init ai model with generating imgs

Dockerfile

@@ -0,0 +1,11 @@
+FROM python:3.10
+
+WORKDIR /app
+
+RUN pip install --no-cache-dir torch torchvision torchaudio diffusers gradio
+RUN pip install --no-cache-dir black-forest-labs-flux
+COPY . /app
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

app.py

@@ -0,0 +1,29 @@
+import torch
+from diffusers import FluxPipeline
+import gradio as gr # type: ignore
+
+
+pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
+pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power
+
+prompt = "A cat holding a sign that says hello world"
+image = pipe(
+    prompt,
+    height=1024,
+    width=1024,
+    guidance_scale=3.5,
+    num_inference_steps=50,
+    max_sequence_length=512,
+    generator=torch.Generator("cpu").manual_seed(0)
+).images[0]
+image.save("flux-dev.png")
+
+gradio_app = gr.Interface(
+    image,
+    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
+    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
+    title="Hot Dog? Or Not?",
+)
+
+if __name__ == "__main__":
+    gradio_app.launch()