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https://github.com/invoke-ai/InvokeAI
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Keyboard interrupt retains seed and log information in files produced prior to interrupt. Closes #21
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Lincoln Stein
parent
bc7b1fdd37
commit
710b908290
188
ldm/simplet2i.py
188
ldm/simplet2i.py
@ -186,58 +186,66 @@ The vast majority of these arguments default to reasonable values.
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images = list()
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seeds = list()
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filename = None
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image_count = 0
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tic = time.time()
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try:
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with torch.no_grad():
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with precision_scope("cuda"):
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with model.ema_scope():
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all_samples = list()
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for n in trange(iterations, desc="Sampling"):
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seed_everything(seed)
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for prompts in tqdm(data, desc="data", dynamic_ncols=True):
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uc = None
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if cfg_scale != 1.0:
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uc = model.get_learned_conditioning(batch_size * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = model.get_learned_conditioning(prompts)
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shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
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samples_ddim, _ = sampler.sample(S=steps,
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conditioning=c,
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batch_size=batch_size,
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shape=shape,
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verbose=False,
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unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=uc,
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eta=ddim_eta,
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x_T=start_code)
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x_samples_ddim = model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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if not grid:
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for x_sample in x_samples_ddim:
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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filename = self._unique_filename(outdir,previousname=filename,
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seed=seed,isbatch=(batch_size>1))
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assert not os.path.exists(filename)
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Image.fromarray(x_sample.astype(np.uint8)).save(filename)
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images.append([filename,seed])
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else:
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all_samples.append(x_samples_ddim)
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seeds.append(seed)
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image_count += 1
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seed = self._new_seed()
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if grid:
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images = self._make_grid(samples=all_samples,
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seeds=seeds,
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batch_size=batch_size,
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iterations=iterations,
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outdir=outdir)
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with torch.no_grad():
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with precision_scope("cuda"):
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with model.ema_scope():
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all_samples = list()
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for n in trange(iterations, desc="Sampling"):
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seed_everything(seed)
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for prompts in tqdm(data, desc="data", dynamic_ncols=True):
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uc = None
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if cfg_scale != 1.0:
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uc = model.get_learned_conditioning(batch_size * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = model.get_learned_conditioning(prompts)
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shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
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samples_ddim, _ = sampler.sample(S=steps,
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conditioning=c,
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batch_size=batch_size,
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shape=shape,
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verbose=False,
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unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=uc,
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eta=ddim_eta,
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x_T=start_code)
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x_samples_ddim = model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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if not grid:
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for x_sample in x_samples_ddim:
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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filename = self._unique_filename(outdir,previousname=filename,
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seed=seed,isbatch=(batch_size>1))
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assert not os.path.exists(filename)
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Image.fromarray(x_sample.astype(np.uint8)).save(filename)
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images.append([filename,seed])
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else:
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all_samples.append(x_samples_ddim)
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seeds.append(seed)
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seed = self._new_seed()
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if grid:
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images = self._make_grid(samples=all_samples,
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seeds=seeds,
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batch_size=batch_size,
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iterations=iterations,
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outdir=outdir)
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except KeyboardInterrupt:
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print('*interrupted*')
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print('Partial results will be returned; if --grid was requested, nothing will be returned.')
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except RuntimeError as e:
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print(str(e))
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toc = time.time()
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print(f'{batch_size * iterations} images generated in',"%4.2fs"% (toc-tic))
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print(f'{image_count} images generated in',"%4.2fs"% (toc-tic))
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return images
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@ -305,54 +313,60 @@ The vast majority of these arguments default to reasonable values.
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images = list()
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seeds = list()
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filename = None
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image_count = 0 # actual number of iterations performed
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tic = time.time()
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with torch.no_grad():
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with precision_scope("cuda"):
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with model.ema_scope():
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all_samples = list()
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for n in trange(iterations, desc="Sampling"):
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seed_everything(seed)
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for prompts in tqdm(data, desc="data", dynamic_ncols=True):
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uc = None
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if cfg_scale != 1.0:
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uc = model.get_learned_conditioning(batch_size * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = model.get_learned_conditioning(prompts)
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# encode (scaled latent)
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z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
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# decode it
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samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=uc,)
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try:
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with torch.no_grad():
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with precision_scope("cuda"):
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with model.ema_scope():
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all_samples = list()
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for n in trange(iterations, desc="Sampling"):
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seed_everything(seed)
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for prompts in tqdm(data, desc="data", dynamic_ncols=True):
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uc = None
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if cfg_scale != 1.0:
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uc = model.get_learned_conditioning(batch_size * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = model.get_learned_conditioning(prompts)
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x_samples = 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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# encode (scaled latent)
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z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
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# decode it
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samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=uc,)
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if not grid:
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for x_sample in x_samples:
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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filename = self._unique_filename(outdir,filename,seed=seed,isbatch=(batch_size>1))
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assert not os.path.exists(filename)
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Image.fromarray(x_sample.astype(np.uint8)).save(filename)
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images.append([filename,seed])
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else:
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all_samples.append(x_samples)
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seeds.append(seed)
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x_samples = 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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seed = self._new_seed()
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if not grid:
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for x_sample in x_samples:
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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filename = self._unique_filename(outdir,filename,seed=seed,isbatch=(batch_size>1))
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assert not os.path.exists(filename)
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Image.fromarray(x_sample.astype(np.uint8)).save(filename)
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images.append([filename,seed])
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else:
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all_samples.append(x_samples)
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seeds.append(seed)
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image_count += 1
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seed = self._new_seed()
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if grid:
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images = self._make_grid(samples=all_samples,
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seeds=seeds,
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batch_size=batch_size,
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iterations=iterations,
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outdir=outdir)
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if grid:
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images = self._make_grid(samples=all_samples,
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seeds=seeds,
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batch_size=batch_size,
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iterations=iterations,
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outdir=outdir)
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except KeyboardInterrupt:
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print('Partial results will be returned; if --grid was requested, nothing will be returned.')
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except RuntimeError as e:
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print(str(e))
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toc = time.time()
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print(f'{batch_size * iterations} images generated in',"%4.2fs"% (toc-tic))
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print(f'{image_count} images generated in',"%4.2fs"% (toc-tic))
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return images
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@ -85,7 +85,8 @@ def main():
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cmd_parser = create_cmd_parser()
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main_loop(t2i,cmd_parser,log,infile)
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log.close()
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infile.close()
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if infile:
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infile.close()
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def main_loop(t2i,parser,log,infile):
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@ -157,19 +158,12 @@ def main_loop(t2i,parser,log,infile):
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print("Try again with a prompt!")
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continue
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try:
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if opt.init_img is None:
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results = t2i.txt2img(**vars(opt))
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else:
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results = t2i.img2img(**vars(opt))
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print("Outputs:")
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write_log_message(t2i,opt,results,log)
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except KeyboardInterrupt:
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print('*interrupted*')
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continue
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except RuntimeError as e:
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print(str(e))
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continue
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if opt.init_img is None:
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results = t2i.txt2img(**vars(opt))
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else:
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results = t2i.img2img(**vars(opt))
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print("Outputs:")
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write_log_message(t2i,opt,results,log)
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print("goodbye!")
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