sample_aniv3.py

import torch
import random
from tqdm.rich import tqdm

# ----------------------------------------------------------------------


import torch
import random
from typing import Optional
import PIL

from transformers import CLIPTokenizer
from diffusers.loaders import (
    StableDiffusionXLLoraLoaderMixin,
    TextualInversionLoaderMixin,
)
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.utils import (
    USE_PEFT_BACKEND,
    logging,
    scale_lora_layers,
    unscale_lora_layers,
)
from diffusers.pipelines import StableDiffusionXLPipeline

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

def parse_prompt_attention(text):
    """
    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
    Accepted tokens are:
      (abc) - increases attention to abc by a multiplier of 1.1
      (abc:3.12) - increases attention to abc by a multiplier of 3.12
      [abc] - decreases attention to abc by a multiplier of 1.1
      \\( - literal character '('
      \\[ - literal character '['
      \\) - literal character ')'
      \\] - literal character ']'
      \\ - literal character '\'
      anything else - just text

    >>> parse_prompt_attention('normal text')
    [['normal text', 1.0]]
    >>> parse_prompt_attention('an (important) word')
    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
    >>> parse_prompt_attention('(unbalanced')
    [['unbalanced', 1.1]]
    >>> parse_prompt_attention('\\(literal\\]')
    [['(literal]', 1.0]]
    >>> parse_prompt_attention('(unnecessary)(parens)')
    [['unnecessaryparens', 1.1]]
    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
    [['a ', 1.0],
     ['house', 1.5730000000000004],
     [' ', 1.1],
     ['on', 1.0],
     [' a ', 1.1],
     ['hill', 0.55],
     [', sun, ', 1.1],
     ['sky', 1.4641000000000006],
     ['.', 1.1]]
    """
    import re

    re_attention = re.compile(
        r"""
            \{|\}|\\\(|\\\)|\\\[|\\]|\\\\|\\|\(|\[|:([+-]?[.\d]+)\)|
            \)|]|[^\\()\[\]:]+|:
        """,
        re.X,
    )

    re_break = re.compile(r"\s*\bBREAK\b\s*", re.S)

    res = []
    round_brackets = []
    square_brackets = []
    curly_brackets = []
    round_bracket_multiplier = 1.05
    curly_bracket_multiplier = 1.05
    square_bracket_multiplier = 1 / 1.05

    def multiply_range(start_position, multiplier):
        for p in range(start_position, len(res)):
            res[p][1] *= multiplier

    for m in re_attention.finditer(text):
        text = m.group(0)
        weight = m.group(1)

        if text.startswith("\\"):
            res.append([text[1:], 1.0])
        elif text == "(":
            round_brackets.append(len(res))
        elif text == "{":
            curly_brackets.append(len(res))
        elif text == "[":
            square_brackets.append(len(res))
        elif weight is not None and len(round_brackets) > 0:
            multiply_range(round_brackets.pop(), float(weight))
        elif text == ")" and len(round_brackets) > 0:
            multiply_range(round_brackets.pop(), round_bracket_multiplier)
        elif text == "}" and len(round_brackets) > 0:
            multiply_range(curly_brackets.pop(), curly_bracket_multiplier)
        elif text == "]" and len(square_brackets) > 0:
            multiply_range(square_brackets.pop(), square_bracket_multiplier)
        else:
            parts = re.split(re_break, text)
            for i, part in enumerate(parts):
                if i > 0:
                    res.append(["BREAK", -1])
                res.append([part, 1.0])

    for pos in round_brackets:
        multiply_range(pos, round_bracket_multiplier)

    for pos in square_brackets:
        multiply_range(pos, square_bracket_multiplier)

    if len(res) == 0:
        res = [["", 1.0]]

