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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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Added support for RunwayML inpainting model
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random_thoughtss
208
modules/sd_hijack_inpainting.py
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208
modules/sd_hijack_inpainting.py
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import torch
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import numpy as np
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from tqdm import tqdm
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from einops import rearrange, repeat
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from omegaconf import ListConfig
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from types import MethodType
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import ldm.models.diffusion.ddpm
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import ldm.models.diffusion.ddim
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from ldm.models.diffusion.ddpm import LatentDiffusion
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from ldm.models.diffusion.ddim import DDIMSampler, noise_like
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# =================================================================================================
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# Monkey patch DDIMSampler methods from RunwayML repo directly.
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# Adapted from:
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# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
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# =================================================================================================
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@torch.no_grad()
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def sample(
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self,
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S,
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batch_size,
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shape,
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conditioning=None,
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callback=None,
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normals_sequence=None,
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img_callback=None,
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quantize_x0=False,
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eta=0.,
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mask=None,
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x0=None,
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temperature=1.,
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noise_dropout=0.,
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score_corrector=None,
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corrector_kwargs=None,
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verbose=True,
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x_T=None,
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log_every_t=100,
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unconditional_guidance_scale=1.,
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unconditional_conditioning=None,
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# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
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**kwargs
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):
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if conditioning is not None:
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if isinstance(conditioning, dict):
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ctmp = conditioning[list(conditioning.keys())[0]]
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while isinstance(ctmp, list):
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ctmp = elf.inpainting_fill == 2:
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self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
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elif self.inpainting_fill == 3:
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self.init_latent = self.init_latent * self.mask
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if self.image_mask is not None:
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conditioning_mask = np.array(self.image_mask.convert("L"))
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conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
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conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
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# Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
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conditioning_mask = torch.round(conditioning_mask)
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else:
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conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
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# Create another latent image, this time with a masked version of the original input.
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conditioning_mask = conditioning_mask.to(image.device)
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conditioning_image = image * (1.0 - conditioning_mask)
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conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
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# Create the concatenated conditioning tensor to be fed to `c_concat`
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conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
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conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
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self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
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self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
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x = create_random_tensors([opctmp[0]
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cbs = ctmp.shape[0]
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if cbs != batch_size:
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print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
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else:
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if conditioning.shape[0] != batch_size:
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print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
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self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
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# sampling
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C, H, W = shape
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size = (batch_size, C, H, W)
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print(f'Data shape for DDIM sampling is {size}, eta {eta}')
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samples, intermediates = self.ddim_sampling(conditioning, size,
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callback=callback,
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img_callback=img_callback,
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quantize_denoised=quantize_x0,
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mask=mask, x0=x0,
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ddim_use_original_steps=False,
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noise_dropout=noise_dropout,
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temperature=temperature,
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score_corrector=score_corrector,
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corrector_kwargs=corrector_kwargs,
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x_T=x_T,
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log_every_t=log_every_t,
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unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=unconditional_conditioning,
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)
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return samples, intermediates
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@torch.no_grad()
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def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
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temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
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unconditional_guidance_scale=1., unconditional_conditioning=None):
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b, *_, device = *x.shape, x.device
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if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
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e_t = self.model.apply_model(x, t, c)
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else:
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x_in = torch.cat([x] * 2)
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t_in = torch.cat([t] * 2)
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if isinstance(c, dict):
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assert isinstance(unconditional_conditioning, dict)
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c_in = dict()
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for k in c:
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if isinstance(c[k], list):
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c_in[k] = [
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torch.cat([unconditional_conditioning[k][i], c[k][i]])
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for i in range(len(c[k]))
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]
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else:
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c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
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else:
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c_in = torch.cat([unconditional_conditioning, c])
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e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
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e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
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if score_corrector is not None:
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assert self.model.parameterization == "eps"
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e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
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alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
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alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
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sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
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sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
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# select parameters corresponding to the currently considered timestep
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a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
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a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
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sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
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sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
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# current prediction for x_0
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pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
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if quantize_denoised:
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pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
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# direction pointing to x_t
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dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
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noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
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if noise_dropout > 0.:
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noise = torch.nn.functional.dropout(noise, p=noise_dropout)
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x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
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return x_prev, pred_x0
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# =================================================================================================
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# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
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# Adapted from:
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# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddpm.py
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# =================================================================================================
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@torch.no_grad()
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def get_unconditional_conditioning(self, batch_size, null_label=None):
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if null_label is not None:
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xc = null_label
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if isinstance(xc, ListConfig):
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xc = list(xc)
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if isinstance(xc, dict) or isinstance(xc, list):
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c = self.get_learned_conditioning(xc)
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else:
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if hasattr(xc, "to"):
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xc = xc.to(self.device)
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c = self.get_learned_conditioning(xc)
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else:
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# todo: get null label from cond_stage_model
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raise NotImplementedError()
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c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
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return c
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class LatentInpaintDiffusion(LatentDiffusion):
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def __init__(
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self,
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concat_keys=("mask", "masked_image"),
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masked_image_key="masked_image",
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*args,
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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self.masked_image_key = masked_image_key
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assert self.masked_image_key in concat_keys
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self.concat_keys = concat_keys
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def should_hijack_inpainting(checkpoint_info):
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return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml")
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def do_inpainting_hijack():
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ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
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ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
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ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
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ldm.models.diffusion.ddim.DDIMSampler.sample = sample
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