app.py

from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, EulerAncestralDiscreteScheduler
from transformers import CLIPFeatureExtractor
import gradio as gr
import torch
from PIL import Image
import random
import os
from huggingface_hub import hf_hub_download


model_id = 'aipicasso/picasso-diffusion-1-0-demo'
auth_token=os.environ.get("ACCESS_TOKEN")

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

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

pipe_i2i_merged = StableDiffusionImg2ImgPipeline.from_pretrained(
  model_id,
  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
  scheduler=scheduler,
  requires_safety_checker=False,
  safety_checker=None,
  feature_extractor=feature_extractor, use_auth_token=auth_token
)

pipe=pipe_merged.to("cuda")
pipe_i2i=pipe_i2i_merged.to("cuda")
pipe.enable_xformers_memory_efficient_attention()
pipe_i2i.enable_xformers_memory_efficient_attention()

embeddings_path=hf_hub_download(repo_id=model_id, filename="nfixer.pt", use_auth_token=auth_token)
embeddings_dict=torch.load(embeddings_path)
print(embeddings_dict)
if "string_to_param" in embeddings_dict:
    embeddings = next(iter(embeddings_dict['string_to_param'].values()))
    nfixer = ""
    for i, emb in enumerate(embeddings):
        token = f"sksd{chr(i+65)}"
        nfixer += token
        pipe.tokenizer.add_tokens(token)
        pipe.text_encoder.resize_token_embeddings(len(pipe.tokenizer))
        token_id = pipe.tokenizer.convert_tokens_to_ids(token)
        pipe.text_encoder.get_input_embeddings().weight.data[token_id] = emb
else:
    nfixer = list(embeddings_dict.keys())[0]
    embeddings = embeddings_dict[nfixer].to(pipe.text_encoder.get_input_embeddings().weight.dtype)
    pipe.tokenizer.add_tokens(placeholder_token)
    pipe.text_encoder.resize_token_embeddings(len(pipe.tokenizer))
    placeholder_token_id = pipe.tokenizer.convert_tokens_to_ids(placeholder_token)
    pipe.text_encoder.get_input_embeddings().weight.data[placeholder_token_id] = embeddings

embeddings_path=hf_hub_download(repo_id=model_id, filename="embellish2.pt", use_auth_token=auth_token)
embeddings_dict=torch.load(embeddings_path)
print(embeddings_dict)
if "string_to_param" in embeddings_dict:
    embeddings = next(iter(embeddings_dict['string_to_param'].values()))
    embellish2 = ""
    for i, emb in enumerate(embeddings):
        token = f"kskd{chr(i%26+65)}{chr(i//26+65)}"
        embellish2 += token
        pipe.tokenizer.add_tokens(token)
        pipe.text_encoder.resize_token_embeddings(len(pipe.tokenizer))
        token_id = pipe.tokenizer.convert_tokens_to_ids(token)
        pipe.text_encoder.get_input_embeddings().weight.data[token_id] = emb
else:
    embellish2 = list(embeddings_dict.keys())[0]
    embeddings = embeddings_dict[embellish2].to(pipe.text_encoder.get_input_embeddings().weight.dtype)
    pipe.tokenizer.add_tokens(placeholder_token)
    pipe.text_encoder.resize_token_embeddings(len(pipe.tokenizer))
    placeholder_token_id = pipe.tokenizer.convert_tokens_to_ids(placeholder_token)
    pipe.text_encoder.get_input_embeddings().weight.data[placeholder_token_id] = embeddings

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

def inference(prompt, guidance, steps, image_size="Square", seed=0, img=None, strength=0.5, neg_prompt="", disable_auto_prompt_correction=False, image_style="Realistic", original_model=False):
  global pipe,pipe_i2i
  
  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,image_style)

