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import os |
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import copy |
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import random |
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import subprocess |
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import numpy as np |
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import time |
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import torch |
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import torch.nn.functional as F |
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from PIL import ImageFont |
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from cog import BasePredictor, Input, Path, BaseModel |
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from diffusers import StableDiffusionXLPipeline, DDIMScheduler |
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from diffusers.utils import load_image |
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from utils import PhotoMakerStableDiffusionXLPipeline |
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from utils.style_template import styles |
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from utils.gradio_utils import ( |
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AttnProcessor2_0 as AttnProcessor, |
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) |
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from utils.gradio_utils import cal_attn_mask_xl |
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from utils.utils import get_comic |
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|
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MODEL_URL = "https://weights.replicate.delivery/default/HVision_NKU/StoryDiffusion.tar" |
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MODEL_CACHE = "model_weights" |
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STYLE_NAMES = list(styles.keys()) |
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DEFAULT_STYLE_NAME = "Japanese Anime" |
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|
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global total_count, attn_count, cur_step, mask1024, mask4096, attn_procs, unet |
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global sa32, sa64 |
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global write |
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global height, width |
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""" |
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# load and upload the weights to replicate.delivery for faster booting on Replicate |
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models_dict = { |
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"RealVision": "SG161222/RealVisXL_V4.0", |
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"Unstable": "stablediffusionapi/sdxl-unstable-diffusers-y", |
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} |
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# photomaker_path = hf_hub_download(repo_id="TencentARC/PhotoMaker", filename="photomaker-v1.bin", repo_type="model") |
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photomaker_path = f"{MODEL_CACHE}/PhotoMaker/photomaker-v1.bin" |
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pipe_unstable = PhotoMakerStableDiffusionXLPipeline.from_pretrained( |
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models_dict["Unstable"], |
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torch_dtype=torch.float16, |
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use_safetensors=False, |
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) |
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pipe_unstable.save_pretrained(f"{MODEL_CACHE}/Unstable/stablediffusionapi/sdxl-unstable-diffusers-y") |
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pipe_realvision = PhotoMakerStableDiffusionXLPipeline.from_pretrained( |
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models_dict["RealVision"], torch_dtype=torch.float16, use_safetensors=True |
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) |
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pipe_realvision.save_pretrained(f"{MODEL_CACHE}/RealVision/SG161222/RealVisXL_V4.0") |
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""" |
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class ModelOutput(BaseModel): |
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comic: Path |
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individual_images: list[Path] |
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def download_weights(url, dest): |
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start = time.time() |
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print("downloading url: ", url) |
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print("downloading to: ", dest) |
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subprocess.check_call(["pget", "-x", url, dest], close_fds=False) |
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print("downloading took: ", time.time() - start) |
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def setup_seed(seed): |
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torch.manual_seed(seed) |
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torch.cuda.manual_seed_all(seed) |
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np.random.seed(seed) |
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random.seed(seed) |
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torch.backends.cudnn.deterministic = True |
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def apply_style_positive(style_name: str, positive: str): |
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p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) |
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return p.replace("{prompt}", positive) |
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def apply_style(style_name: str, positives: list, negative: str = ""): |
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p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) |
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return [ |
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p.replace("{prompt}", positive) for positive in positives |
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], n + " " + negative |
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def set_attention_processor(unet, id_length, is_ipadapter=False): |
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global total_count |
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total_count = 0 |
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attn_procs = {} |
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for name in unet.attn_processors.keys(): |
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cross_attention_dim = ( |
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None |
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if name.endswith("attn1.processor") |
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else unet.config.cross_attention_dim |
