1180 lines
57 KiB
Python
1180 lines
57 KiB
Python
# copy from https://github.com/huggingface/diffusers/blob/main/examples/community/lpw_stable_diffusion.py
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# and modify to support SD2.x
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import inspect
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import re
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from typing import Callable, List, Optional, Union
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import numpy as np
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import PIL
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import torch
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from packaging import version
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from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
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import diffusers
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from diffusers import SchedulerMixin, StableDiffusionPipeline
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from diffusers.models import AutoencoderKL, UNet2DConditionModel
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from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
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from diffusers.utils import logging
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try:
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from diffusers.utils import PIL_INTERPOLATION
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except ImportError:
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if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
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PIL_INTERPOLATION = {
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"linear": PIL.Image.Resampling.BILINEAR,
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"bilinear": PIL.Image.Resampling.BILINEAR,
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"bicubic": PIL.Image.Resampling.BICUBIC,
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"lanczos": PIL.Image.Resampling.LANCZOS,
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"nearest": PIL.Image.Resampling.NEAREST,
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}
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else:
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PIL_INTERPOLATION = {
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"linear": PIL.Image.LINEAR,
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"bilinear": PIL.Image.BILINEAR,
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"bicubic": PIL.Image.BICUBIC,
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"lanczos": PIL.Image.LANCZOS,
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"nearest": PIL.Image.NEAREST,
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}
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# ------------------------------------------------------------------------------
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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re_attention = re.compile(
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r"""
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\\\(|
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\\\)|
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\\\[|
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\\]|
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\\\\|
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\\|
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\(|
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\[|
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:([+-]?[.\d]+)\)|
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\)|
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]|
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[^\\()\[\]:]+|
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:
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""",
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re.X,
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)
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def parse_prompt_attention(text):
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"""
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Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
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Accepted tokens are:
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(abc) - increases attention to abc by a multiplier of 1.1
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(abc:3.12) - increases attention to abc by a multiplier of 3.12
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[abc] - decreases attention to abc by a multiplier of 1.1
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\( - literal character '('
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\[ - literal character '['
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\) - literal character ')'
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\] - literal character ']'
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\\ - literal character '\'
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anything else - just text
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>>> parse_prompt_attention('normal text')
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[['normal text', 1.0]]
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>>> parse_prompt_attention('an (important) word')
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[['an ', 1.0], ['important', 1.1], [' word', 1.0]]
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>>> parse_prompt_attention('(unbalanced')
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[['unbalanced', 1.1]]
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>>> parse_prompt_attention('\(literal\]')
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[['(literal]', 1.0]]
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>>> parse_prompt_attention('(unnecessary)(parens)')
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[['unnecessaryparens', 1.1]]
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>>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
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[['a ', 1.0],
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['house', 1.5730000000000004],
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[' ', 1.1],
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['on', 1.0],
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[' a ', 1.1],
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['hill', 0.55],
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[', sun, ', 1.1],
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['sky', 1.4641000000000006],
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['.', 1.1]]
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"""
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res = []
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round_brackets = []
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square_brackets = []
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round_bracket_multiplier = 1.1
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square_bracket_multiplier = 1 / 1.1
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def multiply_range(start_position, multiplier):
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for p in range(start_position, len(res)):
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res[p][1] *= multiplier
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for m in re_attention.finditer(text):
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text = m.group(0)
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weight = m.group(1)
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if text.startswith("\\"):
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res.append([text[1:], 1.0])
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elif text == "(":
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round_brackets.append(len(res))
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elif text == "[":
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square_brackets.append(len(res))
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elif weight is not None and len(round_brackets) > 0:
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multiply_range(round_brackets.pop(), float(weight))
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elif text == ")" and len(round_brackets) > 0:
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multiply_range(round_brackets.pop(), round_bracket_multiplier)
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elif text == "]" and len(square_brackets) > 0:
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multiply_range(square_brackets.pop(), square_bracket_multiplier)
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else:
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res.append([text, 1.0])
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for pos in round_brackets:
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multiply_range(pos, round_bracket_multiplier)
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for pos in square_brackets:
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multiply_range(pos, square_bracket_multiplier)
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if len(res) == 0:
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res = [["", 1.0]]
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# merge runs of identical weights
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i = 0
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while i + 1 < len(res):
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if res[i][1] == res[i + 1][1]:
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res[i][0] += res[i + 1][0]
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res.pop(i + 1)
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else:
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i += 1
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return res
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def get_prompts_with_weights(pipe: StableDiffusionPipeline, prompt: List[str], max_length: int):
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r"""
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Tokenize a list of prompts and return its tokens with weights of each token.
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No padding, starting or ending token is included.
