Duplicate from anzorq/sd-space-creator

Co-authored-by: AQ <[email protected]>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.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
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: SD Space Creator
+emoji: 🌌🔨
+colorFrom: red
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.10.1
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: anzorq/sd-space-creator
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import subprocess
+from huggingface_hub import HfApi, upload_folder
+import gradio as gr
+import requests
+from huggingface_hub import whoami, list_models
+
+
+def error_str(error, title="Error"):
+    return f"""#### {title}
+            {error}"""  if error else ""
+
+def url_to_model_id(model_id_str):
+    return model_id_str.split("/")[-2] + "/" + model_id_str.split("/")[-1] if model_id_str.startswith("https://huggingface.co/") else model_id_str
+
+def has_diffusion_model(model_id, token):
+    api = HfApi(token=token)
+    return any([f.endswith("diffusion_pytorch_model.bin") for f in api.list_repo_files(repo_id=model_id)])
+
+def get_my_model_names(token):
+    
+    try:
+        author = whoami(token=token)
+        model_infos = list_models(author=author["name"], use_auth_token=token)
+
+        
+        model_names = []
+        for model_info in model_infos:
+            model_id = model_info.modelId
+            if has_diffusion_model(model_id, token):
+                model_names.append(model_id)
+        
+        # if not model_names:
+        #     return [], Exception("No diffusion models found in your account.")
+
+        return model_names, None
+        
+    except Exception as e:
+        return [], e
+
+def on_token_change(token):
+    
+    if token:
+        model_names, error = get_my_model_names(token)
+        return gr.update(visible=not error), gr.update(choices=model_names, label="Select a model:"), error_str(error)
+    else:
+        return gr.update(visible=False), gr.update(choices=[], label="Select a model:"), None
+
+def on_load_model(user_model_id, other_model_id, token):
+
+    if not user_model_id and not other_model_id:
+        return None, None, None, None, gr.update(value=error_str("Please enter a model ID."))
+
+    try:
+        model_id = url_to_model_id(other_model_id) if other_model_id else user_model_id
+        original_model_id = model_id
+
+        if not has_diffusion_model(model_id, token):
+            return None, None, None, None, gr.update(value=error_str("There are no diffusion weights in the model you selected."))
+
+        user = whoami(token=token)
+        model_id = user["name"] + "/" + model_id.split("/")[-1]
+        title = " ".join([w.capitalize() for w in model_id.split("/")[-1].replace("-", " ").replace("_", " ").split(" ")])
+        
+        description = f"""Demo for <a href="https://huggingface.co/{original_model_id}">{title}</a> Stable Diffusion model."""
+
+        return gr.update(visible=True), gr.update(value=model_id), gr.update(value=title), gr.update(value=description), None
+    
+    except Exception as e:
+        return None, None, None, None, gr.update(value=error_str(e))
+
+def create_and_push(space_type, hardware, private_space, other_model_name, radio_model_names, model_id, title, description, prefix, update, token):
+
+    try:
+
+        # 1. Create the new space
+        api = HfApi(token=token)
+        repo_url = api.create_repo(
+            repo_id=model_id,
+            exist_ok=update,
+            repo_type="space",
+            space_sdk="gradio",
+            private=private_space
+        )
+        api_url = f'https://huggingface.co/api/spaces/{model_id}'
+        headers = { "Authorization" : f"Bearer {token}"}
+        # add HUGGING_FACE_HUB_TOKEN secret to new space
+        requests.post(f'{api_url}/secrets', json={"key":"HUGGING_FACE_HUB_TOKEN","value":token}, headers=headers)
+        # set new Space Hardware flavor
+        requests.post(f'{api_url}/hardware', json={'flavor': hardware}, headers=headers)
+
+        # 2. Replace the name, title, and description in the template
+        with open("template/app_simple.py" if space_type == "Simple" else "template/app_advanced.py", "r") as f:
+            app = f.read()
+        app = app.replace("$model_id", url_to_model_id(other_model_name) if other_model_name else radio_model_names)
+        app = app.replace("$title", title)
+        app = app.replace("$description", description)
+        app = app.replace("$prefix", prefix)
+        app = app.replace("$space_id", whoami(token=token)["name"] + "/" + model_id.split("/")[-1])
+
+        # 3. save the new app.py file
+        with open("app.py", "w") as f:
+            f.write(app)
+
+        # 4. Upload the new app.py to the space
+        api.upload_file(
+            path_or_fileobj="app.py",
+            path_in_repo="app.py",
+            repo_id=model_id,
+            token=token,
+            repo_type="space",
+        )
+
+        # 5. Upload template/requirements.txt to the space
+        if space_type == "Advanced":
+            api.upload_file(
+                path_or_fileobj="template/requirements.txt",
+                path_in_repo="requirements.txt",
+                repo_id=model_id,
+                token=token,
+                repo_type="space",
+            )
+
+        # 5. Delete the app.py file
+        os.remove("app.py")
+
+        return f"""Successfully created space at: <a href="{repo_url}" target="_blank">{repo_url}</a>"""
+
+    except Exception as e:
+        return error_str(e)
+
+
+DESCRIPTION = """### Create a gradio space for your Diffusers🧨 model
+                With this space, you can easily create a gradio demo for your Diffusers model and share it with the community.<br>
+                1️⃣ Make sure you have created your hugging face account<br>
+                2️⃣ Generate a token here with write access<br>
+                3️⃣ Choose a stable diffusion base model, there are thousands of them here<br>
+                4️⃣ Choose Space type<br>
+                5️⃣ Choose the new Space Hardware<br>
+                It is done.
+            """
+
+with gr.Blocks() as demo:
+
+    gr.Markdown(DESCRIPTION)
+    with gr.Row():
+
+        with gr.Column(scale=11):
+            with gr.Column():
+                gr.Markdown("#### 1. Choose a model")
+                input_token = gr.Textbox(
+                    max_lines=1,
+                    type="password",
+                    label="Enter your Hugging Face token",
+                    placeholder="WRITE permission is required!",
+                )
+                gr.Markdown("You can get a token [here](https://huggingface.co/settings/tokens)")
+                with gr.Group(visible=False) as group_model:
+                    radio_model_names = gr.Radio(label="Your models:")
+                    other_model_name = gr.Textbox(label="Other model:", placeholder="URL or model id, e.g. username/model_name")
+                    btn_load = gr.Button(value="Load model")
+
+        with gr.Column(scale=10):
+            with gr.Column(visible=False) as group_create:
+                gr.Markdown("#### 2. Enter details and create the space")
+                name = gr.Textbox(label="Name", placeholder="e.g. diffusers-demo")
+                title = gr.Textbox(label="Title", placeholder="e.g. Diffusers Demo")
+                description = gr.Textbox(label="Description", placeholder="e.g. Demo for my awesome Diffusers model", lines=5)
+                prefix = gr.Textbox(label="Prefix tokens", placeholder="Tokens that are required to be present in the prompt, e.g. `rick and morty style`")
+                gr.Markdown("""#### Choose space type
+                - **Simple** - Runs on GPU using Hugging Face inference API, but you cannot control image generation parameters.
+                - **Advanced** - Runs on CPU by default, with the option to upgrade to GPU. You can control image generation parameters: guidance, number of steps, image size, etc. Also supports **image-to-image** generation.""")
+                space_type =gr.Radio(label="Space type", choices=["Simple", "Advanced"], value="Simple")
+                update = gr.Checkbox(label="Update the space if it already exists?")
+                private_space = gr.Checkbox(label="Private Space")
+                gr.Markdown("Choose the new Space Hardware <small>[check pricing page](https://huggingface.co/pricing#spaces), you need payment method to upgrade your Space hardware</small>")
+                hardware = gr.Dropdown(["cpu-basic","cpu-upgrade","t4-small","t4-medium","a10g-small","a10g-large"],value = "cpu-basic", label="Space Hardware")
+                brn_create = gr.Button("Create the space")
+
+    error_output = gr.Markdown(label="Output")
+
+
+    input_token.change(
+        fn=on_token_change,
+        inputs=input_token,
+        outputs=[group_model, radio_model_names, error_output],
+        queue=False,
+        scroll_to_output=True)
+
+    btn_load.click(
+        fn=on_load_model,
+        inputs=[radio_model_names, other_model_name, input_token],
+        outputs=[group_create, name, title, description, error_output],
+        queue=False,
+        scroll_to_output=True)
+
+    brn_create.click(
+        fn=create_and_push,
+        inputs=[space_type, hardware, private_space, other_model_name, radio_model_names, name, title, description, prefix, update, input_token],
+        outputs=[error_output],
+        scroll_to_output=True
+    )
+
+    # gr.Markdown("""<img src="https://raw.githubusercontent.com/huggingface/diffusers/main/docs/source/imgs/diffusers_library.jpg" width="150"/>""")
+    gr.HTML("""
+    <div style="border-top: 1px solid #303030;">
+      <br>
+      <p>Space by: <a href="https://twitter.com/hahahahohohe"><img src="https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social" alt="Twitter Follow"></a></p><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.sd-space-creator" alt="visitors"></p>
+    </div>
+    """)
+    
+demo.queue()
+demo.launch(debug=True)

