Duplicate from SUPERSHANKY/Finetuned_Diffusion_Max

Co-authored-by: Sankalp Pateriya <[email protected]>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+title: Finetuned Diffusion
+emoji: 🪄🖼️
+colorFrom: red
+colorTo: pink
+sdk: gradio
+sdk_version: 3.16.2
+app_file: app.py
+pinned: true
+license: mit
+duplicated_from: SUPERSHANKY/Finetuned_Diffusion_Max
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
+import gradio as gr
+import torch
+from PIL import Image
+import utils
+import datetime
+import time
+import psutil
+import random
+
+
+start_time = time.time()
+is_colab = utils.is_google_colab()
+state = None
+current_steps = 25
+
+class Model:
+    def __init__(self, name, path="", prefix=""):
+        self.name = name
+        self.path = path
+        self.prefix = prefix
+        self.pipe_t2i = None
+        self.pipe_i2i = None
+
+models = [
+     Model("Dreamlike Diffusion 1.0", "dreamlike-art/dreamlike-diffusion-1.0", "dreamlikeart "),
+     Model("Dreamlike Photoreal 2.0", "dreamlike-art/dreamlike-photoreal-2.0", ""),
+     Model("Eimis Anime 1.0", "flax/EimisAnimeDiffusion_1.0v", ""),
+     Model("Eimis SemiRealistic", "eimiss/EimisSemiRealistic", ""),
+     Model("Portrait Plus", "wavymulder/portraitplus", "portrait+ style "),
+     Model("Protogen 5.3 (for plain realism, a bit bland)", "darkstorm2150/Protogen_v5.3_Official_Release", ""),
+     Model("Protogen 5.8 (for realism, but toward fantasy)", "darkstorm2150/Protogen_v5.8_Official_Release", ""),
+     Model("Protogen Dragon (for fantasy)", "darkstorm2150/Protogen_Dragon_Official_Release", ""),
+     Model("Protogen Nova (the all in one)", "darkstorm2150/Protogen_Nova_Official_Release", ""),
+     Model("Seek.Art Mega", "coreco/seek.art_MEGA", ""),
+     Model("Uber Realistic Porn Merge","PrimaPramudya/uberRealisticPrnMer_urpMv11", ""),
+     Model("Vintedois 0.1", "22h/vintedois-diffusion-v0-1", ""),
+     Model("Analog Diffusion", "wavymulder/Analog-Diffusion", "analog style "),
+     Model("Anything V3", "Linaqruf/anything-v3.0", ""),
+     Model("Arcane", "nitrosocke/Arcane-Diffusion", "arcane style "),
+     Model("Archer", "nitrosocke/archer-diffusion", "archer style "),
+     Model("Cyberpunk Anime", "DGSpitzer/Cyberpunk-Anime-Diffusion", "dgs illustration style "),
+     Model("Disney, modern", "nitrosocke/mo-di-diffusion", "modern disney style "),
+     Model("Disney, Classic", "nitrosocke/classic-anim-diffusion", "classic disney style "),
+     Model("DnD Item", "stale2000/sd-dnditem", "dnditem "),
+     Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "),
+     Model("f222 Zeipfher", "m4gnett/zeipher-f222", ""),
+     Model("f222 + Anything V3", "m4gnett/anything-of-f222", ""),
+     Model("Loving Vincent (Van Gogh)", "dallinmackay/Van-Gogh-diffusion", "lvngvncnt "),
+     Model("Midjourney v4 style", "prompthero/openjourney", "mdjrny-v4 style "),
+     Model("Pokémon", "lambdalabs/sd-pokemon-diffusers"),
+     Model("Pony Diffusion", "AstraliteHeart/pony-diffusion"),
+     Model("Redshift renderer (Cinema4D)", "nitrosocke/redshift-diffusion", "redshift style "),
+     Model("Robo Diffusion", "nousr/robo-diffusion"),
