app.py

from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline, EulerAncestralDiscreteScheduler
import gradio as gr
import torch
from PIL import Image
import random
import os
from huggingface_hub import hf_hub_download
import torch
from torch import autocast
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
from safetensors import safe_open
from compel import Compel, ReturnedEmbeddingsType
from huggingface_hub import hf_hub_download

model_id = 'aipicasso/emi'
auth_token=os.environ["ACCESS_TOKEN"]

scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler", use_auth_token=auth_token)

pipe = StableDiffusionXLPipeline.from_pretrained(
  model_id,
  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
  scheduler=scheduler, use_auth_token=auth_token)
#pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)

#ckpt_file=hf_hub_download(repo_id=model_id, filename="v2.safetensors", token=auth_token)
#pipe = StableDiffusionXLPipeline.from_single_file(
#    ckpt_file,
#    torch_dtype=torch.float16,
#    scheduler=scheduler
#)
#pipe.load_lora_weights("manual.safetensors")

pipe_i2i = StableDiffusionXLImg2ImgPipeline.from_pretrained(
  model_id,
  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
  scheduler=scheduler,
  use_auth_token=auth_token
)

pipe=pipe.to("cuda")
pipe_i2i=pipe_i2i.to("cuda")

token_num=65

unaestheticXLv31=""
embeddings_dict = {}
with safe_open("unaestheticXLv31.safetensors", framework="pt") as f:
    for k in f.keys():
        embeddings_dict[k] = f.get_tensor(k)
for i in range(len(embeddings_dict["clip_l"])):
    token = f"sksd{chr(token_num)}"
    token_num+=1
    unaestheticXLv31 += token
    pipe.tokenizer.add_tokens(token)
    token_id = pipe.tokenizer.convert_tokens_to_ids(token)
    pipe.text_encoder.resize_token_embeddings(len(pipe.tokenizer))
    pipe.text_encoder_2.resize_token_embeddings(len(pipe.tokenizer))
    pipe.text_encoder.get_input_embeddings().weight.data[token_id] = embeddings_dict["clip_l"][i]
    pipe.text_encoder_2.get_input_embeddings().weight.data[token_id] = embeddings_dict["clip_g"][i]

unaestheticXLv1=""
embeddings_dict = {}
with safe_open("unaestheticXLv1.safetensors", framework="pt") as f:
    for k in f.keys():
        embeddings_dict[k] = f.get_tensor(k)
for i in range(len(embeddings_dict["clip_l"])):
    token = f"sksd{chr(token_num)}"
    token_num+=1
    unaestheticXLv1 += token
    pipe.tokenizer.add_tokens(token)
    token_id = pipe.tokenizer.convert_tokens_to_ids(token)
    pipe.text_encoder.resize_token_embeddings(len(pipe.tokenizer))
    pipe.text_encoder_2.resize_token_embeddings(len(pipe.tokenizer))
    pipe.text_encoder.get_input_embeddings().weight.data[token_id] = embeddings_dict["clip_l"][i]
    pipe.text_encoder_2.get_input_embeddings().weight.data[token_id] = embeddings_dict["clip_g"][i]

unaestheticXLv13=""
embeddings_dict = {}
with safe_open("unaestheticXLv13.safetensors", framework="pt") as f:
    for k in f.keys():
        embeddings_dict[k] = f.get_tensor(k)
for i in range(len(embeddings_dict["clip_l"])):
    token = f"sksd{chr(token_num)}"
    unaestheticXLv13 += token
    token_num+=1
    pipe.tokenizer.add_tokens(token)
    token_id = pipe.tokenizer.convert_tokens_to_ids(token)
    pipe.text_encoder.resize_token_embeddings(len(pipe.tokenizer))
    pipe.text_encoder_2.resize_token_embeddings(len(pipe.tokenizer))
    pipe.text_encoder.get_input_embeddings().weight.data[token_id] = embeddings_dict["clip_l"][i]
    pipe.text_encoder_2.get_input_embeddings().weight.data[token_id] = embeddings_dict["clip_g"][i]


compel = Compel(tokenizer=[pipe.tokenizer, pipe.tokenizer_2] , 
                text_encoder=[pipe.text_encoder, pipe.text_encoder_2], 
                returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, 
                requires_pooled=[False, True])

def error_str(error, title="Error"):
    return f"""#### {title}
            {error}"""  if error else ""

def inference(prompt, guidance, steps, image_size="Landscape", seed=0, img=None, strength=0.5, neg_prompt="", disable_auto_prompt_correction=False, image_style="Animetic", original_model=False):
  