    # merge runs of identical weights
    i = 0
    while i + 1 < len(res):
        if res[i][1] == res[i + 1][1]:
            res[i][0] += res[i + 1][0]
            res.pop(i + 1)
        else:
            i += 1

    return res


def get_prompts_tokens_with_weights(clip_tokenizer: CLIPTokenizer, prompt: str):
    """
    Get prompt token ids and weights, this function works for both prompt and negative prompt

    Args:
        pipe (CLIPTokenizer)
            A CLIPTokenizer
        prompt (str)
            A prompt string with weights

    Returns:
        text_tokens (list)
            A list contains token ids
        text_weight (list)
            A list contains the correspondent weight of token ids

    Example:
        import torch
        from transformers import CLIPTokenizer

        clip_tokenizer = CLIPTokenizer.from_pretrained(
            "stablediffusionapi/deliberate-v2"
            , subfolder = "tokenizer"
            , dtype = torch.float16
        )

        token_id_list, token_weight_list = get_prompts_tokens_with_weights(
            clip_tokenizer = clip_tokenizer
            ,prompt = "a (red:1.5) cat"*70
        )
    """
    texts_and_weights = parse_prompt_attention(prompt)
    text_tokens, text_weights = [], []
    for word, weight in texts_and_weights:
        # tokenize and discard the starting and the ending token
        token = clip_tokenizer(word, truncation=False).input_ids[1:-1]  # so that tokenize whatever length prompt
        # the returned token is a 1d list: [320, 1125, 539, 320]

        # merge the new tokens to the all tokens holder: text_tokens
        text_tokens = [*text_tokens, *token]

        # each token chunk will come with one weight, like ['red cat', 2.0]
        # need to expand weight for each token.
        chunk_weights = [weight] * len(token)

        # append the weight back to the weight holder: text_weights
        text_weights = [*text_weights, *chunk_weights]
    return text_tokens, text_weights


def group_tokens_and_weights(token_ids: list, weights: list, pad_last_block=False):
    """
    Produce tokens and weights in groups and pad the missing tokens

    Args:
        token_ids (list)
            The token ids from tokenizer
        weights (list)
            The weights list from function get_prompts_tokens_with_weights
        pad_last_block (bool)
            Control if fill the last token list to 75 tokens with eos
    Returns:
        new_token_ids (2d list)
        new_weights (2d list)

    Example:
        token_groups,weight_groups = group_tokens_and_weights(
            token_ids = token_id_list
            , weights = token_weight_list
        )
    """
    bos, eos = 49406, 49407

    # this will be a 2d list
    new_token_ids = []
    new_weights = []
    while len(token_ids) >= 75:
        # get the first 75 tokens
        head_75_tokens = [token_ids.pop(0) for _ in range(75)]
        head_75_weights = [weights.pop(0) for _ in range(75)]

        # extract token ids and weights
        temp_77_token_ids = [bos] + head_75_tokens + [eos]
        temp_77_weights = [1.0] + head_75_weights + [1.0]

        # add 77 token and weights chunk to the holder list
        new_token_ids.append(temp_77_token_ids)
        new_weights.append(temp_77_weights)

    # padding the left
    if len(token_ids) > 0:
        padding_len = 75 - len(token_ids) if pad_last_block else 0

        temp_77_token_ids = [bos] + token_ids + [eos] * padding_len + [eos]
        new_token_ids.append(temp_77_token_ids)

        temp_77_weights = [1.0] + weights + [1.0] * padding_len + [1.0]
        new_weights.append(temp_77_weights)

    return new_token_ids, new_weights


def get_weighted_text_embeddings_sdxl(
    pipe,
    prompt: str = "",
    prompt_2: str = None,
    neg_prompt: str = "",
    neg_prompt_2: str = None,
    num_images_per_prompt: int = 1,
    device: Optional[torch.device] = None,
    clip_skip: Optional[int] = None,
    lora_scale: Optional[int] = None,
):
    """
    This function can process long prompt with weights, no length limitation
    for Stable Diffusion XL