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

  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,image_style):
    # auto prompt correction
    prompt=str(prompt_ui)
    neg_prompt=str(neg_prompt_ui)
    prompt=prompt.lower()
    neg_prompt=neg_prompt.lower()

    if(image_style=="Animetic"):
        style="anime"
    else:
        style=f"anime,{embellish2}"
    
    if(disable_auto_prompt_correction):
        prompt=f"{style}, {prompt}"
        return prompt, neg_prompt

    if(prompt=="" and neg_prompt==""):
        prompt=f"{style}, masterpiece, portrait, a girl with flowers, good pupil, detailed"
        neg_prompt=f"{nfixer},(((deformed))), blurry, ((((bad anatomy)))),3d, cg, text , bad pupil, disfigured, poorly drawn face, mutation, mutated, (extra limb), (ugly), (poorly drawn hands), bad hands, fused fingers, messy drawing, broken legs censor, low quality, (mutated hands and fingers:1.5), (long body :1.3), (mutation, poorly drawn :1.2), ((bad eyes)), ui, error, missing fingers, fused fingers, one hand with more than 5 fingers, one hand with less than 5 fingers, one hand with more than 5 digit, one hand with less than 5 digit, extra digit, fewer digits, fused digit, missing digit, bad digit, liquid digit, long body, uncoordinated body, unnatural body, lowres, jpeg artifacts"
        return prompt, neg_prompt

    splited_prompt=prompt.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"{style}, masterpiece, {prompt}, good pupil, detailed"
            neg_prompt=f"{nfixer},(((deformed))), blurry, ((((bad anatomy)))), {neg_prompt}, 3d, cg, text, bad pupil, disfigured, poorly drawn face, mutation, mutated, (extra limb), (ugly), (poorly drawn hands), bad hands, fused fingers, messy drawing, broken legs censor, low quality, (mutated hands and fingers:1.5), (long body :1.3), (mutation, poorly drawn :1.2), ((bad eyes)), ui, error, missing fingers, fused fingers, one hand with more than 5 fingers, one hand with less than 5 fingers, one hand with more than 5 digit, one hand with less than 5 digit, extra digit, fewer digits, fused digit, missing digit, bad digit, liquid digit, long body, uncoordinated body, unnatural body, lowres, jpeg artifacts"
            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"{style}, a {prompt}, 4k, detailed"
            neg_prompt=f"{nfixer}, girl, (((deformed))), blurry, ((((bad anatomy)))), {neg_prompt}, 3d, cg, text, bad pupil, disfigured, poorly drawn face, mutation, mutated, (extra limb), (ugly), (poorly drawn hands), bad hands, fused fingers, messy drawing, broken legs censor, low quality, (mutated hands and fingers:1.5), (long body :1.3), (mutation, poorly drawn :1.2), ((bad eyes)), ui, error, missing fingers, fused fingers, one hand with more than 5 fingers, one hand with less than 5 fingers, one hand with more than 5 digit, one hand with less than 5 digit, extra digit, fewer digits, fused digit, missing digit, bad digit, liquid digit, long body, uncoordinated body, unnatural body, lowres, jpeg artifacts"
            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"{style}, shinkai makoto, {word}, 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):
    result = pipe(
      prompt,
      negative_prompt = neg_prompt,
      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,
        init_image = img,
        num_inference_steps = int(steps),
        strength = strength,
        guidance_scale = guidance,
        #width = width,
        #height = height,
        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>Picasso Diffusion 1.0 +α Demo</h1>
              </div>
              <p>
               Demo for <a href="https://huggingface.co/aipicasso/picasso-diffusion-1-0">Picasso Diffusion 1.0 (Comming soon)</a> + <a href="https://huggingface.co/IlluminatiAI/Illuminati_Diffusion_v1.0">Illumi. Diffusion</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 : girl, kimono
              </p>
              <p>
              sample prompt2 : boy, armor
              </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/picasso-diffusion-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():
                image_style=gr.Radio(["Realistic","Animetic"])
                image_style.show_label=False
                image_style.value="Realistic"
            
              with gr.Row():
                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="[your prompt]").style(container=False)
                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))

              image_out = gr.Image(height=768,width=768)
          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.")
              #original_model = gr.Checkbox(label="Change the model into the original model.")
              with gr.Row():
                image_size=gr.Radio(["Portrait","Landscape","Square","Highreso."])
                image_size.show_label=False
                image_size.value="Square"
                
              with gr.Row():
                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
                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,image_style]#, original_model]

    outputs = [image_out, error_output]
    prompt.submit(inference, inputs=inputs, outputs=outputs)
    generate.click(inference, inputs=inputs, outputs=outputs,api_name="generate")

demo.queue(concurrency_count=1)
demo.launch()