init

app.py

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+from contextlib import nullcontext
+import gradio as gr
+from torch import autocast
+from removebg import RemoveBg
+import os
+import torch
+
+import PIL
+from PIL import Image
+
+from diffusers import StableDiffusionPipeline
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+def image_grid(imgs, rows, cols):
+    assert len(imgs) == rows*cols
+
+    w, h = imgs[0].size
+    grid = Image.new('RGB', size=(cols*w, rows*h))
+    grid_w, grid_h = grid.size
+    
+    for i, img in enumerate(imgs):
+        grid.paste(img, box=(i%cols*w, i//cols*h))
+    return grid
+
+pretrained_model_name_or_path = "vmanot/valiant-effort-one" #@param {type:"string"}
+
+tokenizer = CLIPTokenizer.from_pretrained(
+    pretrained_model_name_or_path,
+    subfolder="tokenizer",
+)
+text_encoder = CLIPTextModel.from_pretrained(
+    pretrained_model_name_or_path, subfolder="text_encoder", torch_dtype=torch.float16
+)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+context = autocast if device == "cuda" else nullcontext
+dtype = torch.float16 if device == "cuda" else torch.float32
+
+pipe = StableDiffusionPipeline.from_pretrained(
+    pretrained_model_name_or_path,
+    revision="main",
+    torch_dtype=torch.float16,
+    text_encoder=text_encoder,
+    tokenizer=tokenizer,
+).to("cuda")
+
+disable_safety = True
+
+if disable_safety:
+  def null_safety(images, **kwargs):
+      return images, False
+  pipe.safety_checker = null_safety
+
+  
+num_samples = 2 #@param {type:"number"}
+num_rows = 2 #@param {type:"number"}
+
+def infer(prompt, n_samples, steps, scale):
+    i = 0
+    with context("cuda"):
+        images = pipe(n_samples*[prompt], guidance_scale=scale, num_inference_steps=steps).images
+        for im in images:
+          name = 'im' + str(i) + '.png'
+          i += 1
+          im.save(name)
+          rmbg = RemoveBg("K74BwTbRWVgpszyvJ8VvXPmv", "error.log")
+          # with open(im) as image_file:
+          #     encoded_string = base64.b64encode(image_file.read())
+          im = rmbg.remove_background_from_img_file(name)
+        # images[0].save('output.png')
+    return images
+
+css = """
+        a {
+            color: inherit;
+            text-decoration: underline;
+        }
+        .gradio-container {
+            font-family: 'IBM Plex Sans', sans-serif;
+        }
+        .gr-button {
+            color: white;
+            border-color: #9d66e5;
+            background: #9d66e5;
+        }
+        input[type='range'] {
+            accent-color: green;
+        }
+        .dark input[type='range'] {
+            accent-color: green;
+        }
+        .container {
+            max-width: 730px;
+            margin: auto;
+            padding-top: 1.5rem;
+        }
+        #gallery {
+            min-height: 22rem;
+            margin-bottom: 15px;
+            margin-left: auto;
+            margin-right: auto;
+            border-bottom-right-radius: .5rem !important;
+            border-bottom-left-radius: .5rem !important;
+        }
+        #gallery>div>.h-full {
+            min-height: 20rem;
+        }
+        .details:hover {
+            text-decoration: underline;
+        }
+        .gr-button {
+            white-space: nowrap;
+        }
+        .gr-button:focus {
+            border-color: rgb(147 197 253 / var(--tw-border-opacity));
+            outline: none;
+            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
+            --tw-border-opacity: 1;
+            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
+            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
+            --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
+            --tw-ring-opacity: .5;
+        }
+        #advanced-options {
+            margin-bottom: 20px;
+        }
+        .footer {
+            margin-bottom: 45px;
+            margin-top: 35px;
+            text-align: center;
+            border-bottom: 1px solid #e5e5e5;
+        }
+        .footer>p {
+            font-size: .8rem;
+            display: inline-block;
+            padding: 0 10px;
+            transform: translateY(10px);
+            background: white;
+        }
+        .dark .logo{ filter: invert(1); }
+        .dark .footer {
+            border-color: #303030;
+        }
+        .dark .footer>p {
+            background: #0b0f19;
+        }
+        .acknowledgments h4{
+            margin: 1.25em 0 .25em 0;
+            font-weight: bold;
+            font-size: 115%;
+        }
+"""
+
+block = gr.Blocks(css=css)
+
+examples = [
+    [
+        'Yoda',
+        2,
+        7.5,
+    ],
+    [
+        'Abraham Lincoln',
+        2,
+        7.5,
+    ],
+    [
+        'George Washington',
+        2,
+        7,
+    ],
+]
+
+with block:
+    gr.HTML(
+        """
+            <div style="text-align: center; max-width: 650px; margin: 0 auto;">
+              <div>
+                <img class="logo" src="https://lambdalabs.com/static/images/lambda-logo.svg" alt="Lambda Logo"
+                    style="margin: auto; max-width: 7rem;">
+                <h1 style="font-weight: 900; font-size: 3rem;">
+                  Icon Generator
+                </h1>
+              </div>
+            </div>
+        """
+    )
+    with gr.Group():
+        with gr.Box():
+            with gr.Row().style(mobile_collapse=False, equal_height=True):
+                text = gr.Textbox(
+                    label="Enter your prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                ).style(
+                    border=(True, False, True, True),
+                    rounded=(True, False, False, True),
+                    container=False,
+                )
+                btn = gr.Button("Generate image").style(
+                    margin=False,
+                    rounded=(False, True, True, False),
+                )
+
+        gallery = gr.Gallery(
+            label="Generated images", show_label=False, elem_id="gallery"
+        ).style(grid=[2], height="auto")
+
+
+        with gr.Row(elem_id="advanced-options"):
+            samples = gr.Slider(label="Images", minimum=1, maximum=4, value=2, step=1)
+            steps = gr.Slider(label="Steps", minimum=5, maximum=50, value=25, step=5)
+            scale = gr.Slider(
+                label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
+            )
+
+
+        ex = gr.Examples(examples=examples, fn=infer, inputs=[text, samples, scale], outputs=gallery, cache_examples=False)
+        ex.dataset.headers = [""]
+
+
+        text.submit(infer, inputs=[text, samples, steps, scale], outputs=gallery)
+        btn.click(infer, inputs=[text, samples, steps, scale], outputs=gallery)
+        gr.HTML(
+            """
+                <div class="footer">
+                    <p> Gradio Demo by 🤗 Hugging Face
+                    </p>
+                </div>
+           """
+        )
+
+block.launch(debug=True)

requirements.txt

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+diffusers==0.4.1 transformers==4.22.1 ftfy
+"ipywidgets>=7,<8"
+--extra-index-url https://download.pytorch.org/whl/cu113
+pytorch
+scipy datasets
+gradio
+removebg