template/app_advanced.py

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+from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
+import gradio as gr
+import torch
+from PIL import Image
+
+model_id = '$model_id'
+prefix = '$prefix'
+     
+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>$title</h1>
+              </div>
+              <p>
+               $description<br>
+               {"Add the following tokens to your prompts for the model to work properly: <b>prefix</b>" if prefix else ""}
+              </p>
+              Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU 🥶</b>. For faster inference it is recommended to <b>upgrade to GPU in <a href='https://huggingface.co/spaces/$space_id/settings'>Settings</a></b>"} after duplicating the space<br><br>
+              <a style="display:inline-block" href="https://huggingface.co/spaces/$space_id?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
+            </div>
+        """
+    )
+    with gr.Row():
+        
+        with gr.Column(scale=55):
+          with gr.Group():
+              with gr.Row():
+                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
+                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
+
+              image_out = gr.Image(height=512)
+          error_output = gr.Markdown()
+
+        with gr.Column(scale=45):
+          with gr.Tab("Options"):
+            with gr.Group():
+              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
+              auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically ($prefix)", 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()

template/app_simple.py

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+import os
+import gradio as gr
+
+API_KEY=os.environ.get('HUGGING_FACE_HUB_TOKEN', None)
+
+article = """---
+This space was created using [SD Space Creator](https://huggingface.co/spaces/anzorq/sd-space-creator)."""
+
+gr.Interface.load(
+    name="models/$model_id",
+    title="""$title""",
+    description="""$description""",
+    article=article,
+    api_key=API_KEY,
+    ).queue(concurrency_count=20).launch()

template/requirements.txt

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+--extra-index-url https://download.pytorch.org/whl/cu113
+torch
+diffusers
+#transformers
+git+https://github.com/huggingface/transformers
+accelerate
+ftfy