+     Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "),
+     Model("TrinArt v2", "naclbit/trinart_stable_diffusion_v2"),
+     Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy "),
+     Model("Waifu", "hakurei/waifu-diffusion"),
+     Model("Wavyfusion", "wavymulder/wavyfusion", "wa-vy style "),
+     Model("Balloon Art", "Fictiverse/Stable_Diffusion_BalloonArt_Model", "BalloonArt "),
+     Model("Anything V3 Better-Vae", "Linaqruf/anything-v3-better-vae", ""),
+     Model("Anything V4", "andite/anything-v4.0", ""),
+     Model("Cyberpunk Anime with Genshin Characters supported", "AdamOswald1/Cyberpunk-Anime-Diffusion_with_support_for_Gen-Imp_characters", "cyberpunk style"),
+     Model("Dark Souls", "Guizmus/DarkSoulsDiffusion", "dark souls style"),
+     Model("Space Machine", "rabidgremlin/sd-db-epic-space-machine", "EpicSpaceMachine"),
+     Model("Spacecraft", "rabidgremlin/sd-db-epic-space-machine, Guizmus/Tardisfusion", "EpicSpaceMachine, Tardis Box style"),
+     Model("TARDIS", "Guizmus/Tardisfusion", "Tardis Box style"),
+     Model("Modern Era TARDIS Interior", "Guizmus/Tardisfusion", "Modern Tardis style"),
+     Model("Classic Era TARDIS Interior", "Guizmus/Tardisfusion", "Classic Tardis style"),
+     Model("Spacecraft Interior", "Guizmus/Tardisfusion, rabidgremlin/sd-db-epic-space-machine", "Classic Tardis style, Modern Tardis style, EpicSpaceMachine"),
+     Model("CLIP", "EleutherAI/clip-guided-diffusion", "CLIP"),
+     Model("Genshin Waifu", "crumb/genshin-stable-inversion, yuiqena/GenshinImpact, katakana/2D-Mix, Guizmus/AnimeChanStyle", "Female, female, Woman, woman, Girl, girl"),
+     Model("Genshin", "crumb/genshin-stable-inversion, yuiqena/GenshinImpact, katakana/2D-Mix, Guizmus/AnimeChanStyle", ""),
+     Model("Test", "AdamOswald1/Idk", ""),
+     Model("Test2", "AdamOswald1/Tester", ""),
+     Model("Anime", "Guizmus/AnimeChanStyle, katakana/2D-Mix", ""),
+     Model("Beeple", "riccardogiorato/beeple-diffusion", "beeple style "),
+     Model("Avatar", "riccardogiorato/avatar-diffusion", "avatartwow style "),
+     Model("Poolsuite", "prompthero/poolsuite", "poolsuite style "),
+     Model("Epic Diffusion", "johnslegers/epic-diffusion", ""),
+     Model("Comic Diffusion", "ogkalu/Comic-Diffusion", ""),
+     Model("Realistic Vision 1.2", "SG161222/Realistic_Vision_V1.2", ""),
+     Model("Stable Diffusion 2.1", "stabilityai/stable-diffusion-2-1", ""),
+     Model("OrangeMixs", "WarriorMama777/OrangeMixs", "Abyss"),
+     Model("Inkpunk-Diffusion", "Envvi/Inkpunk-Diffusion", "nvinkpunk"),
+     Model("openjourney-v2", "prompthero/openjourney-v2", ""),
+     Model("hassenblend 1.4", "hassanblend/hassanblend1.4", ""),
+     Model("Cyberpunk-Anime-Diffusion", "DGSpitzer/Cyberpunk-Anime-Diffusion", "DGS Illustration style"),
+     Model("Ghibli-Diffusion", "nitrosocke/Ghibli-Diffusion", "ghibli style"),
+     Model("Pastel-Mix", "andite/pastel-mix", "mksks style"),
+     Model("trinart_stable_diffusion_v2", "naclbit/trinart_stable_diffusion_v2", ""),
+     Model("Counterfeit-V2.0", "gsdf/Counterfeit-V2.0", ""),
+     Model("stable diffusion 2.1 base", "stabilityai/stable-diffusion-2-1-base", ""),