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"""
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tokens = []
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weights = []
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truncated = False
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for text in prompt:
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texts_and_weights = parse_prompt_attention(text)
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text_token = []
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text_weight = []
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for word, weight in texts_and_weights:
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# tokenize and discard the starting and the ending token
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token = pipe.tokenizer(word).input_ids[1:-1]
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text_token += token
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# copy the weight by length of token
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text_weight += [weight] * len(token)
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# stop if the text is too long (longer than truncation limit)
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if len(text_token) > max_length:
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truncated = True
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break
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# truncate
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if len(text_token) > max_length:
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truncated = True
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text_token = text_token[:max_length]
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text_weight = text_weight[:max_length]
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tokens.append(text_token)
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weights.append(text_weight)
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if truncated:
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logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
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return tokens, weights
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def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, no_boseos_middle=True, chunk_length=77):
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r"""
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Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
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"""
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max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
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weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
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for i in range(len(tokens)):
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tokens[i] = [bos] + tokens[i] + [eos] * (max_length - 1 - len(tokens[i]))
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if no_boseos_middle:
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weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
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else:
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w = []
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if len(weights[i]) == 0:
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w = [1.0] * weights_length
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else:
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for j in range(max_embeddings_multiples):
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w.append(1.0) # weight for starting token in this chunk
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w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))]
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w.append(1.0) # weight for ending token in this chunk
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w += [1.0] * (weights_length - len(w))
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weights[i] = w[:]
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return tokens, weights
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def get_unweighted_text_embeddings(
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pipe: StableDiffusionPipeline,
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text_input: torch.Tensor,
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chunk_length: int,
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clip_skip: int,
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eos: int,
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pad: int,
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no_boseos_middle: Optional[bool] = True,
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):
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"""
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When the length of tokens is a multiple of the capacity of the text encoder,
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it should be split into chunks and sent to the text encoder individually.
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"""
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max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
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if max_embeddings_multiples > 1:
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text_embeddings = []
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for i in range(max_embeddings_multiples):
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# extract the i-th chunk
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text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone()
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# cover the head and the tail by the starting and the ending tokens
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text_input_chunk[:, 0] = text_input[0, 0]
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if pad == eos: # v1
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text_input_chunk[:, -1] = text_input[0, -1]
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else: # v2
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for j in range(len(text_input_chunk)):
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if text_input_chunk[j, -1] != eos and text_input_chunk[j, -1] != pad: # 最後に普通の文字がある
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text_input_chunk[j, -1] = eos
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if text_input_chunk[j, 1] == pad: # BOSだけであとはPAD
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text_input_chunk[j, 1] = eos
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if clip_skip is None or clip_skip == 1:
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text_embedding = pipe.text_encoder(text_input_chunk)[0]
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else:
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enc_out = pipe.text_encoder(text_input_chunk, output_hidden_states=True, return_dict=True)
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text_embedding = enc_out["hidden_states"][-clip_skip]
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text_embedding = pipe.text_encoder.text_model.final_layer_norm(text_embedding)
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# cover the head and the tail by the starting and the ending tokens
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text_input_chunk[:, 0] = text_input[0, 0]
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text_input_chunk[:, -1] = text_input[0, -1]
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text_embedding = pipe.text_encoder(text_input_chunk, attention_mask=None)[0]
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if no_boseos_middle:
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if i == 0:
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# discard the ending token
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text_embedding = text_embedding[:, :-1]
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elif i == max_embeddings_multiples - 1:
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# discard the starting token
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text_embedding = text_embedding[:, 1:]
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else:
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# discard both starting and ending tokens
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text_embedding = text_embedding[:, 1:-1]
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text_embeddings.append(text_embedding)
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text_embeddings = torch.concat(text_embeddings, axis=1)
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else:
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text_embeddings = pipe.text_encoder(text_input)[0]
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return text_embeddings
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def get_weighted_text_embeddings(
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pipe: StableDiffusionPipeline,
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prompt: Union[str, List[str]],
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uncond_prompt: Optional[Union[str, List[str]]] = None,
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max_embeddings_multiples: Optional[int] = 3,
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no_boseos_middle: Optional[bool] = False,
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skip_parsing: Optional[bool] = False,
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skip_weighting: Optional[bool] = False,
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clip_skip=None,
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):
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r"""
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Prompts can be assigned with local weights using brackets. For example,
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prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
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and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
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Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
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Args:
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pipe (`StableDiffusionPipeline`):
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Pipe to provide access to the tokenizer and the text encoder.
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prompt (`str` or `List[str]`):
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The prompt or prompts to guide the image generation.