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) |
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if name.startswith("mid_block"): |
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hidden_size = unet.config.block_out_channels[-1] |
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elif name.startswith("up_blocks"): |
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block_id = int(name[len("up_blocks.")]) |
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hidden_size = list(reversed(unet.config.block_out_channels))[block_id] |
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elif name.startswith("down_blocks"): |
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block_id = int(name[len("down_blocks.")]) |
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hidden_size = unet.config.block_out_channels[block_id] |
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if cross_attention_dim is None: |
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if name.startswith("up_blocks"): |
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attn_procs[name] = SpatialAttnProcessor2_0(id_length=id_length) |
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total_count += 1 |
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else: |
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attn_procs[name] = AttnProcessor() |
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else: |
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if is_ipadapter: |
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attn_procs[name] = IPAttnProcessor2_0( |
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hidden_size=hidden_size, |
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cross_attention_dim=cross_attention_dim, |
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scale=1, |
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num_tokens=4, |
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).to(unet.device, dtype=torch.float16) |
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else: |
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attn_procs[name] = AttnProcessor() |
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|
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unet.set_attn_processor(copy.deepcopy(attn_procs)) |
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print("Successfully load paired self-attention") |
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print(f"Number of the processor : {total_count}") |
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class SpatialAttnProcessor2_0(torch.nn.Module): |
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r""" |
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Attention processor for IP-Adapater for PyTorch 2.0. |
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Args: |
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hidden_size (`int`): |
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The hidden size of the attention layer. |
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cross_attention_dim (`int`): |
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The number of channels in the `encoder_hidden_states`. |
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text_context_len (`int`, defaults to 77): |
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The context length of the text features. |
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scale (`float`, defaults to 1.0): |
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the weight scale of image prompt. |
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""" |
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def __init__( |
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self, |
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hidden_size=None, |
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cross_attention_dim=None, |
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id_length=4, |
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device="cuda", |
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dtype=torch.float16, |
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): |
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super().__init__() |
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if not hasattr(F, "scaled_dot_product_attention"): |
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raise ImportError( |
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"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0." |
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) |
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self.device = device |
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self.dtype = dtype |
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self.hidden_size = hidden_size |
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self.cross_attention_dim = cross_attention_dim |
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self.total_length = id_length + 1 |
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self.id_length = id_length |
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self.id_bank = {} |
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|
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def __call__( |
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self, |
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attn, |
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hidden_states, |
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encoder_hidden_states=None, |
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attention_mask=None, |
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temb=None, |
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): |
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global total_count, attn_count, cur_step, mask1024, mask4096 |
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global sa32, sa64 |
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global write |
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global height, width |
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if write: |
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self.id_bank[cur_step] = [ |
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hidden_states[: self.id_length], |
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hidden_states[self.id_length :], |
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] |
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else: |
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encoder_hidden_states = torch.cat( |
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( |
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self.id_bank[cur_step][0].to(self.device), |
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hidden_states[:1], |
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self.id_bank[cur_step][1].to(self.device), |
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hidden_states[1:], |
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) |
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) |