  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None

  prompt,neg_prompt=auto_prompt_correction(prompt,neg_prompt,disable_auto_prompt_correction)

  if(image_size=="Portrait"):
      height=1344
      width=768
  elif(image_size=="Landscape"):
      height=768
      width=1344
  else:
      height=1024
      width=1024

  print(prompt,neg_prompt)
    
  try:
    if img is not None:
      return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
    else:
      return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
  except Exception as e:
    return None, error_str(e)

def auto_prompt_correction(prompt_ui,neg_prompt_ui,disable_auto_prompt_correction):
    # auto prompt correction
    prompt=str(prompt_ui)
    neg_prompt=str(neg_prompt_ui)
    prompt=prompt.lower()
    neg_prompt=neg_prompt.lower()
    
    if(disable_auto_prompt_correction):
        return prompt, neg_prompt

    if(prompt=="" and neg_prompt==""):
        prompt="1girl++, sunflowers, brown bob hair, brown eyes, sky"
        neg_prompt=f"({unaestheticXLv31})--"
        return prompt, neg_prompt

    splited_prompt=prompt.replace(","," ").replace("_"," ").replace("+"," ").split(" ")
    
    human_words=["1girl","girl","maid","maids","female","1woman","woman","girls","2girls","3girls","4girls","5girls","a couple of girls","women","1boy","boy","boys","a couple of boys","2boys","male","1man","1handsome","1bishounen","man","men","guy","guys"]
    for word in human_words:
        if( word in splited_prompt):
            prompt=f"{prompt}"
            neg_prompt=f"{neg_prompt},({unaestheticXLv31})--" 
            return prompt, neg_prompt
            
    animal_words=["cat","dog","bird","pigeon","rabbit","bunny","horse"]
    for word in animal_words:
        if( word in splited_prompt):
            prompt=f"a {prompt}, 4k, detailed"
            neg_prompt=f"{neg_prompt},({unaestheticXLv31})--" 
            return prompt, neg_prompt
            
    background_words=["mount fuji","mt. fuji","building", "buildings", "tokyo", "kyoto", "nara", "shibuya", "shinjuku"]
    for word in background_words:
        if( word in splited_prompt):
            prompt=f"{prompt}, highly detailed"
            neg_prompt=f"girl, (((deformed))), {neg_prompt}, girl, boy, photo, people, low quality, ui, error, lowres, jpeg artifacts, 2d, 3d, cg, text"
            return prompt, neg_prompt
            
    return prompt,neg_prompt
    
def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
    conditioning, pooled = compel([prompt, neg_prompt])
    
    result = pipe(
        prompt_embeds=conditioning[0:1],
        pooled_prompt_embeds=pooled[0:1], 
        negative_prompt_embeds=conditioning[1:2], 
        negative_pooled_prompt_embeds=pooled[1:2],
        num_inference_steps = int(steps),
        guidance_scale = guidance,
        width = width,
        height = height,
        generator = generator)
    
    return result.images[0]

def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
    ratio = min(height / img.height, width / img.width)
    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
    result = pipe_i2i(
        prompt,
        negative_prompt = neg_prompt,
        image = img,
        num_inference_steps = int(steps),
        strength = strength,
        guidance_scale = guidance,
        generator = generator)
        
    return result.images[0]

css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
"""
with gr.Blocks(css=css) as demo:
    gr.HTML(
        f"""
            <div class="main-div">
              <div>
                <h1>Emi Demo</h1>
              </div>
              <p>
               Demo for <a href="https://huggingface.co/aipicasso/emi">Emi</a><br>
              </p>
              <p>
              サンプル： そのままGenerateボタンを押してください。<br>
              sample : Click "Generate" button without any prompts.
              </p>
              <p>
              sample prompt1 : 1girl++, smile, purple long hair, purple eyes, stars
              </p>
              <p>
              sample prompt2 : 1boy++, white wavy hair, red eyes, white shirt, school
              </p>
              Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU 🥶</b>. For faster inference it is recommended to <b>upgrade to GPU in <a href='https://huggingface.co/spaces/akhaliq/cool-japan-diffusion-2-1-0/settings'>Settings</a></b>"} <br>
              <a style="display:inline-block" href="https://huggingface.co/spaces/aipicasso/emi-latest-demo?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> to say goodbye from waiting for the generating.
            </div>
        """
    )
    with gr.Row():
        
        with gr.Column(scale=55):
          with gr.Group():
              
              with gr.Row():
                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="[your prompt]")
                generate = gr.Button(value="Generate")

              image_out = gr.Image(height=768,width=1344)
          error_output = gr.Markdown()

        with gr.Column(scale=45):
          with gr.Tab("Options"):
            with gr.Group():
              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
              disable_auto_prompt_correction = gr.Checkbox(label="Disable auto prompt corretion.")
              with gr.Row():
                image_size=gr.Radio(["Portrait","Landscape","Square"])
                image_size.show_label=False
                image_size.value="Square"
                
              with gr.Row():
                guidance = gr.Slider(label="Guidance scale", value=10, maximum=25)
                steps = gr.Slider(label="Steps", value=20, minimum=2, maximum=75, step=1)

              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)

          with gr.Tab("Image to image"):
              with gr.Group():
                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
                  
    inputs = [prompt, guidance, steps, image_size, seed, image, strength, neg_prompt, disable_auto_prompt_correction]

    outputs = [image_out, error_output]
    prompt.submit(inference, inputs=inputs, outputs=outputs)
    generate.click(inference, inputs=inputs, outputs=outputs)
    
demo.queue(concurrency_count=1)
demo.launch()