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uncond_prompt (`str` or `List[str]`):
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The unconditional prompt or prompts for guide the image generation. If unconditional prompt
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is provided, the embeddings of prompt and uncond_prompt are concatenated.
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max_embeddings_multiples (`int`, *optional*, defaults to `3`):
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The max multiple length of prompt embeddings compared to the max output length of text encoder.
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no_boseos_middle (`bool`, *optional*, defaults to `False`):
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If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
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ending token in each of the chunk in the middle.
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skip_parsing (`bool`, *optional*, defaults to `False`):
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Skip the parsing of brackets.
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skip_weighting (`bool`, *optional*, defaults to `False`):
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Skip the weighting. When the parsing is skipped, it is forced True.
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"""
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max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
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if isinstance(prompt, str):
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prompt = [prompt]
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if not skip_parsing:
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prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)
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if uncond_prompt is not None:
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if isinstance(uncond_prompt, str):
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uncond_prompt = [uncond_prompt]
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uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)
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else:
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prompt_tokens = [token[1:-1] for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids]
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prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
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if uncond_prompt is not None:
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if isinstance(uncond_prompt, str):
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uncond_prompt = [uncond_prompt]
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uncond_tokens = [
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token[1:-1] for token in pipe.tokenizer(uncond_prompt, max_length=max_length, truncation=True).input_ids
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]
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uncond_weights = [[1.0] * len(token) for token in uncond_tokens]
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# round up the longest length of tokens to a multiple of (model_max_length - 2)
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max_length = max([len(token) for token in prompt_tokens])
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if uncond_prompt is not None:
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max_length = max(max_length, max([len(token) for token in uncond_tokens]))
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max_embeddings_multiples = min(
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max_embeddings_multiples,
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(max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
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)
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max_embeddings_multiples = max(1, max_embeddings_multiples)
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max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
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# pad the length of tokens and weights
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bos = pipe.tokenizer.bos_token_id
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eos = pipe.tokenizer.eos_token_id
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pad = pipe.tokenizer.pad_token_id
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prompt_tokens, prompt_weights = pad_tokens_and_weights(
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prompt_tokens,
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prompt_weights,
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max_length,
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bos,
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eos,
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no_boseos_middle=no_boseos_middle,
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chunk_length=pipe.tokenizer.model_max_length,
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)
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prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=pipe.device)
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if uncond_prompt is not None:
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uncond_tokens, uncond_weights = pad_tokens_and_weights(
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uncond_tokens,
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uncond_weights,
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max_length,
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bos,
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eos,
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no_boseos_middle=no_boseos_middle,
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chunk_length=pipe.tokenizer.model_max_length,
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)
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uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device=pipe.device)
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# get the embeddings
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text_embeddings = get_unweighted_text_embeddings(
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pipe,
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prompt_tokens,
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pipe.tokenizer.model_max_length,
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clip_skip,
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eos,
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pad,
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no_boseos_middle=no_boseos_middle,
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)
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prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=pipe.device)
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if uncond_prompt is not None:
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uncond_embeddings = get_unweighted_text_embeddings(
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pipe,
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uncond_tokens,
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pipe.tokenizer.model_max_length,
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clip_skip,
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eos,
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pad,
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no_boseos_middle=no_boseos_middle,
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)
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uncond_weights = torch.tensor(uncond_weights, dtype=uncond_embeddings.dtype, device=pipe.device)
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# assign weights to the prompts and normalize in the sense of mean
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# TODO: should we normalize by chunk or in a whole (current implementation)?
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if (not skip_parsing) and (not skip_weighting):
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previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
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text_embeddings *= prompt_weights.unsqueeze(-1)
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current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
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text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
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if uncond_prompt is not None:
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previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
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uncond_embeddings *= uncond_weights.unsqueeze(-1)
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current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
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uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
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if uncond_prompt is not None:
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return text_embeddings, uncond_embeddings
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return text_embeddings, None
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def preprocess_image(image):
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w, h = image.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
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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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image = torch.from_numpy(image)
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return 2.0 * image - 1.0
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def preprocess_mask(mask, scale_factor=8):
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mask = mask.convert("L")
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w, h = mask.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
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mask = np.array(mask).astype(np.float32) / 255.0
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mask = np.tile(mask, (4, 1, 1))
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mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?