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if cur_step < 5: |
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hidden_states = self.__call2__( |
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attn, hidden_states, encoder_hidden_states, attention_mask, temb |
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) |
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else: |
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random_number = random.random() |
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if cur_step < 20: |
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rand_num = 0.3 |
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else: |
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rand_num = 0.1 |
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if random_number > rand_num: |
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if not write: |
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if hidden_states.shape[1] == (height // 32) * (width // 32): |
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attention_mask = mask1024[ |
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mask1024.shape[0] // self.total_length * self.id_length : |
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] |
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else: |
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attention_mask = mask4096[ |
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mask4096.shape[0] // self.total_length * self.id_length : |
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] |
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else: |
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if hidden_states.shape[1] == (height // 32) * (width // 32): |
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attention_mask = mask1024[ |
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: mask1024.shape[0] // self.total_length * self.id_length, |
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: mask1024.shape[0] // self.total_length * self.id_length, |
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] |
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else: |
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attention_mask = mask4096[ |
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: mask4096.shape[0] // self.total_length * self.id_length, |
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: mask4096.shape[0] // self.total_length * self.id_length, |
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] |
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hidden_states = self.__call1__( |
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attn, hidden_states, encoder_hidden_states, attention_mask, temb |
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) |
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else: |
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hidden_states = self.__call2__( |
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attn, hidden_states, None, attention_mask, temb |
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) |
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attn_count += 1 |
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if attn_count == total_count: |
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attn_count = 0 |
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cur_step += 1 |
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mask1024, mask4096 = cal_attn_mask_xl( |
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self.total_length, |
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self.id_length, |
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sa32, |
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sa64, |
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height, |
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width, |
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device=self.device, |
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dtype=self.dtype, |
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) |
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return hidden_states |
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|
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def __call1__( |
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self, |
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attn, |
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hidden_states, |
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encoder_hidden_states=None, |
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attention_mask=None, |
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temb=None, |
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): |
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residual = hidden_states |
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if attn.spatial_norm is not None: |
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hidden_states = attn.spatial_norm(hidden_states, temb) |
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input_ndim = hidden_states.ndim |
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|
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if input_ndim == 4: |
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total_batch_size, channel, height, width = hidden_states.shape |
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hidden_states = hidden_states.view( |
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total_batch_size, channel, height * width |
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).transpose(1, 2) |
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total_batch_size, nums_token, channel = hidden_states.shape |
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img_nums = total_batch_size // 2 |
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hidden_states = hidden_states.view(-1, img_nums, nums_token, channel).reshape( |
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-1, img_nums * nums_token, channel |
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) |
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batch_size, sequence_length, _ = hidden_states.shape |
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|
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if attn.group_norm is not None: |
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hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose( |
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1, 2 |
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) |
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|
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query = attn.to_q(hidden_states) |
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|
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if encoder_hidden_states is None: |
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encoder_hidden_states = hidden_states |
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else: |
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encoder_hidden_states = encoder_hidden_states.view( |
