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https://github.com/openvinotoolkit/stable-diffusion-webui.git
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Merge remote-tracking branch 'origin/master'
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commit
5f561ee95d
@ -79,7 +79,7 @@ class StableDiffusionProcessing:
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self.paste_to = None
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self.color_corrections = None
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self.denoising_strength: float = 0
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self.sampler_noise_scheduler_override = None
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self.ddim_discretize = opts.ddim_discretize
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self.s_churn = opts.s_churn
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self.s_tmin = opts.s_tmin
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@ -130,7 +130,7 @@ class Processed:
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self.s_tmin = p.s_tmin
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self.s_tmax = p.s_tmax
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self.s_noise = p.s_noise
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self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
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self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
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self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
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self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
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@ -290,7 +290,10 @@ class KDiffusionSampler:
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
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steps, t_enc = setup_img2img_steps(p, steps)
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sigmas = self.model_wrap.get_sigmas(steps)
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if p.sampler_noise_scheduler_override:
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sigmas = p.sampler_noise_scheduler_override(steps)
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else:
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sigmas = self.model_wrap.get_sigmas(steps)
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noise = noise * sigmas[steps - t_enc - 1]
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xi = x + noise
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@ -306,7 +309,10 @@ class KDiffusionSampler:
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def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
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steps = steps or p.steps
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sigmas = self.model_wrap.get_sigmas(steps)
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if p.sampler_noise_scheduler_override:
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sigmas = p.sampler_noise_scheduler_override(steps)
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else:
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sigmas = self.model_wrap.get_sigmas(steps)
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x = x * sigmas[0]
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extra_params_kwargs = self.initialize(p)
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@ -5,6 +5,7 @@ import numpy as np
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import torch
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from PIL import Image
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from basicsr.utils.download_util import load_file_from_url
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from tqdm import tqdm
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from modules import modelloader
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from modules.paths import models_path
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@ -122,18 +123,20 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
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E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)
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W = torch.zeros_like(E, dtype=torch.half, device=device)
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for h_idx in h_idx_list:
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for w_idx in w_idx_list:
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in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
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out_patch = model(in_patch)
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out_patch_mask = torch.ones_like(out_patch)
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with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
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for h_idx in h_idx_list:
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for w_idx in w_idx_list:
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in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
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out_patch = model(in_patch)
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out_patch_mask = torch.ones_like(out_patch)
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E[
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..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
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].add_(out_patch)
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W[
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..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
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].add_(out_patch_mask)
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E[
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..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
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].add_(out_patch)
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W[
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..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
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].add_(out_patch_mask)
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pbar.update(1)
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output = E.div_(W)
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return output
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