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mask = 1 - mask # repaint white, keep black
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mask = torch.from_numpy(mask)
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return mask
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class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
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r"""
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Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing
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weighting in prompt.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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Args:
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
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text_encoder ([`CLIPTextModel`]):
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|
Frozen text-encoder. Stable Diffusion uses the text portion of
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[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
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the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
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tokenizer (`CLIPTokenizer`):
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Tokenizer of class
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[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
|
|
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
|
|
scheduler ([`SchedulerMixin`]):
|
|
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
|
|
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
|
|
safety_checker ([`StableDiffusionSafetyChecker`]):
|
|
Classification module that estimates whether generated images could be considered offensive or harmful.
|
|
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
|
|
feature_extractor ([`CLIPFeatureExtractor`]):
|
|
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
|
|
"""
|
|
|
|
# if version.parse(version.parse(diffusers.__version__).base_version) >= version.parse("0.9.0"):
|
|
|
|
def __init__(
|
|
self,
|
|
vae: AutoencoderKL,
|
|
text_encoder: CLIPTextModel,
|
|
tokenizer: CLIPTokenizer,
|
|
unet: UNet2DConditionModel,
|
|
scheduler: SchedulerMixin,
|
|
clip_skip: int,
|
|
safety_checker: StableDiffusionSafetyChecker,
|
|
feature_extractor: CLIPFeatureExtractor,
|
|
requires_safety_checker: bool = True,
|
|
):
|
|
super().__init__(
|
|
vae=vae,
|
|
text_encoder=text_encoder,
|
|
tokenizer=tokenizer,
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
safety_checker=safety_checker,
|
|
feature_extractor=feature_extractor,
|
|
requires_safety_checker=requires_safety_checker,
|
|
)
|
|
self.clip_skip = clip_skip
|
|
self.__init__additional__()
|
|
|
|
# else:
|
|
# def __init__(
|
|
# self,
|
|
# vae: AutoencoderKL,
|
|
# text_encoder: CLIPTextModel,
|
|
# tokenizer: CLIPTokenizer,
|
|
# unet: UNet2DConditionModel,
|
|
# scheduler: SchedulerMixin,
|
|
# safety_checker: StableDiffusionSafetyChecker,
|
|
# feature_extractor: CLIPFeatureExtractor,
|
|
# ):
|
|
# super().__init__(
|
|
# vae=vae,
|
|
# text_encoder=text_encoder,
|
|
# tokenizer=tokenizer,
|
|
# unet=unet,
|
|
# scheduler=scheduler,
|
|
# safety_checker=safety_checker,
|
|
# feature_extractor=feature_extractor,
|
|
# )
|
|
# self.__init__additional__()
|
|
|
|
def __init__additional__(self):
|
|
if not hasattr(self, "vae_scale_factor"):
|
|
setattr(self, "vae_scale_factor", 2 ** (len(self.vae.config.block_out_channels) - 1))
|
|
|
|
@property
|
|
def _execution_device(self):
|
|
r"""
|
|
Returns the device on which the pipeline's models will be executed. After calling
|
|
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
|
|
hooks.
|
|
"""
|
|
if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
|
|
return self.device
|
|
for module in self.unet.modules():
|
|
if (
|
|
hasattr(module, "_hf_hook")
|
|
and hasattr(module._hf_hook, "execution_device")
|
|
and module._hf_hook.execution_device is not None
|
|
):
|
|
return torch.device(module._hf_hook.execution_device)
|
|
return self.device
|
|
|
|
def _encode_prompt(
|
|
self,
|
|
prompt,
|
|
device,
|
|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt,
|
|
max_embeddings_multiples,
|
|
):
|
|
r"""
|
|
Encodes the prompt into text encoder hidden states.
|
|
|
|
Args:
|
|
prompt (`str` or `list(int)`):
|
|
prompt to be encoded
|
|
device: (`torch.device`):
|
|
torch device
|
|
num_images_per_prompt (`int`):
|
|
number of images that should be generated per prompt
|
|
do_classifier_free_guidance (`bool`):
|
|
whether to use classifier free guidance or not
|
|
negative_prompt (`str` or `List[str]`):
|
|
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
|
if `guidance_scale` is less than `1`).
|
|
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
|
|
The max multiple length of prompt embeddings compared to the max output length of text encoder.
|
|
"""
|
|
batch_size = len(prompt) if isinstance(prompt, list) else 1
|
|
|
|
if negative_prompt is None:
|
|
negative_prompt = [""] * batch_size
|
|
elif isinstance(negative_prompt, str):
|
|
negative_prompt = [negative_prompt] * batch_size
|
|
if batch_size != len(negative_prompt):
|
|
raise ValueError(
|
|
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
|
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
|
" the batch size of `prompt`."
