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https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
start support for 1.5 inpainting model, not complete
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@ -404,7 +404,10 @@ class Generate:
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)
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# TODO: Hacky selection of operation to perform. Needs to be refactored.
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if (init_image is not None) and (mask_image is not None):
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if self.sampler.conditioning_key() in ('hybrid','concat'):
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print(f'** Inpainting model detected. Will try it! **')
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generator = self._make_omnibus()
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elif (init_image is not None) and (mask_image is not None):
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generator = self._make_inpaint()
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elif (embiggen != None or embiggen_tiles != None):
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generator = self._make_embiggen()
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@ -690,6 +693,12 @@ class Generate:
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self.generators['inpaint'] = Inpaint(self.model, self.precision)
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return self.generators['inpaint']
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def _make_omnibus(self):
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if not self.generators.get('omnibus'):
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from ldm.invoke.generator.omnibus import Omnibus
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self.generators['omnibus'] = Omnibus(self.model, self.precision)
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return self.generators['omnibus']
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def load_model(self):
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'''
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preload model identified in self.model_name
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@ -40,12 +40,13 @@ class Generator():
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self.variation_amount = variation_amount
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self.with_variations = with_variations
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def generate(self,prompt,init_image,width,height,iterations=1,seed=None,
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def generate(self,prompt,init_image,width,height,sampler, iterations=1,seed=None,
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image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
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**kwargs):
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scope = choose_autocast(self.precision)
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make_image = self.get_make_image(
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make_image = self.get_make_image(
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prompt,
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sampler = sampler,
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init_image = init_image,
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width = width,
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height = height,
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@ -54,13 +55,16 @@ class Generator():
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perlin = perlin,
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**kwargs
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)
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results = []
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seed = seed if seed is not None else self.new_seed()
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first_seed = seed
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seed, initial_noise = self.generate_initial_noise(seed, width, height)
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with scope(self.model.device.type), self.model.ema_scope():
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scope = (scope(self.model.device.type), self.model.ema_scope()) if sampler.conditioning_key() not in ('hybrid','concat') else scope(self.model.device.type)
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with scope:
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for n in trange(iterations, desc='Generating'):
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print('DEBUG: in iterations loop() called')
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x_T = None
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if self.variation_amount > 0:
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seed_everything(seed)
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@ -75,7 +79,6 @@ class Generator():
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x_T = self.get_noise(width,height)
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except:
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pass
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image = make_image(x_T)
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results.append([image, seed])
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if image_callback is not None:
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@ -83,10 +86,10 @@ class Generator():
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seed = self.new_seed()
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return results
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def sample_to_image(self,samples):
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def sample_to_image(self,samples)->Image.Image:
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"""
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Returns a function returning an image derived from the prompt and the initial image
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Return value depends on the seed at the time you call it
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Given samples returned from a sampler, converts
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it into a PIL Image
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"""
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x_samples = self.model.decode_first_stage(samples)
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x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
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@ -13,6 +13,7 @@ import gc
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import hashlib
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import psutil
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import transformers
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import traceback
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import os
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from sys import getrefcount
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from omegaconf import OmegaConf
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@ -73,6 +74,7 @@ class ModelCache(object):
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self.models[model_name]['hash'] = hash
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except Exception as e:
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print(f'** model {model_name} could not be loaded: {str(e)}')
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print(traceback.format_exc())
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print(f'** restoring {self.current_model}')
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self.get_model(self.current_model)
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return None
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@ -66,7 +66,7 @@ class VQModel(pl.LightningModule):
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self.use_ema = use_ema
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if self.use_ema:
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self.model_ema = LitEma(self)
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print(f'Keeping EMAs of {len(list(self.model_ema.buffers()))}.')
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print(f'>> Keeping EMAs of {len(list(self.model_ema.buffers()))}.')
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if ckpt_path is not None:
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self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
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@ -41,7 +41,19 @@ class DDIMSampler(Sampler):
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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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c_in = torch.cat([unconditional_conditioning, c])
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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 * (
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e_t - e_t_uncond
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@ -19,6 +19,7 @@ from functools import partial
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from tqdm import tqdm
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from torchvision.utils import make_grid
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from pytorch_lightning.utilities.distributed import rank_zero_only
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from omegaconf import ListConfig
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import urllib
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from ldm.util import (
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@ -120,7 +121,7 @@ class DDPM(pl.LightningModule):
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self.use_ema = use_ema
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if self.use_ema:
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self.model_ema = LitEma(self.model)
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print(f'Keeping EMAs of {len(list(self.model_ema.buffers()))}.')