    Args:
        pipe (StableDiffusionPipeline)
        prompt (str)
        prompt_2 (str)
        neg_prompt (str)
        neg_prompt_2 (str)
        num_images_per_prompt (int)
        device (torch.device)
        clip_skip (int)
    Returns:
        prompt_embeds (torch.Tensor)
        neg_prompt_embeds (torch.Tensor)
    """
    device = device or pipe._execution_device

    # set lora scale so that monkey patched LoRA
    # function of text encoder can correctly access it
    if lora_scale is not None and isinstance(pipe, StableDiffusionXLLoraLoaderMixin):
        pipe._lora_scale = lora_scale

        # dynamically adjust the LoRA scale
        if pipe.text_encoder is not None:
            if not USE_PEFT_BACKEND:
                adjust_lora_scale_text_encoder(pipe.text_encoder, lora_scale)
            else:
                scale_lora_layers(pipe.text_encoder, lora_scale)

        if pipe.text_encoder_2 is not None:
            if not USE_PEFT_BACKEND:
                adjust_lora_scale_text_encoder(pipe.text_encoder_2, lora_scale)
            else:
                scale_lora_layers(pipe.text_encoder_2, lora_scale)

    if prompt_2:
        prompt = f"{prompt} {prompt_2}"

    if neg_prompt_2:
        neg_prompt = f"{neg_prompt} {neg_prompt_2}"

    prompt_t1 = prompt_t2 = prompt
    neg_prompt_t1 = neg_prompt_t2 = neg_prompt

    if isinstance(pipe, TextualInversionLoaderMixin):
        prompt_t1 = pipe.maybe_convert_prompt(prompt_t1, pipe.tokenizer)
        neg_prompt_t1 = pipe.maybe_convert_prompt(neg_prompt_t1, pipe.tokenizer)
        prompt_t2 = pipe.maybe_convert_prompt(prompt_t2, pipe.tokenizer_2)
        neg_prompt_t2 = pipe.maybe_convert_prompt(neg_prompt_t2, pipe.tokenizer_2)

    eos = pipe.tokenizer.eos_token_id

    # tokenizer 1
    prompt_tokens, prompt_weights = get_prompts_tokens_with_weights(pipe.tokenizer, prompt_t1)
    neg_prompt_tokens, neg_prompt_weights = get_prompts_tokens_with_weights(pipe.tokenizer, neg_prompt_t1)

    # tokenizer 2
    prompt_tokens_2, prompt_weights_2 = get_prompts_tokens_with_weights(pipe.tokenizer_2, prompt_t2)
    neg_prompt_tokens_2, neg_prompt_weights_2 = get_prompts_tokens_with_weights(pipe.tokenizer_2, neg_prompt_t2)

    # padding the shorter one for prompt set 1
    prompt_token_len = len(prompt_tokens)
    neg_prompt_token_len = len(neg_prompt_tokens)

    if prompt_token_len > neg_prompt_token_len:
        # padding the neg_prompt with eos token
        neg_prompt_tokens = neg_prompt_tokens + [eos] * abs(prompt_token_len - neg_prompt_token_len)
        neg_prompt_weights = neg_prompt_weights + [1.0] * abs(prompt_token_len - neg_prompt_token_len)
    else:
        # padding the prompt
        prompt_tokens = prompt_tokens + [eos] * abs(prompt_token_len - neg_prompt_token_len)
        prompt_weights = prompt_weights + [1.0] * abs(prompt_token_len - neg_prompt_token_len)

