app.py

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

state = None
current_steps = 25

model_id = 'stabilityai/stable-diffusion-2'

scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")

pipe = StableDiffusionPipeline.from_pretrained(
      model_id,
      revision="fp16" if torch.cuda.is_available() else "fp32",
      torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
      scheduler=scheduler
    ).to("cuda")
pipe.enable_attention_slicing()
pipe.enable_xformers_memory_efficient_attention()

pipe_i2i = None
pipe_upscale = None
pipe_inpaint = None

attn_slicing_enabled = True
mem_eff_attn_enabled = True

modes = {
    'txt2img': 'Text to Image',
    'img2img': 'Image to Image',
    'inpaint': 'Inpainting',
    'upscale4x': 'Upscale 4x',
}
current_mode = modes['txt2img']

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

def update_state(new_state):
  global state
  state = new_state

def update_state_info(old_state):
  if state and state != old_state:
    return gr.update(value=state)

def set_mem_optimizations(pipe):
    if attn_slicing_enabled:
      pipe.enable_attention_slicing()
    else:
      pipe.disable_attention_slicing()
    
    if mem_eff_attn_enabled:
      pipe.enable_xformers_memory_efficient_attention()
    else:
      pipe.disable_xformers_memory_efficient_attention()

def get_i2i_pipe(scheduler):
    
    update_state("Loading image to image model...")

    pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
      model_id,
      revision="fp16" if torch.cuda.is_available() else "fp32",
      torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
      scheduler=scheduler
    )
    set_mem_optimizations(pipe)
    pipe.to("cuda")
    return pipe

def get_inpaint_pipe():
  
  update_state("Loading inpainting model...")

  pipe = DiffusionPipeline.from_pretrained(
      "stabilityai/stable-diffusion-2-inpainting",
      revision="fp16" if torch.cuda.is_available() else "fp32",
      torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
      # scheduler=scheduler # TODO currently setting scheduler here messes up the end result. A bug in Diffusers🧨
    ).to("cuda")
  pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
  pipe.enable_attention_slicing()
  pipe.enable_xformers_memory_efficient_attention()
  return pipe

def get_upscale_pipe(scheduler):
    
    update_state("Loading upscale model...")

    pipe = StableDiffusionUpscalePipeline.from_pretrained(
      "stabilityai/stable-diffusion-x4-upscaler",
      revision="fp16" if torch.cuda.is_available() else "fp32",
      torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
      # scheduler=scheduler
    )
    # pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
    set_mem_optimizations(pipe)
    pipe.to("cuda")
    return pipe

def switch_attention_slicing(attn_slicing):
    global attn_slicing_enabled
    attn_slicing_enabled = attn_slicing

def switch_mem_eff_attn(mem_eff_attn):
    global mem_eff_attn_enabled
    mem_eff_attn_enabled = mem_eff_attn

def pipe_callback(step: int, timestep: int, latents: torch.FloatTensor):
    update_state(f"{step}/{current_steps} steps")#\nTime left, sec: {timestep/100:.0f}")

def inference(inf_mode, prompt, n_images, guidance, steps, width=768, height=768, seed=0, img=None, strength=0.5, neg_prompt=""):

  update_state(" ")

  global current_mode
  if inf_mode != current_mode:
    pipe.to("cuda" if inf_mode == modes['txt2img'] else "cpu")

    if pipe_i2i is not None:
      pipe_i2i.to("cuda" if inf_mode == modes['img2img'] else "cpu")

    if pipe_inpaint is not None:
      pipe_inpaint.to("cuda" if inf_mode == modes['inpaint'] else "cpu")

    if pipe_upscale is not None:
      pipe_upscale.to("cuda" if inf_mode == modes['upscale4x'] else "cpu")

    current_mode = inf_mode

  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
  prompt = prompt

  try:
    
    if inf_mode == modes['txt2img']:
      return txt_to_img(prompt, n_images, neg_prompt, guidance, steps, width, height, generator), gr.update(visible=False, value=None)
    
    elif inf_mode == modes['img2img']:
      if img is None:
        return None, gr.update(visible=True, value=error_str("Image is required for Image to Image mode"))

      return img_to_img(prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator), gr.update(visible=False, value=None)
    