|
|
)
|
|
|
|
text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
|
|
pipe=self,
|
|
prompt=prompt,
|
|
uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
|
|
max_embeddings_multiples=max_embeddings_multiples,
|
|
clip_skip=self.clip_skip,
|
|
)
|
|
bs_embed, seq_len, _ = text_embeddings.shape
|
|
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
|
|
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
|
|
|
if do_classifier_free_guidance:
|
|
bs_embed, seq_len, _ = uncond_embeddings.shape
|
|
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
|
|
uncond_embeddings = uncond_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
|
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
|
|
|
|
return text_embeddings
|
|
|
|
def check_inputs(self, prompt, height, width, strength, callback_steps):
|
|
if not isinstance(prompt, str) and not isinstance(prompt, list):
|
|
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
|
|
|
if strength < 0 or strength > 1:
|
|
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
|
|
|
|
if height % 8 != 0 or width % 8 != 0:
|
|
print(height, width)
|
|
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
|
|
|
if (callback_steps is None) or (
|
|
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
|
|
):
|
|
raise ValueError(
|
|
f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}."
|
|
)
|
|
|
|
def get_timesteps(self, num_inference_steps, strength, device, is_text2img):
|
|
if is_text2img:
|
|
return self.scheduler.timesteps.to(device), num_inference_steps
|
|
else:
|
|
# get the original timestep using init_timestep
|
|
offset = self.scheduler.config.get("steps_offset", 0)
|
|
init_timestep = int(num_inference_steps * strength) + offset
|
|
init_timestep = min(init_timestep, num_inference_steps)
|
|
|
|
t_start = max(num_inference_steps - init_timestep + offset, 0)
|
|
timesteps = self.scheduler.timesteps[t_start:].to(device)
|
|
return timesteps, num_inference_steps - t_start
|
|
|
|
def run_safety_checker(self, image, device, dtype):
|
|
if self.safety_checker is not None:
|
|
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
|
|
image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_checker_input.pixel_values.to(dtype))
|
|
else:
|
|
has_nsfw_concept = None
|
|
return image, has_nsfw_concept
|
|
|
|
def decode_latents(self, latents):
|
|
latents = 1 / 0.18215 * latents
|
|
image = self.vae.decode(latents).sample
|
|
image = (image / 2 + 0.5).clamp(0, 1)
|
|
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
|
|
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
|
|
return image
|
|
|
|
def prepare_extra_step_kwargs(self, generator, eta):
|
|
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
|
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
|
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
|
# and should be between [0, 1]
|
|
|
|
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
|
extra_step_kwargs = {}
|
|
if accepts_eta:
|
|
extra_step_kwargs["eta"] = eta
|
|
|
|
# check if the scheduler accepts generator
|
|
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
|
if accepts_generator:
|
|
extra_step_kwargs["generator"] = generator
|
|
return extra_step_kwargs
|
|
|
|
def prepare_latents(self, image, timestep, batch_size, height, width, dtype, device, generator, latents=None):
|
|
if image is None:
|
|
shape = (
|
|
batch_size,
|
|
self.unet.in_channels,
|
|
height // self.vae_scale_factor,
|
|
width // self.vae_scale_factor,
|
|
)
|
|
|
|
if latents is None:
|
|
if device.type == "mps":
|
|
# randn does not work reproducibly on mps
|
|
latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
|
|
else:
|
|
latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
|
|
else:
|
|
if latents.shape != shape:
|
|
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
|
|
latents = latents.to(device)
|
|
|
|
# scale the initial noise by the standard deviation required by the scheduler
|
|
latents = latents * self.scheduler.init_noise_sigma
|
|
return latents, None, None
|
|
else:
|
|
init_latent_dist = self.vae.encode(image).latent_dist
|
|
init_latents = init_latent_dist.sample(generator=generator)
|
|
init_latents = 0.18215 * init_latents
|
|
init_latents = torch.cat([init_latents] * batch_size, dim=0)
|
|
init_latents_orig = init_latents
|
|
shape = init_latents.shape
|
|
|
|
# add noise to latents using the timesteps
|
|
if device.type == "mps":
|
|
noise = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
|
|
else:
|
|
noise = torch.randn(shape, generator=generator, device=device, dtype=dtype)
|
|
latents = self.scheduler.add_noise(init_latents, noise, timestep)
|
|
return latents, init_latents_orig, noise
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self,
|
|
prompt: Union[str, List[str]],
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
image: Union[torch.FloatTensor, PIL.Image.Image] = None,
|
|
mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
|
|
height: int = 512,
|
|
width: int = 512,
|
|
num_inference_steps: int = 50,
|
|
guidance_scale: float = 7.5,
|
|
strength: float = 0.8,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
eta: float = 0.0,
|
|
generator: Optional[torch.Generator] = None,
|
|
latents: Optional[torch.FloatTensor] = None,
|
|
max_embeddings_multiples: Optional[int] = 3,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
|
is_cancelled_callback: Optional[Callable[[], bool]] = None,
|
|
callback_steps: int = 1,
|
|
):
|
|
r"""
|
|
Function invoked when calling the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`):
|
|
The prompt or prompts to guide the image generation.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
|
if `guidance_scale` is less than `1`).