    # padding the shorter one for token set 2
    prompt_token_len_2 = len(prompt_tokens_2)
    neg_prompt_token_len_2 = len(neg_prompt_tokens_2)

    if prompt_token_len_2 > neg_prompt_token_len_2:
        # padding the neg_prompt with eos token
        neg_prompt_tokens_2 = neg_prompt_tokens_2 + [eos] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
        neg_prompt_weights_2 = neg_prompt_weights_2 + [1.0] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
    else:
        # padding the prompt
        prompt_tokens_2 = prompt_tokens_2 + [eos] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
        prompt_weights_2 = prompt_weights + [1.0] * abs(prompt_token_len_2 - neg_prompt_token_len_2)

    embeds = []
    neg_embeds = []

    prompt_token_groups, prompt_weight_groups = group_tokens_and_weights(prompt_tokens.copy(), prompt_weights.copy())

    neg_prompt_token_groups, neg_prompt_weight_groups = group_tokens_and_weights(
        neg_prompt_tokens.copy(), neg_prompt_weights.copy()
    )

    prompt_token_groups_2, prompt_weight_groups_2 = group_tokens_and_weights(
        prompt_tokens_2.copy(), prompt_weights_2.copy()
    )

    neg_prompt_token_groups_2, neg_prompt_weight_groups_2 = group_tokens_and_weights(
        neg_prompt_tokens_2.copy(), neg_prompt_weights_2.copy()
    )

    # get prompt embeddings one by one is not working.
    for i in range(len(prompt_token_groups)):
        # get positive prompt embeddings with weights
        token_tensor = torch.tensor([prompt_token_groups[i]], dtype=torch.long, device=device)
        weight_tensor = torch.tensor(prompt_weight_groups[i], dtype=torch.float16, device=device)

        token_tensor_2 = torch.tensor([prompt_token_groups_2[i]], dtype=torch.long, device=device)

        # use first text encoder
        prompt_embeds_1 = pipe.text_encoder(token_tensor.to(device), output_hidden_states=True)

        # use second text encoder
        prompt_embeds_2 = pipe.text_encoder_2(token_tensor_2.to(device), output_hidden_states=True)
        pooled_prompt_embeds = prompt_embeds_2[0]

        if clip_skip is None:
            prompt_embeds_1_hidden_states = prompt_embeds_1.hidden_states[-2]
            prompt_embeds_2_hidden_states = prompt_embeds_2.hidden_states[-2]
        else:
            # "2" because SDXL always indexes from the penultimate layer.
            prompt_embeds_1_hidden_states = prompt_embeds_1.hidden_states[-(clip_skip + 2)]
            prompt_embeds_2_hidden_states = prompt_embeds_2.hidden_states[-(clip_skip + 2)]

        prompt_embeds_list = [prompt_embeds_1_hidden_states, prompt_embeds_2_hidden_states]
        token_embedding = torch.concat(prompt_embeds_list, dim=-1).squeeze(0)

        for j in range(len(weight_tensor)):
            if weight_tensor[j] != 1.0:
                token_embedding[j] = (
                    token_embedding[-1] + (token_embedding[j] - token_embedding[-1]) * weight_tensor[j]
                )

        token_embedding = token_embedding.unsqueeze(0)
        embeds.append(token_embedding)

        # get negative prompt embeddings with weights
        neg_token_tensor = torch.tensor([neg_prompt_token_groups[i]], dtype=torch.long, device=device)
        neg_token_tensor_2 = torch.tensor([neg_prompt_token_groups_2[i]], dtype=torch.long, device=device)
        neg_weight_tensor = torch.tensor(neg_prompt_weight_groups[i], dtype=torch.float16, device=device)

        # use first text encoder
        neg_prompt_embeds_1 = pipe.text_encoder(neg_token_tensor.to(device), output_hidden_states=True)
        neg_prompt_embeds_1_hidden_states = neg_prompt_embeds_1.hidden_states[-2]