    elif inf_mode == modes['inpaint']:
      if img is None:
        return None, gr.update(visible=True, value=error_str("Image is required for Inpainting mode"))

      return inpaint(prompt, n_images, neg_prompt, img, guidance, steps, width, height, generator), gr.update(visible=False, value=None)

    elif inf_mode == modes['upscale4x']:
      if img is None:
        return None, gr.update(visible=True, value=error_str("Image is required for Upscale mode"))

      return upscale(prompt, n_images, neg_prompt, img, guidance, steps, generator), gr.update(visible=False, value=None)
  except Exception as e:
    return None, gr.update(visible=True, value=error_str(e))

def txt_to_img(prompt, n_images, neg_prompt, guidance, steps, width, height, generator):

    result = pipe(
      prompt,
      num_images_per_prompt = n_images,
      negative_prompt = neg_prompt,
      num_inference_steps = int(steps),
      guidance_scale = guidance,
      width = width,
      height = height,
      generator = generator,
      callback=pipe_callback).images

    update_state("")

    return result

def img_to_img(prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator):

    global pipe_i2i
    if pipe_i2i is None:
      pipe_i2i = get_i2i_pipe(scheduler)

    img = img['image']
    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,
      num_images_per_prompt = n_images,
      negative_prompt = neg_prompt,
      init_image = img,
      num_inference_steps = int(steps),
      strength = strength,
      guidance_scale = guidance,
      width = width,
      height = height,
      generator = generator,
      callback=pipe_callback).images

    update_state("")
        
    return result

# TODO Currently supports only 512x512 images
def inpaint(prompt, n_images, neg_prompt, img, guidance, steps, width, height, generator):

    global pipe_inpaint
    if pipe_inpaint is None:
      pipe_inpaint = get_inpaint_pipe()

    inp_img = img['image']
    mask = img['mask']
    inp_img = square_padding(inp_img)
    mask = square_padding(mask)

    # # ratio = min(height / inp_img.height, width / inp_img.width)
    # ratio = min(512 / inp_img.height, 512 / inp_img.width)
    # inp_img = inp_img.resize((int(inp_img.width * ratio), int(inp_img.height * ratio)), Image.LANCZOS)
    # mask = mask.resize((int(mask.width * ratio), int(mask.height * ratio)), Image.LANCZOS)

    inp_img = inp_img.resize((512, 512))
    mask = mask.resize((512, 512))

    result = pipe_inpaint(
      prompt,
      image = inp_img,
      mask_image = mask,
      num_images_per_prompt = n_images,
      negative_prompt = neg_prompt,
      num_inference_steps = int(steps),
      guidance_scale = guidance,
      # width = width,
      # height = height,
      generator = generator,
      callback=pipe_callback).images
        
    update_state("")

    return result

def square_padding(img):
    width, height = img.size
    if width == height:
        return img
    new_size = max(width, height)
    new_img = Image.new('RGB', (new_size, new_size), (0, 0, 0, 255))
    new_img.paste(img, ((new_size - width) // 2, (new_size - height) // 2))
    return new_img

def upscale(prompt, n_images, neg_prompt, img, guidance, steps, generator):

    global pipe_upscale
    if pipe_upscale is None:
      pipe_upscale = get_upscale_pipe(scheduler)

    img = img['image']
    return upscale_tiling(prompt, neg_prompt, img, guidance, steps, generator)

    # result = pipe_upscale(
    #     prompt,
    #     image = img,
    #     num_inference_steps = int(steps),
    #     guidance_scale = guidance,
    #     negative_prompt = neg_prompt,
    #     num_images_per_prompt = n_images,
    #     generator = generator).images[0]

    # return result

def upscale_tiling(prompt, neg_prompt, img, guidance, steps, generator):

    width, height = img.size

    # calculate the padding needed to make the image dimensions a multiple of 128
    padding_x = 128 - (width % 128) if width % 128 != 0 else 0
    padding_y = 128 - (height % 128) if height % 128 != 0 else 0

    # create a white image of the right size to be used as padding
    padding_img = Image.new('RGB', (padding_x, padding_y), color=(255, 255, 255, 0))