|
|
image (`torch.FloatTensor` or `PIL.Image.Image`):
|
|
`Image`, or tensor representing an image batch, that will be used as the starting point for the
|
|
process.
|
|
mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
|
|
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
|
|
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
|
|
PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
|
|
contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
|
|
height (`int`, *optional*, defaults to 512):
|
|
The height in pixels of the generated image.
|
|
width (`int`, *optional*, defaults to 512):
|
|
The width in pixels of the generated image.
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
|
expense of slower inference.
|
|
guidance_scale (`float`, *optional*, defaults to 7.5):
|
|
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
|
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
|
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
|
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
|
usually at the expense of lower image quality.
|
|
strength (`float`, *optional*, defaults to 0.8):
|
|
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
|
|
`image` will be used as a starting point, adding more noise to it the larger the `strength`. The
|
|
number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
|
|
noise will be maximum and the denoising process will run for the full number of iterations specified in
|
|
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
|
[`schedulers.DDIMScheduler`], will be ignored for others.
|
|
generator (`torch.Generator`, *optional*):
|
|
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
|
|
deterministic.
|
|
latents (`torch.FloatTensor`, *optional*):
|
|
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
|
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
|
tensor will ge generated by sampling using the supplied random `generator`.
|
|
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
|
|
The max multiple length of prompt embeddings compared to the max output length of text encoder.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generate image. Choose between
|
|
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
|
plain tuple.
|
|
callback (`Callable`, *optional*):
|
|
A function that will be called every `callback_steps` steps during inference. The function will be
|
|
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
|
is_cancelled_callback (`Callable`, *optional*):
|
|
A function that will be called every `callback_steps` steps during inference. If the function returns
|
|
`True`, the inference will be cancelled.
|
|
callback_steps (`int`, *optional*, defaults to 1):
|
|
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
|
called at every step.
|
|
|
|
Returns:
|
|
`None` if cancelled by `is_cancelled_callback`,
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
|
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
|
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
|
(nsfw) content, according to the `safety_checker`.
|
|
"""
|
|
# 0. Default height and width to unet
|
|
height = height or self.unet.config.sample_size * self.vae_scale_factor
|
|
width = width or self.unet.config.sample_size * self.vae_scale_factor
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(prompt, height, width, strength, callback_steps)
|
|
|
|
# 2. Define call parameters
|
|
batch_size = 1 if isinstance(prompt, str) else len(prompt)
|
|
device = self._execution_device
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
do_classifier_free_guidance = guidance_scale > 1.0
|
|
|
|
# 3. Encode input prompt
|
|
text_embeddings = self._encode_prompt(
|
|
prompt,
|
|
device,
|
|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt,
|
|
max_embeddings_multiples,
|
|
)
|
|
dtype = text_embeddings.dtype
|
|
|
|
# 4. Preprocess image and mask
|
|
if isinstance(image, PIL.Image.Image):
|
|
image = preprocess_image(image)
|
|
if image is not None:
|
|
image = image.to(device=self.device, dtype=dtype)
|
|
if isinstance(mask_image, PIL.Image.Image):
|
|
mask_image = preprocess_mask(mask_image, self.vae_scale_factor)
|
|
if mask_image is not None:
|
|
mask = mask_image.to(device=self.device, dtype=dtype)
|
|
mask = torch.cat([mask] * batch_size * num_images_per_prompt)
|
|
else:
|
|
mask = None
|
|
|
|
# 5. set timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device, image is None)
|
|
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
|
|
|
|
# 6. Prepare latent variables
|
|
latents, init_latents_orig, noise = self.prepare_latents(
|
|
image,
|
|
latent_timestep,
|
|
batch_size * num_images_per_prompt,
|
|
height,
|
|
width,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
|
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
|
|
|
# 8. Denoising loop
|
|
for i, t in enumerate(self.progress_bar(timesteps)):
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
|
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
|
|
|
# predict the noise residual
|
|
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
|
|
|
|
# perform guidance
|
|
if do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
|
|
|
|
if mask is not None:
|
|
# masking
|
|
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
|
|
latents = (init_latents_proper * mask) + (latents * (1 - mask))
|
|
|
|
# call the callback, if provided
|
|
if i % callback_steps == 0:
|
|
if callback is not None:
|
|
callback(i, t, latents)
|
|
if is_cancelled_callback is not None and is_cancelled_callback():
|
|
return None
|
|
|
|
# 9. Post-processing
|
|
image = self.decode_latents(latents)
|
|
|
|
# 10. Run safety checker
|
|
image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
|
|
|
|
# 11. Convert to PIL
|
|
if output_type == "pil":
|
|
image = self.numpy_to_pil(image)
|
|
|
|
if not return_dict:
|
|
return image, has_nsfw_concept
|
|
|
|
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
|
|
|
def text2img(
|
|
self,
|
|
prompt: Union[str, List[str]],
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
height: int = 512,
|
|
width: int = 512,
|
|
num_inference_steps: int = 50,
|
|
guidance_scale: float = 7.5,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
eta: float = 0.0,
|
|
generator: Optional[torch.Generator] = None,
|
|
latents: Optional[torch.FloatTensor] = None,
|
|
max_embeddings_multiples: Optional[int] = 3,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
|
is_cancelled_callback: Optional[Callable[[], bool]] = None,
|
|
callback_steps: int = 1,
|
|
):
|
|
r"""
|
|
Function for text-to-image generation.
