add

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

app.py

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+import gradio as gr
+import torch
+import pickle
+from torchvision.utils import save_image
+import numpy as np
+from diffusers import StableDiffusionUpscalePipeline
+from huggingface_hub import hf_hub_download
+import torch
+
+# Load the model from Hugging Face Hub
+model_path = hf_hub_download(repo_id="Shaoan/ConceptGAN", filename="augceleba_6451.pkl")
+
+with open(model_path, 'rb') as f:
+    G = pickle.load(f)['G_ema'].cpu().float()  # torch.nn.Module
+
+
+cchoices = ['Bald',
+            'Black Hair',
+            'Blond Hair',
+            'Smiling',
+            'NoSmile',
+            'Male',
+            'Female'
+            ]
+
+model_choices = [
+    'Change Dim = 8',
+    'Change Dim = 15',
+    'Change Dim = 30',
+    'Change Dim = 60'
+]
+
+
+cchoices = [
+    'Big Nose',
+    'Black Hair',
+    'Blond Hair',
+    'Chubby',
+    'Eyeglasses',
+    'Male',
+    'Pale Skin',
+    'Smiling',
+    'Straight Hair',
+    'Wavy Hair',
+    'Wearing Hat',
+    'Young'
+]
+
+
+import requests
+from PIL import Image
+from io import BytesIO
+from diffusers import LDMSuperResolutionPipeline
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_id = "CompVis/ldm-super-resolution-4x-openimages"
+
+# load model and scheduler
+pipeline = LDMSuperResolutionPipeline.from_pretrained(model_id)
+pipeline = pipeline.to(device)
+model_id = "stabilityai/stable-diffusion-x4-upscaler"
+text_pipeline = StableDiffusionUpscalePipeline.from_pretrained(
+    model_id, variant="fp32", torch_dtype=torch.float32
+)
+# let's download an  image
+
+
+def super_res(low_res_img, num_steps):
+    # run pipeline in inference (sample random noise and denoise)
+    upscaled_image = pipeline(low_res_img, num_inference_steps=num_steps, eta=1).images[0]
+    #upscaled_image = text_pipeline(prompt="a sharp image of human face", image=low_res_img, num_inference_steps=75).images[0]
+    return upscaled_image
+
+
+@torch.no_grad()
+def generate(seed, upscale, upscale_steps,*checkboxes):
+    z = torch.randn([1, G.z_dim], generator=torch.Generator().manual_seed(seed))
+    #m = torch.tensor([[1, 0, 0, 0, 1, 1, 0.]]).repeat(1, 1)
+    checkboxes_vector = torch.zeros([20])
+    for i in range(len(checkboxes)):
+        if i == 1:
+            checkboxes_vector[cchoices.index('Black Hair')] = checkboxes[i]
+        elif i == 2:
+            checkboxes_vector[cchoices.index('Blond Hair')] = checkboxes[i]
+        elif i == 3:
+            checkboxes_vector[cchoices.index('Straight Hair')] = checkboxes[i]
+        elif i == 4:
+            checkboxes_vector[cchoices.index('Wavy Hair')] = checkboxes[i]
+        elif i == 5:
+            checkboxes_vector[cchoices.index('Young')] = checkboxes[i] * 2
+        elif i == 6:
+            checkboxes_vector[cchoices.index('Male')] = checkboxes[i]
+        elif i == 9:
+            checkboxes_vector[cchoices.index('Big Nose')] = checkboxes[i]
+        elif i == 10:
+            checkboxes_vector[cchoices.index('Chubby')] = checkboxes[i]
+        elif i == 11:
+            checkboxes_vector[cchoices.index('Eyeglasses')] = checkboxes[i] * 2
+        elif i == 12:
+            checkboxes_vector[cchoices.index('Pale Skin')] = checkboxes[i]
+        elif i == 13:
+            checkboxes_vector[cchoices.index('Smiling')] = checkboxes[i]
+        elif i == 14:
+            checkboxes_vector[cchoices.index('Wearing Hat')] = checkboxes[i] * 2
+
+
+    is_young = checkboxes[5]
+    is_male = checkboxes[6]
+    is_bald = checkboxes[0]
+    is_goatee = checkboxes[7]
+    is_mustache = checkboxes[8]
+
+    checkboxes_vector[12] = is_mustache * 2
+    checkboxes_vector[13] = is_mustache * 2
+    checkboxes_vector[14] = is_goatee *2
+    checkboxes_vector[15] = is_goatee*2
+
+    checkboxes_vector[16] = is_bald
+    checkboxes_vector[17] = is_bald
+    checkboxes_vector[18] = is_bald
+    checkboxes_vector[19] = is_bald
+
+
+
+    print(checkboxes_vector)
+
+    m = checkboxes_vector.view(1, 20)
+    ws = G.mapping(z, m, truncation_psi=0.5)
+    img = (G.synthesis(ws, force_fp32=True).clip(-1,1)+1)/2
+    if upscale:
+        up_img = np.array(super_res(img*2-1, upscale_steps))
+        return up_img
+    else:
+        return img[0].permute(1, 2, 0).numpy()
+
+
+# Create the interface using gr.Blocks
+with gr.Blocks() as demo:
+    with gr.Row():
+        sliders = [
+            gr.Slider(label='Bald', minimum=0, maximum=1, step=0.01),
+            gr.Slider(label='Black Hair', minimum=0, maximum=1, step=0.01),
+            gr.Slider(label='Blond Hair', minimum=0, maximum=1, step=0.01),
+            gr.Slider(label='Straight Hair', minimum=0, maximum=1, step=0.01),
+            gr.Slider(label='Wavy Hair', minimum=0, maximum=1, step=0.01),
+        ]
+
+    with gr.Row():
+        sliders += [gr.Slider(label='Young', minimum=0, maximum=1, step=0.01)]
+        sliders += [gr.Slider(label='Male', minimum=0, maximum=1, step=0.01)]
+
+    with gr.Row():
+        sliders += [gr.Slider(label='Goatee', minimum=0, maximum=1, step=0.01)]
+        sliders += [gr.Slider(label='Mustache', minimum=0, maximum=1, step=0.01)]
+
+    with gr.Row():
+        sliders += [
+            gr.Slider(label='Big Nose', minimum=0, maximum=1, step=0.01),
+            gr.Slider(label='Chubby', minimum=0, maximum=1, step=0.01),
+            gr.Slider(label='Eyeglasses', minimum=0, maximum=1, step=0.01),
+            gr.Slider(label='Pale Skin', minimum=0, maximum=1, step=0.01),
+            gr.Slider(label='Smiling', minimum=0, maximum=1, step=0.01),
+            gr.Slider(label='Wearing Hat', minimum=0, maximum=1, step=0.01),
+        ]
+
+    seed_input = gr.Number(label="Seed", value=6)
+    upscale_funcs = []
+    with gr.Row():
+        upscale_funcs = [gr.Checkbox(label="Upscale 4x")]
+        upscale_funcs += [gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=10)]
+    generate_button = gr.Button("Generate")
+
+    output_image = gr.Image(label="Generated Image")
+
+    # Set the action for the button
+    generate_button.click(fn=generate, inputs=[seed_input] + upscale_funcs +sliders, outputs=output_image)
+
+# Launch the demo
+demo.launch()