    # paste the padding image onto the original image to add the padding
    img.paste(padding_img, (width, height))

    # update the image dimensions to include the padding
    width += padding_x
    height += padding_y

    if width > 128 or height > 128:

        num_tiles_x = int(width / 128)
        num_tiles_y = int(height / 128)

        upscaled_img = Image.new('RGB', (img.size[0] * 4, img.size[1] * 4))
        for x in range(num_tiles_x):
            for y in range(num_tiles_y):
                update_state(f"Upscaling tile {x * num_tiles_y + y + 1}/{num_tiles_x * num_tiles_y}")
                tile = img.crop((x * 128, y * 128, (x + 1) * 128, (y + 1) * 128))

                upscaled_tile = pipe_upscale(
                    prompt="",
                    image=tile,
                    num_inference_steps=steps,
                    guidance_scale=guidance,
                    # negative_prompt = neg_prompt,
                    generator=generator,
                ).images[0]

                upscaled_img.paste(upscaled_tile, (x * upscaled_tile.size[0], y * upscaled_tile.size[1]))

        return [upscaled_img]
    else:
        return pipe_upscale(
            prompt=prompt,
            image=img,
            num_inference_steps=steps,
            guidance_scale=guidance,
            negative_prompt = neg_prompt,
            generator=generator,
        ).images



def on_mode_change(mode):
  return gr.update(visible = mode in (modes['img2img'], modes['inpaint'], modes['upscale4x'])), \
         gr.update(visible = mode == modes['inpaint']), \
         gr.update(visible = mode == modes['upscale4x']), \
         gr.update(visible = mode == modes['img2img'])

def on_steps_change(steps):
  global current_steps
  current_steps = steps

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>Stable Diffusion 2</h1>
            </div><br>
            <p> Model used: <a href="https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/768-v-ema.ckpt" target="_blank">768-v-ema.ckpt</a></p>
            Running on <b>{"GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"}</b>
          </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"Enter prompt").style(container=False)
                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))

              gallery = gr.Gallery(label="Generated images", show_label=False).style(grid=[2], height="auto")
          state_info = gr.Textbox(label="State", show_label=False, max_lines=2).style(container=False)
          error_output = gr.Markdown(visible=False)

        with gr.Column(scale=45):
          inf_mode = gr.Radio(label="Inference Mode", choices=list(modes.values())[:4], value=modes['txt2img']) # TODO remove [:3] limit
          
          with gr.Group(visible=False) as i2i_options:
            image = gr.Image(label="Image", height=128, type="pil", tool='sketch')
            inpaint_info = gr.Markdown("Inpainting resizes and pads images to 512x512", visible=False)
            upscale_info = gr.Markdown("""Best for small images (128x128 or smaller).<br>
                                        Bigger images will be sliced into 128x128 tiles which will be upscaled individually.<br>
                                        This is done to avoid running out of GPU memory.""", visible=False)
            strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)

          with gr.Group():
            neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")

            n_images = gr.Slider(label="Number of images", value=1, minimum=1, maximum=4, step=1)
            with gr.Row():
              guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
              steps = gr.Slider(label="Steps", value=current_steps, minimum=2, maximum=100, step=1)

            with gr.Row():
              width = gr.Slider(label="Width", value=768, minimum=64, maximum=1024, step=8)
              height = gr.Slider(label="Height", value=768, minimum=64, maximum=1024, step=8)

            seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
            with gr.Accordion("Memory optimization"):
              attn_slicing = gr.Checkbox(label="Attention slicing (a bit slower, but uses less memory)", value=attn_slicing_enabled)
              mem_eff_attn = gr.Checkbox(label="Memory efficient attention (xformers)", value=mem_eff_attn_enabled)

    inf_mode.change(on_mode_change, inputs=[inf_mode], outputs=[i2i_options, inpaint_info, upscale_info, strength], queue=False)
    steps.change(on_steps_change, inputs=[steps], outputs=[], queue=False)
    attn_slicing.change(lambda x: switch_attention_slicing(x), inputs=[attn_slicing], queue=False)
    mem_eff_attn.change(lambda x: switch_mem_eff_attn(x), inputs=[mem_eff_attn], queue=False)

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

    demo.load(update_state_info, inputs=state_info, outputs=state_info, every=0.5, show_progress=False)

demo.queue()
demo.launch(debug=True, share=True, height=768)