+     Model("Double Exposure Diffusion", "joachimsallstrom/Double-Exposure-Diffusion", "dublex style, dublex"),
+     Model("Yohan Diffusion", "andite/yohan-diffusion", ""),
+     Model("rMadArt2.5", "rmada/rMadArt2.5", ""),
+     Model("unico", "Cinnamomo/unico", ""),
+     Model("Inizio", "Cinnamomo/inizio", ""),
+     Model("HARDblend", "theintuitiveye/HARDblend", "photorealistic, instagram photography, shot on iphone, RAW, professional photograph"),
+     Model("FantasyMix-v1", "theintuitiveye/FantasyMix-v1", ""),
+     Model("modernartstyle", "theintuitiveye/modernartstyle", "modernartst"),
+     Model("paint-jpurney-v2", "FredZhang7/paint-journey-v2", "oil painting"),
+     Model("Sygil-Diffusion", "Sygil/Sygil-Diffusion", ""),
+     Model("g_yuusukeStyle", "grullborg/g_yuusukeStyle", ""),
+     Model("th-diffusion", "furusu/th-diffusion", "realistic"),
+     Model("SD_Black_Ancient_Egyptian_Style", "Akumetsu971/SD_Black_Ancient_Egyptian_Style", "Bck_Egpt"),
+     Model("Shortjourney", "x67/shortjourney", "sjrny-v1 style"),
+     Model("Kenshi", "SweetLuna/Kenshi", ""),
+     Model("lomo-diffusion", "wavymulder/lomo-diffusion", "lomo style"),
+     Model("RainerMix", "Hemlok/RainierMix", ""),
+     Model("GuoFeng3", "xiaolxl/GuoFeng3", ""),
+     Model("sketchstyle-cutesexyrobutts", "Cosk/sketchstyle-cutesexyrobutts", ""),
+     Model("Counterfeit-V2.5", "gsdf/Counterfeit-V2.5", ""),
+     Model("TriPhaze", "Lucetepolis/TriPhaze", ""),
+     Model("SukiyakiMix-1.0", "Vsukiyaki/SukiyakiMix-v1.0", ""),
+     Model("icon-diffusion-v1-1", "crumb/icon-diffusion-v1-1", ""),
+     Model("Strange_Dedication", "MortalSage/Strange_Dedication", ""),
+     Model("openjourney-v2", "prompthero/openjourney-v2", ""),
+     Model("Funko-Diffusion", "prompthero/funko-diffusion", "funko style"),
+     Model("DreamShaper", "Lykon/DreamShaper", "dreamshaper"),
+     Model("Realistic_Vision_V1.4", "SG161222/Realistic_Vision_V1.4", ""),
+
+
+
+
+
+]
+
+custom_model = None
+if is_colab:
+  models.insert(0, Model("Custom model"))
+  custom_model = models[0]
+
+last_mode = "txt2img"
+current_model = models[1] if is_colab else models[0]
+current_model_path = current_model.path
+
+if is_colab:
+  pipe = StableDiffusionPipeline.from_pretrained(
+      current_model.path,
+      torch_dtype=torch.float16,
+      scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
+      safety_checker=None
+      )
+
+else:
+  pipe = StableDiffusionPipeline.from_pretrained(
+      current_model.path,
+      torch_dtype=torch.float16,
+      scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
+      )
+    
+if torch.cuda.is_available():
+  pipe = pipe.to("cuda")
+  pipe.enable_xformers_memory_efficient_attention()
+
+device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
+
+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 custom_model_changed(path):
+  models[0].path = path
+  global current_model
+  current_model = models[0]
+
+def on_model_change(model_name):
+  
+  prefix = "Enter prompt. \"" + next((m.prefix for m in models if m.name == model_name), None) + "\" is prefixed automatically" if model_name != models[0].name else "Don't forget to use the custom model prefix in the prompt!"
