added sd2

app.py

@@ -0,0 +1,401 @@
+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)

requirements.txt

@@ -0,0 +1,8 @@
+diffusers
+transformers
+accelerate
+scipy
+triton
+ftfy
+gradio
+https://github.com/TheLastBen/fast-stable-diffusion/raw/main/precompiled/T4/xformers-0.0.13.dev0-py3-none-any.whl