ssit
aba0ccc2cf
Speed and image quality should be improved. Borrowed functions from A1111 for similar behavior.
104 lines
3.4 KiB
Python
104 lines
3.4 KiB
Python
# Patched classes to adapt from A111 webui for ComfyUI
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from nodes import common_ksampler, VAEEncode, VAEDecode
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from utils import pil_to_tensor, tensor_to_pil, get_mask_region, expand_crop_region, resize_image
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import modules.shared as shared
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import numpy as np
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import torch
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from PIL import Image, ImageFilter
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class StableDiffusionProcessing:
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def __init__(self, init_img, model, positive, negative, vae, seed, steps, cfg, sampler_name, scheduler, denoise):
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# Variables used by the upscaler script
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self.init_images = [init_img]
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self.image_mask = None
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self.mask_blur = 0
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self.inpaint_full_res_padding = 0
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self.width = 0
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self.height = 0
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# ComfyUI Sampler inputs
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self.model = model
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self.positive = positive
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self.negative = negative
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self.vae = vae
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self.seed = seed
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self.steps = steps
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self.cfg = cfg
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self.sampler_name = sampler_name
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self.scheduler = scheduler
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self.denoise = denoise
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# Other required A1111 variables for the upscaler script that is currently unused in this script
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self.extra_generation_params = {}
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class Processed:
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def __init__(self, p: StableDiffusionProcessing, images: list, seed: int, info: str):
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self.images = images
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self.seed = seed
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self.info = info
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def infotext(self, p: StableDiffusionProcessing, index):
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return None
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def fix_seed(p: StableDiffusionProcessing):
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pass
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def process_images(p: StableDiffusionProcessing) -> Processed:
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# Where the main image generation happens in A1111
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# Setup
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image_mask = p.image_mask.convert('L')
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init_image = p.init_images[0]
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# Blur the mask
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if p.mask_blur > 0:
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image_mask = image_mask.filter(ImageFilter.GaussianBlur(p.mask_blur))
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# Locate the white region of the mask outlining the tile and add padding
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crop_region = get_mask_region(image_mask, p.inpaint_full_res_padding)
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crop_region = expand_crop_region(crop_region, p.width, p.height, image_mask.width, image_mask.height)
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# Crop the init_image to get the tile that will be used for generation
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tile = init_image.crop(crop_region)
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initial_tile_size = tile.size
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tile = resize_image(tile, p.width, p.height)
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# Encode the image
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vae_encoder = VAEEncode()
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(latent,) = vae_encoder.encode(p.vae, pil_to_tensor(tile))
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# Generate samples
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(samples,) = common_ksampler(p.model, p.seed, p.steps, p.cfg, p.sampler_name,
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p.scheduler, p.positive, p.negative, latent, denoise=p.denoise)
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# Decode the sample
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vae_decoder = VAEDecode()
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(decoded,) = vae_decoder.decode(p.vae, samples)
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# Convert the sample to a PIL image
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tile_sampled = tensor_to_pil(decoded)
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# Resize back to the original size
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tile_sampled = resize_image(tile_sampled, initial_tile_size[0], initial_tile_size[1])
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# Put the tile into position
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image_tile_only = Image.new('RGB', init_image.size)
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image_tile_only.paste(tile_sampled, crop_region[:2])
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# Add the mask as an alpha channel
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image_tile_only.putalpha(image_mask)
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# Add back the tile to the initial image according to the mask in the alpha channel
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result = init_image.convert('RGBA')
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result.alpha_composite(image_tile_only)
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# Return the original image instead of the generated image because the masked parts of the image are noised
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processed = Processed(p, [result], p.seed, None)
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return processed
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