Duplicate from huggingface-projects/stable-diffusion-latent-upscaler

Co-authored-by: Patrick von Platen <[email protected]>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+title: Stable Diffusion Latent Upscaler
+emoji: ➕
+colorFrom: purple
+colorTo: red
+sdk: gradio
+sdk_version: 3.15.0
+app_file: app.py
+pinned: true
+license: mit
+duplicated_from: huggingface-projects/stable-diffusion-latent-upscaler
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from diffusers import (
+    StableDiffusionPipeline,
+    DPMSolverMultistepScheduler,
+    DiffusionPipeline,
+)
+import gradio as gr
+import torch
+from PIL import Image
+import time
+import psutil
+import random
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+
+
+start_time = time.time()
+current_steps = 25
+
+SAFETY_CHECKER = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker", torch_dtype=torch.float16)
+
+UPSCALER = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16)
+UPSCALER.to("cuda")
+UPSCALER.enable_xformers_memory_efficient_attention()
+
+
+class Model:
+    def __init__(self, name, path=""):
+        self.name = name
+        self.path = path
+
+        if path != "":
+            self.pipe_t2i = StableDiffusionPipeline.from_pretrained(
+                path, torch_dtype=torch.float16, safety_checker=SAFETY_CHECKER
+            )
+            self.pipe_t2i.scheduler = DPMSolverMultistepScheduler.from_config(
+                self.pipe_t2i.scheduler.config
+            )
+        else:
+            self.pipe_t2i = None
+
+
+models = [
+    #Model("Stable Diffusion v1-4", "CompVis/stable-diffusion-v1-4"),
+    # Model("Stable Diffusion v1-5", "runwayml/stable-diffusion-v1-5"),
+    Model("anything-v4.0", "xyn-ai/anything-v4.0"),
+]
+
+MODELS = {m.name: m for m in models}
+
+device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
+
+
+def error_str(error, title="Error"):
+    return (
+        f"""#### {title}
+            {error}"""
+        if error
+        else ""
+    )
+
+
+def inference(
+    prompt,
+    neg_prompt,
+    guidance,
+    steps,
+    seed,
+    model_name,
+):
+
+    print(psutil.virtual_memory())  # print memory usage
+
+    if seed == 0:
+        seed = random.randint(0, 2147483647)
+
+    generator = torch.Generator("cuda").manual_seed(seed)
+
+    try:
+        low_res_image, up_res_image = txt_to_img(
+            model_name,
+            prompt,
+            neg_prompt,
+            guidance,
+            steps,
+            generator,
+        )
+        return low_res_image, up_res_image, f"Done. Seed: {seed}",
+    except Exception as e:
+        return None, None, error_str(e)
+
+
+def txt_to_img(
+    model_name,
+    prompt,
+    neg_prompt,
+    guidance,
+    steps,
+    generator,
+):
+    pipe = MODELS[model_name].pipe_t2i
+
+    if torch.cuda.is_available():
+        pipe = pipe.to("cuda")
+        pipe.enable_xformers_memory_efficient_attention()
+
+    low_res_latents = pipe(
+        prompt,
+        negative_prompt=neg_prompt,
+        num_inference_steps=int(steps),
+        guidance_scale=guidance,
+        generator=generator,
+        output_type="latent",
+    ).images
+
+    with torch.no_grad():
+        low_res_image = pipe.decode_latents(low_res_latents)
+        low_res_image = pipe.numpy_to_pil(low_res_image)
+
+    up_res_image = UPSCALER(
+        prompt=prompt,
+        negative_prompt=neg_prompt,
+        image=low_res_latents,
+        num_inference_steps=20,
+        guidance_scale=0,
+        generator=generator,
+    ).images
+
+    pipe.to("cpu")
+    torch.cuda.empty_cache()
+
+    return low_res_image[0], up_res_image[0]
+
+
+def replace_nsfw_images(results):
+    for i in range(len(results.images)):
+        if results.nsfw_content_detected[i]:
+            results.images[i] = Image.open("nsfw.png")
+    return results.images
+
+
+with gr.Blocks(css="style.css") as demo:
+    gr.HTML(
+        f"""
+            <div class="finetuned-diffusion-div">
+              <div style="text-align: center">
+                <h1>Anything v4 model + <a href="https://huggingface.co/stabilityai/sd-x2-latent-upscaler">Stable Diffusion Latent Upscaler</a></h1>
+                <p>
+                   Demo for the <a href="https://huggingface.co/andite/anything-v4.0">Anything v4</a> model hooked with the ultra-fast <a href="https://huggingface.co/stabilityai/sd-x2-latent-upscaler">Latent Upscaler</a>
+                  </p>
+              </div>
+              <!-- 
+              <p>To skip the queue, you can duplicate this Space<br>
