testing basics

.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

.idea/hot_dog_demo_test.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="Eslint" enabled="true" level="WARNING" enabled_by_default="true" />
+    <inspection_tool class="Stylelint" enabled="true" level="ERROR" enabled_by_default="true" />
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/hot_dog_demo_test.iml" filepath="$PROJECT_DIR$/.idea/hot_dog_demo_test.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

app.py

@@ -1,15 +1,81 @@
 import gradio as gr
-from transformers import pipeline
+import torch
+from diffusers import StableDiffusionPipeline
+from torchvision.transforms.functional import to_pil_image
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+# pipeline = StableDiffusionPipeline.from_pretrained(
+#     pretrained_model_name_or_path="weights", torch_dtype=torch.float16
+# )
 
-def predict(image):
-  predictions = pipeline(image)
-  return {p["label"]: p["score"] for p in predictions}
+# pipeline.to('cuda')
 
-gr.Interface(
-    predict,
-    inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),
-    outputs=gr.outputs.Label(num_top_classes=2),
-    title="Hot Dog? Or Not?",
-).launch()
+concept_to_dummy_tokens_map = torch.load("concept_to_dummy_tokens_map.pt")
+
+
+def replace_concept_tokens(text: str):
+    for concept_token, dummy_tokens in concept_to_dummy_tokens_map.items():
+        text = text.replace(concept_token, dummy_tokens)
+    return text
+
+
+# def inference(
+#     prompt: str, num_inference_steps: int = 50, guidance_scale: int = 3.0
+# ):
+#     prompt = replace_concept_tokens(prompt)
+#     for _ in range(3):
+#         img_list = pipeline(
+#             prompt=prompt,
+#             num_inference_steps=num_inference_steps,
+#             guidance_scale=guidance_scale,
+#         )
+#         if not img_list["nsfw_content_detected"]:
+#             break
+#     return img_list["sample"]
+
+DEFAULT_PROMPT = (
+    "A watercolor painting on textured paper of a <det-logo> using soft strokes,"
+    " pastel colors, incredible composition, masterpiece"
+)
+
+
+def white_imgs(prompt: str, guidance_scale: float, num_inference_steps: int, seed: int):
+    return [torch.ones(512, 512, 3).numpy() for _ in range(2)]
+
+
+with gr.Blocks() as demo:
+    prompt = gr.Textbox(
+        label="Prompt including the token '<det-logo>'",
+        placeholder=DEFAULT_PROMPT,
+        interactive=True,
+    )
+    guidance_scale = gr.Slider(
+        minimum=1.0, maximum=10.0, value=3.0, label="Guidance Scale", interactive=True
+    )
+    num_inference_steps = gr.Slider(
+        minimum=25,
+        maximum=60,
+        value=40,
+        label="Num Inference Steps",
+        interactive=True,
+        step=1,
+    )
+    seed = gr.Slider(
+        minimum=2147483147,
+        maximum=2147483647,
+        value=2147483397,
+        label="Seed",
+        interactive=True,
+    )
+    generate_btn = gr.Button(label="Generate")
+    gallery = gr.Gallery(
+        label="Generated Images",
+        value=[torch.zeros(512, 512, 3).numpy() for _ in range(2)],
+    ).style(height="auto")
+
+    generate_btn.click(
+        white_imgs,
+        inputs=[prompt, guidance_scale, num_inference_steps, seed],
+        outputs=gallery,
+    )
+
+demo.launch()