sample_illustrious.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("OnomaAIResearch/Illustrious-xl-early-release-v0", 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:1"
pipe = pipe.to(device)
PRESET_Q = "best quality, good quality, masterpiece (quality)"
NEGATIVE_PROMPT = "worst quality, comic, multiple views, bad quality, low quality, lowres, displeasing, very displeasing, bad anatomy, bad hands, scan artifacts, monochrome, greyscale, signature, twitter username, jpeg artifacts, 2koma, 4koma, guro, 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("ranking/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"ranking/data/illustriousv0/{i+1}.webp"
        image.save(fn, "webp", quality=95)
    except Exception as e:
        print(f"Error at prompt {i+1}: {e}")
        continue