Upload app.py

app.py

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+import gradio as gr
+import numpy as np
+import random
+import torch
+import torch
+from micro_diffusion.models.model import create_latent_diffusion
+from huggingface_hub import hf_hub_download
+from safetensors import safe_open
+from PIL import Image
+
+# Init model
+params = {
+    'latent_res': 64,
+    'in_channels': 4,
+    'pos_interp_scale': 2.0,
+}
+model = create_latent_diffusion(**params).to('cuda')
+
+# Download weights from HF
+model_dict_path = hf_hub_download(repo_id="giannisdaras/ambient-o", filename="model.safetensors")
+model_dict = {}
+with safe_open(model_dict_path, framework="pt", device="cpu") as f:
+   for key in f.keys():
+       model_dict[key] = f.get_tensor(key)
+
+# Convert parameters to float32 + load
+float_model_params = {
+    k: v.to(torch.float32) for k, v in model_dict.items()
+}
+model.dit.load_state_dict(float_model_params)
+model = model.eval()
+
+
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+torch.cuda.empty_cache()
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
+
+
+def infer(prompt, seed=42, randomize_seed=False, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    images = model.generate(prompt=[prompt], num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, seed=seed)
+    image = images[0]
+    image = image.detach().cpu()
+    image = image.permute(1, 2, 0)  # [H, W, C]
+    image = (image * 255).clamp(0, 255).to(torch.uint8).numpy()
+    image = Image.fromarray(image)
+    return image, seed
+
+    
+examples = [
+    "a tiny astronaut hatching from an egg on the moon",
+    "a cat holding a sign that says hello world",
+    "an anime illustration of a wiener schnitzel",
+]
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""# Ambient-o text2image model
+[[paper](https://arxiv.org/abs/2506.10038)] 
+[[blog](https://giannisdaras.github.io/publication/ambient_omni)] [[model](https://huggingface.co/giannisdaras/ambient-o)] [[license](https://github.com/giannisdaras/ambient-omni/blob/main/text-to-image/LICENSE)] 
+        """)
+        
+        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 Settings", open=False):
+            
+            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():
+
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=1,
+                    maximum=15,
+                    step=0.1,
+                    value=5.0,
+                )
+  
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=28,
+                )
+        
+        gr.Examples(
+            examples = examples,
+            fn = infer,
+            inputs = [prompt],
+            outputs = [result, seed],
+            cache_examples="lazy"
+        )
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn = infer,
+        inputs = [prompt, seed, randomize_seed, guidance_scale, num_inference_steps],
+        outputs = [result, seed]
+    )
+
+demo.launch()