all toghether

Dockerfile

@@ -5,4 +5,23 @@ RUN chown -R user:user /root/
 
 USER user
 ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:$PATH
+    PATH=/home/user/.local/bin:$PATH \
+    PYTHONPATH=$HOME/app \
+    PYTHONUNBUFFERED=1 \
+    GRADIO_ALLOW_FLAGGING=never \
+    GRADIO_NUM_PORTS=1 \
+    GRADIO_SERVER_NAME=0.0.0.0 \
+    GRADIO_THEME=huggingface \
+    SYSTEM=spaces 
+
+WORKDIR $HOME/app
+COPY ./requirements.txt /code/requirements.txt
+
+# create virtual env for Gradio app
+RUN python -m venv $HOME/.venv && \
+    . $HOME/.venv/bin/activate && \ 
+    pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r /code/requirements.txt 
+
+COPY --chown=user . $HOME/app
+CMD ["sh", "run.sh"]

app.py

@@ -0,0 +1,190 @@
+
+import gradio as gr
+from PIL import Image
+import requests
+import base64
+import numpy as np
+import random
+import io
+
+URL = "http://localhost:5000/predictions"
+HEADERS = {
+    "Content-Type": "application/json",
+}
+
+MAX_SEED = np.iinfo(np.int32).max
+
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    prior_num_inference_steps: int = 30,
+    # prior_timesteps: List[float] = None,
+    prior_guidance_scale: float = 4.0,
+    decoder_num_inference_steps: int = 12,
+    # decoder_timesteps: List[float] = None,
+    decoder_guidance_scale: float = 0.0,
+    num_images_per_prompt: int = 2,
+
+) -> Image:
+    payload = {
+        "input": {
+            "hdr": 0,
+            "image": "https://replicate.delivery/pbxt/KA9yP9n3ZX5A5mkoPz3gsPzKTH1NA7LqVkQRTg7Sov46lOfo/0_1.webp",
+            "steps": 20,
+            "prompt": "UHD 4k vogue, a woman resting in a magic pool, face above the surface of the water, red freckles",
+            "scheduler": "DDIM",
+            "creativity": 0.25,
+            "guess_mode": False,
+            "resolution": "original",
+            "resemblance": 0.75,
+            "guidance_scale": 7,
+            "negative_prompt": "teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant"
+        }
+    }
+    response = requests.post(URL, headers=HEADERS, json=payload)
+    json_response = response.json()
+    if 'output' in json_response:
+        base64_image = json_response["output"][0]
+        image_data = base64.b64decode(
+            base64_image.replace("data:image/png;base64,", ""))
+        image_stream = io.BytesIO(image_data)
+        return Image.open(image_stream)
+    raise gr.Error(json_response["status"])
+
+
+examples = [
+    "An astronaut riding a green horse",
+    "A mecha robot in a favela by Tarsila do Amaral",
+    "The sprirt of a Tamagotchi wandering in the city of Los Angeles",
+    "A delicious feijoada ramen dish"
+]
+
+with gr.Blocks() as demo:
+    with gr.Group():
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            run_button = gr.Button("Run", scale=0)
+        result = gr.Image(label="Result", show_label=False)
+    with gr.Accordion("Advanced options", open=False):
+        negative_prompt = gr.Text(
+            label="Negative prompt",
+            max_lines=1,
+            placeholder="Enter a Negative Prompt",
+        )
+
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=0,
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Row():
+            width = gr.Slider(
+                label="Width",
+                minimum=1024,
+                maximum=1024,
+                step=512,
+                value=1024,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=1024,
+                maximum=1024,
+                step=512,
+                value=1024,
+            )
+            num_images_per_prompt = gr.Slider(
+                label="Number of Images",
+                minimum=1,
+                maximum=2,
+                step=1,
+                value=1,
+            )
+        with gr.Row():
+            prior_guidance_scale = gr.Slider(
+                label="Prior Guidance Scale",
+                minimum=0,
+                maximum=20,
+                step=0.1,
+                value=4.0,
+            )
+            prior_num_inference_steps = gr.Slider(
+                label="Prior Inference Steps",
+                minimum=10,
+                maximum=30,
+                step=1,
+                value=20,
+            )
+
+            decoder_guidance_scale = gr.Slider(
+                label="Decoder Guidance Scale",
+                minimum=0,
+                maximum=0,
+                step=0.1,
+                value=0.0,
+            )
+            decoder_num_inference_steps = gr.Slider(
+                label="Decoder Inference Steps",
+                minimum=4,
+                maximum=12,
+                step=1,
+                value=10,
+            )
+
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=result,
+        fn=generate,
+        cache_examples=False,
+    )
+
+    inputs = [
+        prompt,
+        negative_prompt,
+        seed,
+        width,
+        height,
+        prior_num_inference_steps,
+        # prior_timesteps,
+        prior_guidance_scale,
+        decoder_num_inference_steps,
+        # decoder_timesteps,
+        decoder_guidance_scale,
+        num_images_per_prompt,
+    ]
+    gr.on(
+        triggers=[prompt.submit, negative_prompt.submit, run_button.click],
+        fn=randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=generate,
+        inputs=inputs,
+        outputs=result,
+        api_name="run",
+    )
+
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()

requirements.txt

@@ -0,0 +1,2 @@
+gradio==4.18.0
+replicate==0.23.1

run.sh

@@ -0,0 +1,2 @@
+cd /src && python3 -m cog.server.http --threads=10 &
+cd $HOME/app && . $HOME/.venv/bin/activate && python app.py