app.py

from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
import gradio as gr
import torch
from PIL import Image

model_id = 'AlekseyCalvin/asoon-dreambooth-sd-model'
prefix = 'asoon'
     
scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")

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

pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
  model_id,
  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
  scheduler=scheduler)

if torch.cuda.is_available():
  pipe = pipe.to("cuda")
  pipe_i2i = pipe_i2i.to("cuda")

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

def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):

  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
  prompt = f"{prefix} {prompt}" if auto_prefix else 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 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>Create & Share Your Own A.C.T. SOON®! Get song/poem!</h1>
              </div>
              <p>
               SD model (2.1 fine-tuned). Create your own A.C.T. SOON®, aka this typing self. A sample-bunch of training photos lives on the model page. To be sure, I've worked as a model before, and as an actor too...  Even if such roles refract but a tiny fraction of my eclectic existential history. Now, if suchlike lived histories are oft wrought by fates, paths, peers, posts, and other age-old forming ghosts, these virtualized forms of ours may still (even near 2023) suggest input frames yet-unprecedented. In that spirit, I welcome all those so inclined to transform, morph, or warp this here deworlded, cybermodeled, and conveniently promptable form of mine. Create what you want with it using the token 'asoon' or 'asoon person'.  Then share any particularly inspired or/and inspiring results with me directly at [email protected]. My several favorite shares (if there's any interest at all) shall be reciprocated with the creators' choice of: |a live recording of an original song inspired by the share| OR |a fresh translation (non-machine) of an early Soviet-era modernist poem likewise picked out in response to the art|. Voice your preference in the email in the form of an irl prompt for me!
<br>
               {"Add the following tokens to your prompts for the model to work properly: <b>prefix</b>" if prefix else ""}
              </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/AlekseyCalvin/Create-And-Share-ACTsoonrs/settings'>Settings</a></b>"} after duplicating the space<br><br>
              <a style="display:inline-block" href="https://huggingface.co./spaces/AlekseyCalvin/Create-And-Share-ACTsoonrs?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
            </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=f"{prefix} [your prompt]").style(container=False)
                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))

              image_out = gr.Image(height=512)
          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")
              auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (asoon)", value=prefix, visible=prefix)

              with gr.Row():
                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
                steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)

              with gr.Row():
                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)

              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)

    auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)

    inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
    outputs = [image_out, error_output]
    prompt.submit(inference, inputs=inputs, outputs=outputs)
    generate.click(inference, inputs=inputs, outputs=outputs)

    gr.HTML("""
    <div style="border-top: 1px solid #303030;">
      <br>
      <p>This space was created using <a href="https://huggingface.co./spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
    </div>
    """)

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