151 lines
7.3 KiB
Python
151 lines
7.3 KiB
Python
import os
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import torch
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from torch.nn import functional as F
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from contextlib import nullcontext
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from omegaconf import OmegaConf
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import comfy.model_management
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import folder_paths
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from nodes import ImageScaleBy
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import torch.cuda
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from .sgm.util import instantiate_from_config
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script_directory = os.path.dirname(os.path.abspath(__file__))
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class SUPIR_Upscale:
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def __init__(self):
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self.current_sdxl_model = None
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self.upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"supir_model": (folder_paths.get_filename_list("checkpoints"), ),
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"sdxl_model": (folder_paths.get_filename_list("checkpoints"), ),
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"image": ("IMAGE", ),
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"seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}),
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"resize_method": (s.upscale_methods, {"default": "lanczos"}),
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"scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 20.0, "step": 0.01}),
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"steps": ("INT", {"default": 45, "min": 3, "max": 4096, "step": 1}),
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"restoration_scale": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 6.0, "step": 1.0}),
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"cfg_scale": ("FLOAT", {"default": 7.5,"min": 0, "max": 20, "step": 0.01}),
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"a_prompt": ("STRING", {"multiline": True, "default": "high quality, detailed",}),
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"n_prompt": ("STRING", {"multiline": True, "default": "bad quality, blurry, messy",}),
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"s_churn": ("INT", {"default": 5,"min": 0, "max": 40, "step": 1}),
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"s_noise": ("FLOAT", {"default": 1.003,"min": 1.0, "max": 1.1, "step": 0.001}),
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"control_scale": ("FLOAT", {"default": 1.0, "min": 0, "max": 1, "step": 0.05}),
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"cfg_scale_start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 9.0, "step": 0.05}),
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"control_scale_start": ("FLOAT", {"default": 0.0, "min": 0, "max": 1.0, "step": 0.05}),
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"color_fix_type": (
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[
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'None',
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'AdaIn',
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'Wavelet',
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], {
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"default": 'AdaIn'
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}),
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"keep_model_loaded": ("BOOLEAN", {"default": True}),
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"use_tiled_vae": ("BOOLEAN", {"default": True}),
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"encoder_tile_size_pixels": ("INT", {"default": 512, "min": 64, "max": 8192, "step": 64}),
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"decoder_tile_size_latent": ("INT", {"default": 64, "min": 64, "max": 8192, "step": 64}),
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},
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"optional": {
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"captions": ("STRING", {"forceInput": True, "multiline": False, "default": "",}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES =("upscaled_image",)
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FUNCTION = "process"
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CATEGORY = "SUPIR"
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def process(self, steps, image, color_fix_type, seed, scale_by, cfg_scale, resize_method, s_churn, s_noise, encoder_tile_size_pixels, decoder_tile_size_latent,
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control_scale, cfg_scale_start, control_scale_start, restoration_scale, keep_model_loaded,
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a_prompt, n_prompt, sdxl_model, supir_model, use_tiled_vae, captions=""):
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device = comfy.model_management.get_torch_device()
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image = image.to(device)
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SUPIR_MODEL_PATH = folder_paths.get_full_path("checkpoints", supir_model)
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SDXL_MODEL_PATH = folder_paths.get_full_path("checkpoints", sdxl_model)
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config_path = os.path.join(script_directory, "options/SUPIR_v0.yaml")
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if comfy.model_management.should_use_bf16():
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print("Using bf16")
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dtype = torch.bfloat16
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vae_dtype = 'bf16'
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model_dtype = 'bf16'
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elif comfy.model_management.should_use_fp16():
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print("Using fp16")
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dtype = torch.float16
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vae_dtype = 'fp32'
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model_dtype = 'fp16'
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else:
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print("Using fp32")
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dtype = torch.float32
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vae_dtype = 'fp32'
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model_dtype = 'fp32'
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if not hasattr(self, "model") or self.model is None or self.current_sdxl_model != sdxl_model:
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self.current_sdxl_model = sdxl_model
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config = OmegaConf.load(config_path)
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config.model.params.ae_dtype = vae_dtype
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config.model.params.diffusion_dtype = model_dtype
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self.model = instantiate_from_config(config.model).cpu()
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print(type(self.model))
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from .SUPIR.util import load_state_dict
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supir_state_dict = load_state_dict(SUPIR_MODEL_PATH)
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sdxl_state_dict = load_state_dict(SDXL_MODEL_PATH)
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self.model.load_state_dict(supir_state_dict, strict=False)
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self.model.load_state_dict(sdxl_state_dict, strict=False)
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self.model.to(device).to(dtype)
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if use_tiled_vae:
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self.model.init_tile_vae(encoder_tile_size=encoder_tile_size_pixels, decoder_tile_size=decoder_tile_size_latent, reset=False)
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else:
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self.model.init_tile_vae(encoder_tile_size=encoder_tile_size_pixels, decoder_tile_size=decoder_tile_size_latent, reset=True)
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autocast_condition = dtype == torch.float16 or torch.bfloat16 and not comfy.model_management.is_device_mps(device)
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with torch.autocast(comfy.model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
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image, = ImageScaleBy.upscale(self, image, resize_method, scale_by)
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B, H, W, C = image.shape
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new_height = H // 64 * 64
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new_width = W // 64 * 64
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image = image.permute(0, 3, 1, 2).contiguous()
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resized_image = F.interpolate(image, size=(new_height, new_width), mode='bicubic', align_corners=False)
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captions_list = []
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captions_list.append(captions)
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print(captions_list)
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use_linear_CFG = cfg_scale_start > 0
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use_linear_control_scale = control_scale_start > 0
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out = []
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pbar = comfy.utils.ProgressBar(B)
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for i in range(B):
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# # step 3: Diffusion Process
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samples = self.model.batchify_sample(resized_image[i].unsqueeze(0), captions_list, num_steps=steps, restoration_scale= restoration_scale, s_churn=s_churn,
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s_noise=s_noise, cfg_scale=cfg_scale, control_scale=control_scale, seed=seed,
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num_samples=1, p_p=a_prompt, n_p=n_prompt, color_fix_type=color_fix_type,
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use_linear_CFG=use_linear_CFG, use_linear_control_scale=use_linear_control_scale,
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cfg_scale_start=cfg_scale_start, control_scale_start=control_scale_start)
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out.append(samples.squeeze(0).cpu())
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print("Sampled image ", i, " out of ", B)
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pbar.update(1)
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if not keep_model_loaded:
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self.model = None
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out_stacked = torch.stack(out, dim=0).cpu().to(torch.float32).permute(0, 2, 3, 1)
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return(out_stacked,)
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NODE_CLASS_MAPPINGS = {
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"SUPIR_Upscale": SUPIR_Upscale,
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"SUPIR_Upscale": SUPIR_Upscale
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"SUPIR_Upscale": "SUPIR_Upscale",
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"SUPIR_Upscale": "SUPIR_Upscale"
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} |