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-1, self.id_length + 1, nums_token, channel |
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).reshape(-1, (self.id_length + 1) * nums_token, channel) |
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|
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key = attn.to_k(encoder_hidden_states) |
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value = attn.to_v(encoder_hidden_states) |
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|
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inner_dim = key.shape[-1] |
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head_dim = inner_dim // attn.heads |
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|
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query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) |
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|
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key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) |
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value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) |
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hidden_states = F.scaled_dot_product_attention( |
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query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False |
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) |
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|
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hidden_states = hidden_states.transpose(1, 2).reshape( |
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total_batch_size, -1, attn.heads * head_dim |
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) |
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hidden_states = hidden_states.to(query.dtype) |
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|
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|
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hidden_states = attn.to_out[0](hidden_states) |
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|
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hidden_states = attn.to_out[1](hidden_states) |
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|
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if input_ndim == 4: |
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hidden_states = hidden_states.transpose(-1, -2).reshape( |
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total_batch_size, channel, height, width |
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) |
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if attn.residual_connection: |
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hidden_states = hidden_states + residual |
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hidden_states = hidden_states / attn.rescale_output_factor |
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|
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return hidden_states |
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|
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def __call2__( |
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self, |
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attn, |
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hidden_states, |
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encoder_hidden_states=None, |
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attention_mask=None, |
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temb=None, |
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): |
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residual = hidden_states |
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|
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if attn.spatial_norm is not None: |
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hidden_states = attn.spatial_norm(hidden_states, temb) |
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|
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input_ndim = hidden_states.ndim |
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|
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if input_ndim == 4: |
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batch_size, channel, height, width = hidden_states.shape |
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hidden_states = hidden_states.view( |
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batch_size, channel, height * width |
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).transpose(1, 2) |
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|
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batch_size, sequence_length, channel = hidden_states.shape |
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|
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if attention_mask is not None: |
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attention_mask = attn.prepare_attention_mask( |
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attention_mask, sequence_length, batch_size |
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) |
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|
|
|
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attention_mask = attention_mask.view( |
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batch_size, attn.heads, -1, attention_mask.shape[-1] |
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) |
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|
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if attn.group_norm is not None: |
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hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose( |
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1, 2 |
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) |
|
|
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query = attn.to_q(hidden_states) |
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|
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if encoder_hidden_states is None: |
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encoder_hidden_states = hidden_states |
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else: |
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encoder_hidden_states = encoder_hidden_states.view( |
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-1, self.id_length + 1, sequence_length, channel |
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).reshape(-1, (self.id_length + 1) * sequence_length, channel) |
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|
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key = attn.to_k(encoder_hidden_states) |
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value = attn.to_v(encoder_hidden_states) |
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|
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inner_dim = key.shape[-1] |
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head_dim = inner_dim // attn.heads |
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|
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query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) |
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|
