can you fix it?",
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"How can I remove the fog and mist from this photo?",
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"enhance the colors",
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"I need to enhance the size and quality of this image."
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], {
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"default": "enhance the colors",
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"multiline": True,
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}),
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"custom_prompt": ("STRING", {
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"default": "",
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"multiline": True,
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}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "get_value"
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CATEGORY = "fofo"
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def get_value(self, model, image, prompt, custom_prompt):
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# image = Image.fromarray(np.clip(255. * image[0].cpu().numpy(),0,255).astype(np.uint8))
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if len(custom_prompt.strip())>0:
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prompt = custom_prompt
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print(f"prompt: {prompt}")
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output_image = model.process_img(tensor2pil(image[0]), prompt)
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output_image = output_image.convert("RGB")
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return (pil2tensor(output_image),)
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