+              <a style="display:inline-block" href="https://huggingface.co/spaces/patrickvonplaten/finetuned_diffusion?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.Column(scale=100):
+        with gr.Group(visible=False):
+                model_name = gr.Dropdown(
+                    label="Model",
+                    choices=[m.name for m in models],
+                    value=models[0].name,
+                    visible=False
+                )
+
+        with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
+                    with gr.Column():
+                        prompt = gr.Textbox(
+                            label="Enter your prompt",
+                            show_label=False,
+                            max_lines=1,
+                            placeholder="Enter your prompt",
+                            elem_id="prompt-text-input",
+                        ).style(
+                            border=(True, False, True, True),
+                            rounded=(True, False, False, True),
+                            container=False,
+                        )
+                        neg_prompt = gr.Textbox(
+                            label="Enter your negative prompt",
+                            show_label=False,
+                            max_lines=1,
+                            placeholder="Enter a negative prompt",
+                            elem_id="negative-prompt-text-input",
+                        ).style(
+                            border=(True, False, True, True),
+                            rounded=(True, False, False, True),
+                            container=False,
+                        )
+                    generate = gr.Button("Generate image").style(
+                        margin=False,
+                        rounded=(False, True, True, False),
+                        full_width=False,
+                    )
+            
+        with gr.Accordion("Advanced Options", open=False):
+                with gr.Group():
+                    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=75,
+                            step=1,
+                        )
+
+                    seed = gr.Slider(
+                        0, 2147483647, label="Seed (0 = random)", value=0, step=1
+                    )
+            
+
+    with gr.Column(scale=100):
+        with gr.Row():
+            with gr.Column(scale=75):
+                up_res_image = gr.Image(label="Upscaled 1024px Image", shape=(1024, 1024))
+            with gr.Column(scale=25):
+                low_res_image = gr.Image(label="Original 512px Image", shape=(512, 512))
+        error_output = gr.Markdown()
+
+    inputs = [
+        prompt,
+        neg_prompt,
+        guidance,
+        steps,
+        seed,
+        model_name,
+    ]
+    outputs = [low_res_image, up_res_image, error_output]
+    prompt.submit(inference, inputs=inputs, outputs=outputs)
+    generate.click(inference, inputs=inputs, outputs=outputs)
+
+    ex = gr.Examples(
+        [
+            ["a mecha robot in a favela", "low quality", 7.5, 25, 33, models[0].name],
+            ["the spirit of a tamagotchi wandering in the city of Paris", "low quality, bad render", 7.5, 50, 85, models[0].name],
+        ],
+        inputs=[prompt, neg_prompt, guidance, steps, seed, model_name],
+        outputs=outputs,
+        fn=inference,
+        cache_examples=True,
+    )
+    ex.dataset.headers = [""]
+    
+    gr.HTML(
+        """
+    <div style="border-top: 1px solid #303030;">
+      <br>
+      <p>Space by 🤗 Hugging Face, models by Stability AI, andite, linaqruf 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>This is a Demo Space For:<br>
+      <a href="https://huggingface.co/stabilityai/sd-x2-latent-upscaler">Stability AI's Latent Upscaler</a>
+    </div>
+    """
+    )
+
+print(f"Space built in {time.time() - start_time:.2f} seconds")
+
+demo.queue(concurrency_count=1)
+demo.launch()

requirements.txt

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

style.css

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+.container {
+    max-width: 960px
+}
+
+.finetuned-diffusion-div div {
+    align-items: center;
+    gap: .8rem;
+    font-size: 1.75rem
+}
+
+.finetuned-diffusion-div div h1 {
+    font-weight: 900;
+    margin-bottom: 7px
+}
+
+.finetuned-diffusion-div div p {
+    font-size: 50%
+}
+
+.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

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+def is_google_colab():
+    try:
+        import google.colab
+        return True
+    except:
+        return False