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https://github.com/invoke-ai/InvokeAI
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Merge branch 'resolution-checker' of https://github.com/blessedcoolant/stable-diffusion into main
This commit is contained in:
Lincoln Stein
commit
0be2351c97
@ -50,6 +50,8 @@ class InitImageResizer():
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new_image = Image.new('RGB',(width,height))
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new_image.paste(resized_image,((width-rw)//2,(height-rh)//2))
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print(f'>> Resized image size to {width}x{height}')
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return new_image
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def make_grid(image_list, rows=None, cols=None):
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@ -266,16 +266,9 @@ class T2I:
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assert (
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0.0 <= strength <= 1.0
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), 'can only work with strength in [0.0, 1.0]'
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w, h = map(
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lambda x: x - x % 64, (width, height)
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) # resize to integer multiple of 64
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if h != height or w != width:
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print(
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f'Height and width must be multiples of 64. Resizing to {h}x{w}.'
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)
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height = h
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width = w
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if not(width == self.width and height == self.height):
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width, height, _ = self._resolution_check(width, height, log=True)
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scope = autocast if self.precision == 'autocast' else nullcontext
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@ -353,8 +346,11 @@ class T2I:
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f'Error running RealESRGAN - Your image was not upscaled.\n{e}'
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)
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if image_callback is not None:
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image_callback(image, seed, upscaled=True)
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else: # no callback passed, so we simply replace old image with rescaled one
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if save_original:
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image_callback(image, seed)
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else:
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image_callback(image, seed, upscaled=True)
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else: # no callback passed, so we simply replace old image with rescaled one
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result[0] = image
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except KeyboardInterrupt:
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@ -436,7 +432,7 @@ class T2I:
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width,
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height,
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strength,
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callback, # Currently not implemented for img2img
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callback, # Currently not implemented for img2img
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):
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"""
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An infinite iterator of images from the prompt and the initial image
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@ -445,13 +441,13 @@ class T2I:
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# PLMS sampler not supported yet, so ignore previous sampler
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if self.sampler_name != 'ddim':
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print(
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f"sampler '{self.sampler_name}' is not yet supported. Using DDM sampler"
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f"sampler '{self.sampler_name}' is not yet supported. Using DDIM sampler"
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)
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sampler = DDIMSampler(self.model, device=self.device)
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else:
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sampler = self.sampler
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init_image = self._load_img(init_img,width,height).to(self.device)
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init_image = self._load_img(init_img, width, height).to(self.device)
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with precision_scope(self.device.type):
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init_latent = self.model.get_first_stage_encoding(
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self.model.encode_first_stage(init_image)
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@ -514,7 +510,8 @@ class T2I:
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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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if len(x_samples) != 1:
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raise Exception(f'expected to get a single image, but got {len(x_samples)}')
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raise Exception(
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f'expected to get a single image, but got {len(x_samples)}')
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x_sample = 255.0 * rearrange(
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x_samples[0].cpu().numpy(), 'c h w -> h w c'
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)
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@ -545,8 +542,9 @@ class T2I:
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self.model.cond_stage_model.device = self.device
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except AttributeError:
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import traceback
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print('Error loading model. Only the CUDA backend is supported',file=sys.stderr)
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print(traceback.format_exc(),file=sys.stderr)
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print(
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'Error loading model. Only the CUDA backend is supported', file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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raise SystemExit
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self._set_sampler()
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@ -606,10 +604,26 @@ class T2I:
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print(f'image path = {path}, cwd = {os.getcwd()}')
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with Image.open(path) as img:
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image = img.convert('RGB')
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print(f'loaded input image of size {image.width}x{image.height} from {path}')
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print(
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f'loaded input image of size {image.width}x{image.height} from {path}')
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image = InitImageResizer(image).resize(width,height)
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print(f'resized input image to size {image.width}x{image.height}')
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from ldm.dream.image_util import InitImageResizer
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if width == self.width and height == self.height:
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new_image_width, new_image_height, resize_needed = self._resolution_check(
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image.width, image.height)
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else:
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if height == self.height:
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new_image_width, new_image_height, resize_needed = self._resolution_check(
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width, image.height)
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if width == self.width:
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new_image_width, new_image_height, resize_needed = self._resolution_check(
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image.width, height)
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else:
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image = InitImageResizer(image).resize(width, height)
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resize_needed=False
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if resize_needed:
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image = InitImageResizer(image).resize(
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new_image_width, new_image_height)
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
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@ -633,7 +647,7 @@ class T2I:
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prompt = text[:idx]
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remaining -= idx
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# remove from main text
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text = text[idx + 1 :]
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text = text[idx + 1:]
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# find value for weight
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if ' ' in text:
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idx = text.index(' ') # first occurence
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@ -651,7 +665,7 @@ class T2I:
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weight = 1.0
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# remove from main text
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remaining -= idx
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text = text[idx + 1 :]
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text = text[idx + 1:]
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# append the sub-prompt and its weight
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prompts.append(prompt)
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weights.append(weight)
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@ -662,9 +676,9 @@ class T2I:
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weights.append(1.0)
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remaining = 0
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return prompts, weights
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# shows how the prompt is tokenized
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# usually tokens have '</w>' to indicate end-of-word,
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# shows how the prompt is tokenized
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# usually tokens have '</w>' to indicate end-of-word,
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# but for readability it has been replaced with ' '
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def _log_tokenization(self, text):
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if not self.log_tokenization:
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@ -674,15 +688,31 @@ class T2I:
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discarded = ""
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usedTokens = 0
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totalTokens = len(tokens)
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for i in range(0,totalTokens):
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token = tokens[i].replace('</w>',' ')
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for i in range(0, totalTokens):
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token = tokens[i].replace('</w>', ' ')
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# alternate color
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s = (usedTokens % 6) + 1
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if i < self.model.cond_stage_model.max_length:
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tokenized = tokenized + f"\x1b[0;3{s};40m{token}"
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usedTokens += 1
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else: # over max token length
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else: # over max token length
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discarded = discarded + f"\x1b[0;3{s};40m{token}"
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print(f"\nTokens ({usedTokens}):\n{tokenized}\x1b[0m")
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if discarded != "":
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print(f"Tokens Discarded ({totalTokens-usedTokens}):\n{discarded}\x1b[0m")
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print(
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f"Tokens Discarded ({totalTokens-usedTokens}):\n{discarded}\x1b[0m")
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def _resolution_check(self, width, height, log=False):
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resize_needed = False
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w, h = map(
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lambda x: x - x % 64, (width, height)
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) # resize to integer multiple of 64
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if h != height or w != width:
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if log:
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print(
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f'>> Provided width and height must be multiples of 64. Auto-resizing to {w}x{h}'
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)
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height = h
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width = w
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resize_needed = True
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return width, height, resize_needed
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