+
+  return gr.update(visible = model_name == models[0].name), gr.update(placeholder=prefix)
+
+def on_steps_change(steps):
+  global current_steps
+  current_steps = steps
+
+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(model_name, prompt, guidance, steps, n_images=1, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
+
+  update_state(" ")
+
+  print(psutil.virtual_memory()) # print memory usage
+
+  global current_model
+  for model in models:
+    if model.name == model_name:
+      current_model = model
+      model_path = current_model.path
+
+  # generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
+  if seed == 0:
+    seed = random.randint(0, 2147483647)
+
+  generator = torch.Generator('cuda').manual_seed(seed)
+
+  try:
+    if img is not None:
+      return img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed), f"Done. Seed: {seed}"
+    else:
+      return txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed), f"Done. Seed: {seed}"
+  except Exception as e:
+    return None, error_str(e)
+
+def txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed):
+
+    print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}")
+
+    global last_mode
+    global pipe
+    global current_model_path
+    if model_path != current_model_path or last_mode != "txt2img":
+        current_model_path = model_path
+
+        update_state(f"Loading {current_model.name} text-to-image model...")
+
+        if is_colab or current_model == custom_model:
+          pipe = StableDiffusionPipeline.from_pretrained(
+              current_model_path,
+              torch_dtype=torch.float16,
+              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
+              safety_checker=None
+              )
+        else:
+          pipe = StableDiffusionPipeline.from_pretrained(
+              current_model_path,
+              torch_dtype=torch.float16,
+              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
+              )
+          # pipe = pipe.to("cpu")
+          # pipe = current_model.pipe_t2i
+
+        if torch.cuda.is_available():
+          pipe = pipe.to("cuda")
+          pipe.enable_xformers_memory_efficient_attention()
+        last_mode = "txt2img"
+
+    prompt = current_model.prefix + prompt  
+    result = pipe(
+      prompt,
+      negative_prompt = neg_prompt,
+      num_images_per_prompt=n_images,
+      num_inference_steps = int(steps),
+      guidance_scale = guidance,
+      width = width,
+      height = height,
+      generator = generator,
+      callback=pipe_callback)
+
+    # update_state(f"Done. Seed: {seed}")
+    
+    return replace_nsfw_images(result)
+
+def img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed):
+
+    print(f"{datetime.datetime.now()} img_to_img, model: {model_path}")
+
+    global last_mode
+    global pipe
+    global current_model_path
+    if model_path != current_model_path or last_mode != "img2img":
+        current_model_path = model_path
+
+        update_state(f"Loading {current_model.name} image-to-image model...")
+
+        if is_colab or current_model == custom_model:
+          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+              current_model_path,
+              torch_dtype=torch.float16,
+              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
+              safety_checker=None
+              )
+        else:
+          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+              current_model_path,
+              torch_dtype=torch.float16,
+              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
+              )
+          # pipe = pipe.to("cpu")
+          # pipe = current_model.pipe_i2i
+        
+        if torch.cuda.is_available():
+          pipe = pipe.to("cuda")
+          pipe.enable_xformers_memory_efficient_attention()
+        last_mode = "img2img"
+
+    prompt = current_model.prefix + prompt
+    ratio = min(height / img.height, width / img.width)
+    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
+    result = pipe(
+        prompt,
+        negative_prompt = neg_prompt,
+        num_images_per_prompt=n_images,
+        image = img,
+        num_inference_steps = int(steps),