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key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) |
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value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) |
|
|
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hidden_states = F.scaled_dot_product_attention( |
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query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False |
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) |
|
|
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hidden_states = hidden_states.transpose(1, 2).reshape( |
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batch_size, -1, attn.heads * head_dim |
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) |
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hidden_states = hidden_states.to(query.dtype) |
|
|
|
|
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hidden_states = attn.to_out[0](hidden_states) |
|
|
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hidden_states = attn.to_out[1](hidden_states) |
|
|
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if input_ndim == 4: |
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hidden_states = hidden_states.transpose(-1, -2).reshape( |
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batch_size, channel, height, width |
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) |
|
|
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if attn.residual_connection: |
|
hidden_states = hidden_states + residual |
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|
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hidden_states = hidden_states / attn.rescale_output_factor |
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|
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return hidden_states |
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|
|
|
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class Predictor(BasePredictor): |
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def setup(self) -> None: |
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"""Load the model into memory to make running multiple predictions efficient""" |
|
|
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models_dict = { |
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"RealVision": "SG161222/RealVisXL_V4.0", |
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"Unstable": "stablediffusionapi/sdxl-unstable-diffusers-y", |
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} |
|
|
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if not os.path.exists(MODEL_CACHE): |
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download_weights(MODEL_URL, MODEL_CACHE) |
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|
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photomaker_path = f"{MODEL_CACHE}/PhotoMaker/photomaker-v1.bin" |
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|
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self.sdxl_pipe_unstable = StableDiffusionXLPipeline.from_pretrained( |
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f"{MODEL_CACHE}/Unstable/sdxl/stablediffusionapi/sdxl-unstable-diffusers-y", |
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torch_dtype=torch.float16, |
|
) |
|
self.sdxl_pipe_realvision = StableDiffusionXLPipeline.from_pretrained( |
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f"{MODEL_CACHE}/RealVision/sdxl/SG161222/RealVisXL_V4.0", |
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torch_dtype=torch.float16, |
|
) |
|
|
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self.pipe_unstable = PhotoMakerStableDiffusionXLPipeline.from_pretrained( |
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f"{MODEL_CACHE}/Unstable/stablediffusionapi/sdxl-unstable-diffusers-y", |
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torch_dtype=torch.float16, |
|
use_safetensors=False, |
|
) |
|
self.pipe_unstable.load_photomaker_adapter( |
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os.path.dirname(photomaker_path), |
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subfolder="", |
|
weight_name=os.path.basename(photomaker_path), |
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trigger_word="img", |
|
) |
|
|
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self.pipe_realvision = PhotoMakerStableDiffusionXLPipeline.from_pretrained( |
|
f"{MODEL_CACHE}/RealVision/SG161222/RealVisXL_V4.0", |
|
torch_dtype=torch.float16, |
|
use_safetensors=True, |
|
) |
|
self.pipe_realvision.load_photomaker_adapter( |
|
os.path.dirname(photomaker_path), |
|
subfolder="", |
|
weight_name=os.path.basename(photomaker_path), |
|
trigger_word="img", |
|
) |
|
self.pipe_realvision.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2) |
|
self.pipe_realvision.fuse_lora() |
|
|
|
@torch.inference_mode() |
|
def predict( |
|
self, |
|
sd_model: str = Input( |
|
description="Choose a model", |
|
choices=["Unstable", "RealVision"], |
|
default="Unstable", |
|
), |
|
ref_image: Path = Input( |
|
description="Reference image for the character", |
|
default=None, |
|
), |
|
character_description: str = Input( |
|
description="General description of the character. If ref_image above is provided, making sure to follow the class word you want to customize with the trigger word 'img', such as: 'man img' or 'woman img' or 'girl img'", |
|
default="a man, wearing black suit", |
|
), |
|
negative_prompt: str = Input( |
|
description="Describe things you do not want to see in the output", |
|
default="bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs", |
|
), |
|
comic_description: str = Input( |
|
description="Comic Description. Each frame is divided by a new line. Only the first 10 prompts are valid for demo speed! For comic_description NOT using ref_image: (1) Support Typesetting Style and Captioning. By default, the prompt is used as the caption for each image. If you need to change the caption, add a '#' at the end of each line. Only the part after the '#' will be added as a caption to the image. (2) The [NC] symbol is used as a flag to indicate that no characters should be present in the generated scene images. If you want do that, prepend the '[NC]' at the beginning of the line.", |
|
default="at home, read new paper #at home, The newspaper says there is a treasure house in the forest.\non the road, near the forest\n[NC] The car on the road, near the forest #He drives to the forest in search of treasure.\n[NC]A tiger appeared in the forest, at night \nvery frightened, open mouth, in the forest, at night\nrunning very fast, in the forest, at night\n[NC] A house in the forest, at night #Suddenly, he discovers the treasure house!\nin the house filled with treasure, laughing, at night #He is overjoyed inside the house.", |
|
), |
|
style_name: str = Input( |
|
description="Style template", |
|
choices=STYLE_NAMES, |
|
default=DEFAULT_STYLE_NAME, |
|
), |
|
comic_style: str = Input( |
|
description="Select the comic style for the combined comic", |
|
choices=["Four Pannel", "Classic Comic Style"], |
|
default="Classic Comic Style", |
|
), |
|
style_strength_ratio: int = Input( |
