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https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
factor out exception handler
This commit is contained in:
Kevin Gibbons
parent
0eba55ddbc
commit
a10baf5808
226
ldm/simplet2i.py
226
ldm/simplet2i.py
@ -252,24 +252,37 @@ The vast majority of these arguments default to reasonable values.
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data = [batch_size * [prompt]]
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scope = autocast if self.precision=="autocast" else nullcontext
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tic = time.time()
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if init_img:
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assert os.path.exists(init_img),f'{init_img}: File not found'
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results = self._img2img(prompt,
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data=data,precision_scope=scope,
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batch_size=batch_size,iterations=iterations,
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steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
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skip_normalize=skip_normalize,
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init_img=init_img,strength=strength,variants=variants,
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callback=image_callback)
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else:
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results = self._txt2img(prompt,
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data=data,precision_scope=scope,
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batch_size=batch_size,iterations=iterations,
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steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
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skip_normalize=skip_normalize,
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width=width,height=height,
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callback=image_callback)
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tic = time.time()
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results = list()
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def prompt_callback(image, seed):
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results.append([image, seed])
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if image_callback is not None:
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image_callback(image, seed)
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try:
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if init_img:
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assert os.path.exists(init_img),f'{init_img}: File not found'
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self._img2img(prompt,
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data=data,precision_scope=scope,
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batch_size=batch_size,iterations=iterations,
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steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
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skip_normalize=skip_normalize,
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init_img=init_img,strength=strength,variants=variants,
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callback=prompt_callback)
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else:
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self._txt2img(prompt,
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data=data,precision_scope=scope,
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batch_size=batch_size,iterations=iterations,
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steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
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skip_normalize=skip_normalize,
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width=width,height=height,
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callback=prompt_callback)
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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'{len(results)} images generated in',"%4.2fs"% (toc-tic))
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return results
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@ -292,59 +305,53 @@ The vast majority of these arguments default to reasonable values.
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image_count = 0
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# Gawd. Too many levels of indent here. Need to refactor into smaller routines!
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try:
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with precision_scope(self.device.type), self.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 = self.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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with precision_scope(self.device.type), self.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 = self.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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# weighted sub-prompts
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subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
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if len(subprompts) > 1:
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# i dont know if this is correct.. but it works
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c = torch.zeros_like(uc)
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# get total weight for normalizing
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totalWeight = sum(weights)
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# normalize each "sub prompt" and add it
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for i in range(0,len(subprompts)):
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weight = weights[i]
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if not skip_normalize:
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weight = weight / totalWeight
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c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
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else: # just standard 1 prompt
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c = self.model.get_learned_conditioning(prompts)
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# weighted sub-prompts
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subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
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if len(subprompts) > 1:
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# i dont know if this is correct.. but it works
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c = torch.zeros_like(uc)
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# get total weight for normalizing
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totalWeight = sum(weights)
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# normalize each "sub prompt" and add it
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for i in range(0,len(subprompts)):
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weight = weights[i]
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if not skip_normalize:
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weight = weight / totalWeight
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c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
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else: # just standard 1 prompt
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c = self.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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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_samples_ddim = self.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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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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image = Image.fromarray(x_sample.astype(np.uint8))
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images.append([image,seed])
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if callback is not None:
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callback(image,seed)
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x_samples_ddim = self.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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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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image = Image.fromarray(x_sample.astype(np.uint8))
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images.append([image,seed])
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if callback is not None:
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callback(image,seed)
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seed = self._new_seed()
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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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seed = self._new_seed()
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return images
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@ -379,57 +386,50 @@ The vast majority of these arguments default to reasonable values.
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# print(f"target t_enc is {t_enc} steps")
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images = list()
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try:
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with precision_scope(self.device.type), self.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 = self.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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with precision_scope(self.device.type), self.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 = self.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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# weighted sub-prompts
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subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
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if len(subprompts) > 1:
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# i dont know if this is correct.. but it works
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c = torch.zeros_like(uc)
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# get total weight for normalizing
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totalWeight = sum(weights)
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# normalize each "sub prompt" and add it
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for i in range(0,len(subprompts)):
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weight = weights[i]
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if not skip_normalize:
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weight = weight / totalWeight
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c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
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else: # just standard 1 prompt
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c = self.model.get_learned_conditioning(prompts)
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# weighted sub-prompts
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subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
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if len(subprompts) > 1:
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# i dont know if this is correct.. but it works
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c = torch.zeros_like(uc)
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# get total weight for normalizing
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totalWeight = sum(weights)
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# normalize each "sub prompt" and add it
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for i in range(0,len(subprompts)):
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weight = weights[i]
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if not skip_normalize:
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weight = weight / totalWeight
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c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
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else: # just standard 1 prompt
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c = self.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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# 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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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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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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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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image = Image.fromarray(x_sample.astype(np.uint8))
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images.append([image,seed])
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if callback is not None:
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callback(image,seed)
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seed = self._new_seed()
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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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image = Image.fromarray(x_sample.astype(np.uint8))
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images.append([image,seed])
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if callback is not None:
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callback(image,seed)
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seed = self._new_seed()
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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("Oops! A runtime error has occurred. If this is unexpected, please copy-and-paste this stack trace and post it as an Issue to http://github.com/lstein/stable-diffusion")
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traceback.print_exc()
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return images
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def _new_seed(self):
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