+        strength = strength,
+        guidance_scale = guidance,
+        # width = width,
+        # height = height,
+        generator = generator,
+        callback=pipe_callback)
+
+    # update_state(f"Done. Seed: {seed}")
+        
+    return replace_nsfw_images(result)
+
+def replace_nsfw_images(results):
+
+    if is_colab:
+      return results.images
+      
+    for i in range(len(results.images)):
+      if results.nsfw_content_detected[i]:
+        results.images[i] = Image.open("nsfw.png")
+    return results.images
+
+# css = """.finetuned-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.finetuned-diffusion-div div h1{font-weight:900;margin-bottom:7px}.finetuned-diffusion-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="style.css") as demo:
+    gr.HTML(
+        f"""
+            <div class="Finetuned-Diffusion-Max-div">
+              <div>
+                <h1>Finetuned Diffusion Max</h1>
+              </div>
+              <p>
+               Demo for multiple fine-tuned Stable Diffusion models, trained on different styles: <br>
+               <a href="https://huggingface.co/nitrosocke/Arcane-Diffusion">Arcane</a>, <a href="https://huggingface.co/nitrosocke/archer-diffusion">Archer</a>, <a href="https://huggingface.co/nitrosocke/elden-ring-diffusion">Elden Ring</a>, <a href="https://huggingface.co/nitrosocke/spider-verse-diffusion">Spider-Verse</a>, <a href="https://huggingface.co/nitrosocke/mo-di-diffusion">Modern Disney</a>, <a href="https://huggingface.co/nitrosocke/classic-anim-diffusion">Classic Disney</a>, <a href="https://huggingface.co/dallinmackay/Van-Gogh-diffusion">Loving Vincent (Van Gogh)</a>, <a href="https://huggingface.co/nitrosocke/redshift-diffusion">Redshift renderer (Cinema4D)</a>, <a href="https://huggingface.co/prompthero/midjourney-v4-diffusion">Midjourney v4 style</a>, <a href="https://huggingface.co/hakurei/waifu-diffusion">Waifu</a>, <a href="https://huggingface.co/lambdalabs/sd-pokemon-diffusers">Pokémon</a>, <a href="https://huggingface.co/AstraliteHeart/pony-diffusion">Pony Diffusion</a>, <a href="https://huggingface.co/nousr/robo-diffusion">Robo Diffusion</a>, <a href="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion">Cyberpunk Anime</a>, <a href="https://huggingface.co/dallinmackay/Tron-Legacy-diffusion">Tron Legacy</a>, <a href="https://huggingface.co/Fictiverse/Stable_Diffusion_BalloonArt_Model">Balloon Art</a> + in colab notebook you can load any other Diffusers 🧨 SD model hosted on HuggingFace 🤗.
+              </p>
+              <p>You can skip the queue and load custom models in the colab: <a href="https://colab.research.google.com/gist/shanks125/ea9bf3a133ce53f2c7c31884a1473d80/copy-of-fine-tuned-diffusion-gradio.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
+               Running on <b>{device}</b>{(" in a <b>Google Colab</b>." if is_colab else "")}
+              </p>
+              <p>You can also duplicate this space and upgrade to gpu by going to settings:<br>
+              <a style="display:inline-block" href="https://huggingface.co/spaces/SUPERSHANKY/Finetuned_Diffusion_Max/?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></p>
+            </div>
+        """
+    )
+    with gr.Row():
+        
+        with gr.Column(scale=55):
+          with gr.Group():
+              model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
+              with gr.Box(visible=False) as custom_model_group:
+                custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", interactive=True)
+                gr.HTML("<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>")
+              
+              with gr.Row():
+                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False)
+                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
+
+
+              # image_out = gr.Image(height=512)
+              gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
+          
+          state_info = gr.Textbox(label="State", show_label=False, max_lines=2).style(container=False)
+          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")
+
+              n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=10, 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=250, step=1)