|
|
Args:
|
|
prompt (`str` or `List[str]`):
|
|
The prompt or prompts to guide the image generation.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
|
if `guidance_scale` is less than `1`).
|
|
height (`int`, *optional*, defaults to 512):
|
|
The height in pixels of the generated image.
|
|
width (`int`, *optional*, defaults to 512):
|
|
The width in pixels of the generated image.
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
|
expense of slower inference.
|
|
guidance_scale (`float`, *optional*, defaults to 7.5):
|
|
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
|
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
|
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
|
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
|
usually at the expense of lower image quality.
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
|
[`schedulers.DDIMScheduler`], will be ignored for others.
|
|
generator (`torch.Generator`, *optional*):
|
|
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
|
|
deterministic.
|
|
latents (`torch.FloatTensor`, *optional*):
|
|
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
|
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
|
tensor will ge generated by sampling using the supplied random `generator`.
|
|
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
|
|
The max multiple length of prompt embeddings compared to the max output length of text encoder.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generate image. Choose between
|
|
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
|
plain tuple.
|
|
callback (`Callable`, *optional*):
|
|
A function that will be called every `callback_steps` steps during inference. The function will be
|
|
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
|
is_cancelled_callback (`Callable`, *optional*):
|
|
A function that will be called every `callback_steps` steps during inference. If the function returns
|
|
`True`, the inference will be cancelled.
|
|
callback_steps (`int`, *optional*, defaults to 1):
|
|
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
|
called at every step.
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
|
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
|
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
|
(nsfw) content, according to the `safety_checker`.
|
|
"""
|
|
return self.__call__(
|
|
prompt=prompt,
|
|
negative_prompt=negative_prompt,
|
|
height=height,
|
|
width=width,
|
|
num_inference_steps=num_inference_steps,
|
|
guidance_scale=guidance_scale,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
eta=eta,
|
|
generator=generator,
|
|
latents=latents,
|
|
max_embeddings_multiples=max_embeddings_multiples,
|
|
output_type=output_type,
|
|
return_dict=return_dict,
|
|
callback=callback,
|
|
is_cancelled_callback=is_cancelled_callback,
|
|
callback_steps=callback_steps,
|
|
)
|
|
|
|
def img2img(
|
|
self,
|
|
image: Union[torch.FloatTensor, PIL.Image.Image],
|
|
prompt: Union[str, List[str]],
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
strength: float = 0.8,
|
|
num_inference_steps: Optional[int] = 50,
|
|
guidance_scale: Optional[float] = 7.5,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
eta: Optional[float] = 0.0,
|
|
generator: Optional[torch.Generator] = None,
|
|
max_embeddings_multiples: Optional[int] = 3,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
|
is_cancelled_callback: Optional[Callable[[], bool]] = None,
|
|
callback_steps: int = 1,
|
|
):
|
|
r"""
|
|
Function for image-to-image generation.
|
|
Args:
|
|
image (`torch.FloatTensor` or `PIL.Image.Image`):
|
|
`Image`, or tensor representing an image batch, that will be used as the starting point for the
|
|
process.
|
|
prompt (`str` or `List[str]`):
|
|
The prompt or prompts to guide the image generation.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
|
if `guidance_scale` is less than `1`).
|
|
strength (`float`, *optional*, defaults to 0.8):
|
|
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
|
|
`image` will be used as a starting point, adding more noise to it the larger the `strength`. The
|
|
number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
|
|
noise will be maximum and the denoising process will run for the full number of iterations specified in
|
|
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
|
expense of slower inference. This parameter will be modulated by `strength`.
|
|
guidance_scale (`float`, *optional*, defaults to 7.5):
|
|
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
|
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
|
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
|
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
|
usually at the expense of lower image quality.