|
description="Style strength of Ref Image (%), only used if ref_image is provided", |
|
default=20, |
|
ge=15, |
|
le=50, |
|
), |
|
image_width: int = Input( |
|
description="Width of output image", |
|
choices=[ |
|
256, |
|
288, |
|
320, |
|
352, |
|
384, |
|
416, |
|
448, |
|
480, |
|
512, |
|
544, |
|
576, |
|
608, |
|
640, |
|
672, |
|
704, |
|
736, |
|
768, |
|
800, |
|
832, |
|
864, |
|
896, |
|
928, |
|
960, |
|
992, |
|
1024, |
|
], |
|
default=768, |
|
), |
|
image_height: int = Input( |
|
description="Height of output image", |
|
choices=[ |
|
256, |
|
288, |
|
320, |
|
352, |
|
384, |
|
416, |
|
448, |
|
480, |
|
512, |
|
544, |
|
576, |
|
608, |
|
640, |
|
672, |
|
704, |
|
736, |
|
768, |
|
800, |
|
832, |
|
864, |
|
896, |
|
928, |
|
960, |
|
992, |
|
1024, |
|
], |
|
default=768, |
|
), |
|
num_steps: int = Input( |
|
description="Number of sample steps", ge=20, le=50, default=25 |
|
), |
|
guidance_scale: float = Input( |
|
description="Scale for classifier-free guidance", ge=0.1, le=10, default=5 |
|
), |
|
seed: int = Input( |
|
description="Random seed. Leave blank to randomize the seed", default=None |
|
), |
|
sa32_setting: float = Input( |
|
description="The degree of Paired Attention at 32 x 32 self-attention layers", |
|
default=0.5, |
|
ge=0, |
|
le=1.0, |
|
), |
|
sa64_setting: float = Input( |
|
description="The degree of Paired Attention at 64 x 64 self-attention layers", |
|
default=0.5, |
|
ge=0, |
|
le=1.0, |
|
), |
|
num_ids: int = Input( |
|
description="Number of id images in total images. This should not exceed total number of line-separated prompts", |
|
default=3, |
|
), |
|
output_format: str = Input( |
|
description="Format of the output images", |
|
choices=["webp", "jpg", "png"], |
|
default="webp", |
|
), |
|
output_quality: int = Input( |
|
description="Quality of the output images, from 0 to 100. 100 is best quality, 0 is lowest quality", |
|
default=80, |
|
ge=0, |
|
le=100, |
|
), |
|
) -> ModelOutput: |
|
"""Run a single prediction on the model""" |
|
|
|
global total_count, attn_count, cur_step, mask1024, mask4096, attn_procs, unet |
|
global sa32, sa64 |
|
global write |
|
global height, width |
|
|
|
assert ( |
|
len(character_description.strip()) > 0 |
|
), "Please provide the description of the character." |
|
|
|
if ref_image is not None: |
|
assert ( |
|
"img" in character_description |
|
), f"When using ref_image, please add the trigger word 'img' behind the class word you want to customize, such as: man img or woman img" |
|
assert ( |
|
"[NC]" not in comic_description |
|
), "You should not use trigger word [NC] when ref_image is provided." |
|
|
|
height = image_height |
|
width = image_width |
|
id_length = num_ids |
|
sa32 = sa32_setting |
|
sa64 = sa64_setting |
|
|
|
clipped_prompts = comic_description.splitlines()[:10] |
|
print(clipped_prompts) |
|
prompts = [ |
|
( |
|
character_description + "," + prompt |
|
if "[NC]" not in prompt |
|
else prompt.replace("[NC]", "") |
|
) |
|
for prompt in clipped_prompts |
|
] |
|
print(prompts) |
|
prompts = [ |
|
prompt.rpartition("#")[0].strip() if "#" in prompt else prompt.strip() |
|
for prompt in prompts |
|
] |
|
print(prompts) |
|
assert id_length <= len( |
|
prompts |
|
), "id_length should not exceed total number of line-separated prompts" |
|
|
|
id_prompts = prompts[:id_length] |
|
real_prompts = prompts[id_length:] |
|
|
|
if seed is None: |
|
seed = int.from_bytes(os.urandom(2), "big") |
|
print(f"Using seed: {seed}") |
|
|
|
device = "cuda:0" |
|
setup_seed(seed) |
|
generator = torch.Generator(device=device).manual_seed(seed) |
|
|
|
torch.cuda.empty_cache() |
|
|
|
model_type = "original" if ref_image is None else "Photomaker" |
|
|
|
if model_type == "original": |
|
pipe = ( |
|
self.sdxl_pipe_realvision |
|
if style_name == "(No style)" |
|
else self.sdxl_pipe_unstable |
|
) |
|
pipe = pipe.to(device) |
|
pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2) |
|
else: |
|
if sd_model != "RealVision" and style_name != "(No style)": |
|
pipe = self.pipe_unstable.to(device) |
|
else: |
|
pipe = self.pipe_realvision.to(device) |
|
pipe.id_encoder.to(device) |
|
|
|
write = True |
|
cur_step = 0 |
|
attn_count = 0 |
|
|
|
set_attention_processor(pipe.unet, id_length, is_ipadapter=False) |
|
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) |
|
pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2) |
|
curmodel_type = sd_model + "-" + model_type + "" + str(id_length) |
|
|
|
id_prompts, negative_prompt = apply_style( |
|
style_name, id_prompts, negative_prompt |
|
) |
|
|
|
total_results = [] |
|
if model_type == "original": |
|
id_images = pipe( |
|
id_prompts, |
|
num_inference_steps=num_steps, |
|
guidance_scale=guidance_scale, |
|
height=height, |
|
width=width, |
|
negative_prompt=negative_prompt, |
|
generator=generator, |
|
).images |
|
else: |
|
input_id_images = [load_image(str(ref_image))] |
|
start_merge_step = int(float(style_strength_ratio) / 100 * num_steps) |
|
id_images = pipe( |
|
id_prompts, |
|
input_id_images=input_id_images, |
|
num_inference_steps=num_steps, |
|
guidance_scale=guidance_scale, |
|
start_merge_step=start_merge_step, |
|
height=height, |
|
width=width, |
|
negative_prompt=negative_prompt, |
|
generator=generator, |
|
).images |
|
|
|
total_results = id_images + total_results |
|
|
|
real_images = [] |
|
write = False |
|
for real_prompt in real_prompts: |
|
cur_step = 0 |
|
real_prompt = apply_style_positive(style_name, real_prompt) |
|
if model_type == "original": |
|
real_images.append( |
|
pipe( |
|
real_prompt, |
|
num_inference_steps=num_steps, |
|
guidance_scale=guidance_scale, |
|
height=height, |
|
width=width, |
|
negative_prompt=negative_prompt, |
|
generator=generator, |
|
).images[0] |
|
) |
|
else: |
|
real_images.append( |
|
pipe( |
|
real_prompt, |
|
input_id_images=input_id_images, |
|
num_inference_steps=num_steps, |
|
guidance_scale=guidance_scale, |
|
start_merge_step=start_merge_step, |
|
height=height, |
|
width=width, |
|
negative_prompt=negative_prompt, |
|
generator=generator, |
|
).images[0] |
|
) |
|
|
|
total_results = [real_images[-1]] + total_results |
|
|
|
captions = clipped_prompts |
|
captions = [caption.replace("[NC]", "") for caption in captions] |
|
captions = [ |
|
caption.split("#")[-1].strip() if "#" in caption else caption.strip() |
|
for caption in captions |
|
] |
|
|
|
comic = get_comic( |
|
id_images + real_images, |
|
comic_style, |
|
captions=captions, |
|
font=ImageFont.truetype("./fonts/Inkfree.ttf", int(45)), |
|
) |
|
|
|
extension = output_format.lower() |
|
extension = "jpeg" if extension == "jpg" else extension |
|
comic_out = f"/tmp/comic.{extension}" |
|
comic[0].save(comic_out) |
|
|
|
save_params = {"format": extension.upper()} |
|
if not output_format == "png": |
|
save_params["quality"] = output_quality |
|
save_params["optimize"] = True |
|
|
|
output_paths = [] |
|
for index, sample in enumerate(total_results[::-1]): |
|
output_filename = f"/tmp/out-{index}.{extension}" |
|
sample.save(output_filename, **save_params) |
|
output_paths.append(Path(output_filename)) |
|
|
|
del pipe |
|
|
|
return ModelOutput(comic=Path(comic_out), individual_images=output_paths) |
|
|