+
+              with gr.Row():
+                width = gr.Slider(label="Width", value=512, minimum=64, maximum=2048, step=8)
+                height = gr.Slider(label="Height", value=512, minimum=64, maximum=2048, 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)
+
+    if is_colab:
+        model_name.change(on_model_change, inputs=model_name, outputs=[custom_model_group, prompt], queue=False)
+        custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
+    # n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery)
+    steps.change(on_steps_change, inputs=[steps], outputs=[], queue=False)
+
+    inputs = [model_name, prompt, guidance, steps, n_images, 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)
+
+    ex = gr.Examples([
+        [models[7].name, "tiny cute and adorable kitten adventurer dressed in a warm overcoat with survival gear on a winters day", 7.5, 25],
+        [models[4].name, "portrait of dwayne johnson", 7.0, 35],
+        [models[5].name, "portrait of a beautiful alyx vance half life", 10, 25],
+        [models[6].name, "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7.0, 30],
+        [models[5].name, "fantasy portrait painting, digital art", 4.0, 20],
+    ], inputs=[model_name, prompt, guidance, steps], outputs=outputs, fn=inference, cache_examples=False)
+
+    gr.HTML("""
+    <div style="border-top: 1px solid #303030;">
+      <br>
+      <p>Models by <a href="https://huggingface.co/nitrosocke">@nitrosocke</a>, <a href="https://twitter.com/haruu1367">@haruu1367</a>, <a href="https://twitter.com/DGSpitzer">@Helixngc7293</a>, <a href="https://twitter.com/dal_mack">@dal_mack</a>, <a href="https://twitter.com/prompthero">@prompthero</a> and others. ❤️</p>
+      <p>This space uses the <a href="https://github.com/LuChengTHU/dpm-solver">DPM-Solver++</a> sampler by <a href="https://arxiv.org/abs/2206.00927">Cheng Lu, et al.</a>.</p>
+      <p>Space by:<br>
+      <a href="https://twitter.com/hahahahohohe"><img src="https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social" alt="Twitter Follow"></a><br>
+      <a href="https://github.com/qunash"><img alt="GitHub followers" src="https://img.shields.io/github/followers/qunash?style=social" alt="Github Follow"></a></p><br><br>
+      <a href="https://www.buymeacoffee.com/anzorq" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 45px !important;width: 162px !important;" ></a><br><br>
+      <p><img src="https://visitor-badge.glitch.me/badge?page_id=anzorq.finetuned_diffusion" alt="visitors"></p>
+    </div>
+    """)
+
+    demo.load(update_state_info, inputs=state_info, outputs=state_info, every=0.5, show_progress=False)
+
+print(f"Space built in {time.time() - start_time:.2f} seconds")
+
+# if not is_colab:
+demo.queue(concurrency_count=1)
+demo.launch(debug=is_colab, share=is_colab)

requirements.txt

@@ -0,0 +1,17 @@
+--extra-index-url https://download.pytorch.org/whl/cu117
+torch
+https://download.pytorch.org/whl/cu117/torch-1.13.1%2Bcu117-cp38-cp38-linux_x86_64.whl
+torchvision==0.14.1+cu117
+#diffusers
+git+https://github.com/huggingface/diffusers.git
+#transformers
+git+https://github.com/huggingface/transformers
+scipy
+ftfy
+psutil
+accelerate==0.16.0
+OmegaConf
+pytorch_lightning
+triton==2.0.0.dev20230208
+#https://github.com/apolinario/xformers/releases/download/0.0.3/xformers-0.0.14.dev0-cp38-cp38-linux_x86_64.whl
+https://github.com/ZyCromerZ/xformers_builds/releases/download/xformers-2023-02-12-Py-3.8-Cuda-11.8.0-PyTorch-1.13.1%2Bcu117-ubuntu-18.04/xformers-0.0.17+12c076d.d20230212-cp38-cp38-linux_x86_64.whl

style.css

@@ -0,0 +1,24 @@
+.finetuned-diffusion-div div{
+    display:inline-flex;
+    align-items:center;
+    gap:.8rem;
+    font-size:1.75rem
+}
+.finetuned-diffusion-div div h1{
+    font-weight:900;
+    margin-bottom:7px
+}
+.finetuned-diffusion-div p{
+    margin-bottom:10px;
+    font-size:94%
+}
+a{
+    text-decoration:underline
+}
+.tabs{
+    margin-top:0;
+    margin-bottom:0
+}
+#gallery{
+    min-height:20rem
+}

utils.py

@@ -0,0 +1,6 @@
+def is_google_colab():
+    try:
+        import google.colab
+        return True
+    except:
+        return False