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
|
[`schedulers.DDIMScheduler`], will be ignored for others.
|
|
generator (`torch.Generator`, *optional*):
|
|
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
|
|
deterministic.
|
|
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
|
|
The max multiple length of prompt embeddings compared to the max output length of text encoder.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generate image. Choose between
|
|
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
|
plain tuple.
|
|
callback (`Callable`, *optional*):
|
|
A function that will be called every `callback_steps` steps during inference. The function will be
|
|
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
|
is_cancelled_callback (`Callable`, *optional*):
|
|
A function that will be called every `callback_steps` steps during inference. If the function returns
|
|
`True`, the inference will be cancelled.
|
|
callback_steps (`int`, *optional*, defaults to 1):
|
|
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
|
called at every step.
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
|
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
|
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
|
(nsfw) content, according to the `safety_checker`.
|
|
"""
|
|
return self.__call__(
|
|
prompt=prompt,
|
|
negative_prompt=negative_prompt,
|
|
image=image,
|
|
num_inference_steps=num_inference_steps,
|
|
guidance_scale=guidance_scale,
|
|
strength=strength,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
eta=eta,
|
|
generator=generator,
|
|
max_embeddings_multiples=max_embeddings_multiples,
|
|
output_type=output_type,
|
|
return_dict=return_dict,
|
|
callback=callback,
|
|
is_cancelled_callback=is_cancelled_callback,
|
|
callback_steps=callback_steps,
|
|
)
|
|
|
|
def inpaint(
|
|
self,
|
|
image: Union[torch.FloatTensor, PIL.Image.Image],
|
|
mask_image: Union[torch.FloatTensor, PIL.Image.Image],
|
|
prompt: Union[str, List[str]],
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
strength: float = 0.8,
|
|
num_inference_steps: Optional[int] = 50,
|
|
guidance_scale: Optional[float] = 7.5,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
eta: Optional[float] = 0.0,
|
|
generator: Optional[torch.Generator] = None,
|
|
max_embeddings_multiples: Optional[int] = 3,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
|
is_cancelled_callback: Optional[Callable[[], bool]] = None,
|
|
callback_steps: int = 1,
|
|
):
|
|
r"""
|
|
Function for inpaint.
|
|
Args:
|
|
image (`torch.FloatTensor` or `PIL.Image.Image`):
|
|
`Image`, or tensor representing an image batch, that will be used as the starting point for the
|
|
process. This is the image whose masked region will be inpainted.
|
|
mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
|
|
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
|
|
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
|
|
PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
|
|
contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
|
|
prompt (`str` or `List[str]`):
|
|
The prompt or prompts to guide the image generation.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
|
if `guidance_scale` is less than `1`).
|
|
strength (`float`, *optional*, defaults to 0.8):
|
|
Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
|
|
is 1, the denoising process will be run on the masked area for the full number of iterations specified
|
|
in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more
|
|
noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
|
|
the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
|
|
guidance_scale (`float`, *optional*, defaults to 7.5):
|
|
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
|
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
|
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
|
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
|
usually at the expense of lower image quality.
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
|
[`schedulers.DDIMScheduler`], will be ignored for others.
|
|
generator (`torch.Generator`, *optional*):
|
|
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
|
|
deterministic.
|
|
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
|
|
The max multiple length of prompt embeddings compared to the max output length of text encoder.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generate image. Choose between
|
|
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
|
plain tuple.
|
|
callback (`Callable`, *optional*):
|
|
A function that will be called every `callback_steps` steps during inference. The function will be
|
|
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
|
is_cancelled_callback (`Callable`, *optional*):
|
|
A function that will be called every `callback_steps` steps during inference. If the function returns
|
|
`True`, the inference will be cancelled.
|
|
callback_steps (`int`, *optional*, defaults to 1):
|
|
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
|
called at every step.
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
|
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
|
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
|
(nsfw) content, according to the `safety_checker`.
|
|
"""
|
|
return self.__call__(
|
|
prompt=prompt,
|
|
negative_prompt=negative_prompt,
|
|
image=image,
|
|
mask_image=mask_image,
|
|
num_inference_steps=num_inference_steps,
|
|
guidance_scale=guidance_scale,
|
|
strength=strength,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
eta=eta,
|
|
generator=generator,
|
|
max_embeddings_multiples=max_embeddings_multiples,
|
|
output_type=output_type,
|
|
return_dict=return_dict,
|
|
callback=callback,
|
|
is_cancelled_callback=is_cancelled_callback,
|
|
callback_steps=callback_steps,
|
|
)
|