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print(f' | Keeping EMAs of {len(list(self.model_ema.buffers()))}.')
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self.use_scheduler = scheduler_config is not None
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if self.use_scheduler:
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@ -1883,6 +1884,24 @@ class LatentDiffusion(DDPM):
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return samples, intermediates
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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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@torch.no_grad()
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def log_images(
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self,
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@ -2138,6 +2157,7 @@ class DiffusionWrapper(pl.LightningModule):
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]
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def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
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print(f'DEBUG (ddpm) c_concat = {c_concat}')
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if self.conditioning_key is None:
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out = self.diffusion_model(x, t)
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elif self.conditioning_key == 'concat':
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@ -2147,8 +2167,8 @@ class DiffusionWrapper(pl.LightningModule):
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cc = torch.cat(c_crossattn, 1)
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out = self.diffusion_model(x, t, context=cc)
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elif self.conditioning_key == 'hybrid':
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xc = torch.cat([x] + c_concat, dim=1)
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cc = torch.cat(c_crossattn, 1)
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xc = torch.cat([x] + c_concat, dim=1)
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out = self.diffusion_model(xc, t, context=cc)
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elif self.conditioning_key == 'adm':
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cc = c_crossattn[0]
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@ -2187,3 +2207,58 @@ class Layout2ImgDiffusion(LatentDiffusion):
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cond_img = torch.stack(bbox_imgs, dim=0)
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logs['bbox_image'] = cond_img
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return logs
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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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finetune_keys=None,
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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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@torch.no_grad()
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def get_input(
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self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False
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):
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# note: restricted to non-trainable encoders currently
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assert (
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not self.cond_stage_trainable
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), "trainable cond stages not yet supported for inpainting"
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z, c, x, xrec, xc = super().get_input(
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batch,
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self.first_stage_key,
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return_first_stage_outputs=True,
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force_c_encode=True,
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return_original_cond=True,
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bs=bs,
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)
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assert exists(self.concat_keys)
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c_cat = list()
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for ck in self.concat_keys:
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cc = (
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rearrange(batch[ck], "b h w c -> b c h w")
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.to(memory_format=torch.contiguous_format)
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.float()
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)
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if bs is not None:
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cc = cc[:bs]
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cc = cc.to(self.device)
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bchw = z.shape
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if ck != self.masked_image_key:
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cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
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else:
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cc = self.get_first_stage_encoding(self.encode_first_stage(cc))
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c_cat.append(cc)
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c_cat = torch.cat(c_cat, dim=1)
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all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
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if return_first_stage_outputs:
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return z, all_conds, x, xrec, xc
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return z, all_conds
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@ -281,3 +281,5 @@ class KSampler(Sampler):
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'''
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return self.model.inner_model.q_sample(x0,ts)
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def conditioning_key(self)->str:
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return self.model.inner_model.model.conditioning_key
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@ -158,6 +158,18 @@ class Sampler(object):
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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 = ctmp[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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# check to see if make_schedule() has run, and if not, run it
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if self.ddim_timesteps is None:
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self.make_schedule(
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@ -193,7 +205,7 @@ class Sampler(object):
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)
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return samples, intermediates
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#torch.no_grad()
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@torch.no_grad()
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def do_sampling(
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self,
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cond,
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@ -307,6 +319,19 @@ class Sampler(object):
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mask = None,
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):
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print(f'DEBUG(sampler): cond = {cond}')
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if cond is not None:
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if isinstance(cond, dict):
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ctmp = cond[list(cond.keys())[0]]
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while isinstance(ctmp, list):
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ctmp = ctmp[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} conds but batch-size is {batch_size}")
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else:
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if cond.shape[0] != batch_size:
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print(f"Warning: Got {cond.shape[0]} conditionings but batch-size is {batch_size}")
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timesteps = (
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np.arange(self.ddpm_num_timesteps)
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if use_original_steps
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@ -411,3 +436,6 @@ class Sampler(object):
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return self.model.inner_model.q_sample(x0,ts)
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'''
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return self.model.q_sample(x0,ts)
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def conditioning_key(self)->str:
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return self.model.model.conditioning_key
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