        # use second text encoder
        neg_prompt_embeds_2 = pipe.text_encoder_2(neg_token_tensor_2.to(device), output_hidden_states=True)
        neg_prompt_embeds_2_hidden_states = neg_prompt_embeds_2.hidden_states[-2]
        negative_pooled_prompt_embeds = neg_prompt_embeds_2[0]

        neg_prompt_embeds_list = [neg_prompt_embeds_1_hidden_states, neg_prompt_embeds_2_hidden_states]
        neg_token_embedding = torch.concat(neg_prompt_embeds_list, dim=-1).squeeze(0)

        for z in range(len(neg_weight_tensor)):
            if neg_weight_tensor[z] != 1.0:
                neg_token_embedding[z] = (
                    neg_token_embedding[-1] + (neg_token_embedding[z] - neg_token_embedding[-1]) * neg_weight_tensor[z]
                )

        neg_token_embedding = neg_token_embedding.unsqueeze(0)
        neg_embeds.append(neg_token_embedding)

    prompt_embeds = torch.cat(embeds, dim=1)
    negative_prompt_embeds = torch.cat(neg_embeds, dim=1)

    bs_embed, seq_len, _ = prompt_embeds.shape
    # duplicate text embeddings for each generation per prompt, using mps friendly method
    prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
    prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

    seq_len = negative_prompt_embeds.shape[1]
    negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
    negative_prompt_embeds = negative_prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

    pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1).view(
        bs_embed * num_images_per_prompt, -1
    )
    negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1).view(
        bs_embed * num_images_per_prompt, -1
    )

    if pipe.text_encoder is not None:
        if isinstance(pipe, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
            # Retrieve the original scale by scaling back the LoRA layers
            unscale_lora_layers(pipe.text_encoder, lora_scale)

    if pipe.text_encoder_2 is not None:
        if isinstance(pipe, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
            # Retrieve the original scale by scaling back the LoRA layers
            unscale_lora_layers(pipe.text_encoder_2, lora_scale)

    return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
    
    
class ModText2ImgPipeline(StableDiffusionXLPipeline):
    
    def encode_prompt(self, prompt, num_images_per_prompt, negative_prompt, lora_scale, clip_skip, **kwags):
        return get_weighted_text_embeddings_sdxl(
            pipe=self,
            prompt=prompt,
            neg_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            clip_skip=clip_skip,
            lora_scale=lora_scale,
        )

# ----------------------------------------------------------------------
      
pipe = ModText2ImgPipeline.from_pretrained(
    "cagliostrolab/animagine-xl-3.1",
    torch_dtype=torch.float16
)
pipe.fuse_qkv_projections()
pipe.set_progress_bar_config(leave=False)
pipe.unet.to(memory_format=torch.channels_last)
pipe.vae.to(memory_format=torch.channels_last)
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)

device="cuda"
pipe = pipe.to(device)
PRESET_Q = "masterpiece, best quality, very aesthetic, absurdes"
NEGATIVE_PROMPT = "worst quality, lowres, ugly, abstract, signature, logo, artist name, deformed hand, fused fingers, extra limbs, extra arms, missing arm, extra legs, missing leg, extra digits, fewer digits"

@torch.inference_mode
def generate(prompt, preset=PRESET_Q, h=1216, w=832, negative_prompt=NEGATIVE_PROMPT, guidance_scale=7.0, randomize_seed=True, seed=42):
    prompt = prompt.strip() + ", " + preset.strip()
    negative_prompt = negative_prompt.strip() if negative_prompt and negative_prompt.strip() else None
    
    print(f"Initial seed for prompt `{prompt}`", seed)
    if(randomize_seed):
        seed = random.randint(0, 9007199254740991)
    
    if not prompt and not negative_prompt:
        guidance_scale = 0.0
        
    generator = torch.Generator(device="cuda").manual_seed(seed)
    image = pipe(prompt, height=h, width=w, negative_prompt=negative_prompt, guidance_scale=guidance_scale, generator=generator, num_inference_steps=25).images
    return image

# read prompts for testing
with open("prompts.csv") as f:
    prompts = f.readlines()

# warmup
generate("")

# generate images
for i, prompt in tqdm(enumerate(prompts), total=len(prompts)):
    try:
        image = generate(prompt.strip())[0]
        fn = f"aniv3/{i+1}.webp"
        image.save(fn, "webp", quality=95)
    except Exception as e:
        print(f"Error at prompt {i+1}: {e}")
        continue