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app.py

@@ -1,154 +1,193 @@
+import os
+import torch
 import gradio as gr
-import numpy as np
+import spaces
 import random
+import numpy as np
+from safetensors.torch import load_file
+from huggingface_hub import hf_hub_download
 
-import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+from diffusers.utils import logging
+from PIL import Image
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+from ovis_image.model.tokenizer import build_ovis_tokenizer
+from ovis_image.model.autoencoder import load_ae
+from ovis_image.model.hf_embedder import OvisEmbedder
+from ovis_image.model.model import OvisImageModel
+from ovis_image.sampling import generate_image
+from ovis_image import ovis_image_configs
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+logging.set_verbosity_error()
 
+# DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
 
+device = "cuda"
+_dtype = torch.bfloat16
+hf_token = os.getenv("HF_TOKEN")
+
+# init ovis_image
+model_config = ovis_image_configs["ovis-image-7b"]
+ovis_image = OvisImageModel(model_config)
+ovis_image_path = hf_hub_download(
+    repo_id="AIDC-AI/Ovis-Image-7B", 
+    filename="ovis_image.safetensors", 
+    token=hf_token,
+)
+model_state_dict = load_file(ovis_image_path)
+missing_keys, unexpected_keys = ovis_image.load_state_dict(model_state_dict)
+print(f"Load Missing Keys {missing_keys}")
+print(f"Load Unexpected Keys {unexpected_keys}")
+ovis_image = ovis_image.to(device=device, dtype=_dtype)
+ovis_image.eval()
+
+# init vae
+vae_path = hf_hub_download(
+    repo_id="AIDC-AI/Ovis-Image-7B", 
+    filename="ae.safetensors", 
+    token=hf_token,
+)
+autoencoder = load_ae(
+    vae_path,
+    model_config.autoencoder_params,
+    device=device,
+    dtype=_dtype,
+    random_init=False,
+)
+autoencoder.eval()
+
+# init ovis
+ovis_path = hf_hub_download(
+    repo_id="AIDC-AI/Ovis-Image-7B", 
+    filename="Ovis2.5-2B", 
+    token=hf_token,
+)
+ovis_tokenizer = build_ovis_tokenizer(ovis_path)
+ovis_encoder = OvisEmbedder(
+    model_path=ovis_path,
+    random_init=False,
+    low_cpu_mem_usage=True,
+    torch_dtype=torch.bfloat16,
+).to(device=device, dtype=_dtype)
+
+
+@spaces.GPU(duration=75)
+def generate(prompt, img_height=1024, img_width=1024, seed=42, steps=50, guidance_scale=5.0):
+    print(f'inference with prompt : {prompt}, size: {img_height}x{img_width}, seed : {seed}, step : {steps}, cfg : {guidance_scale}')
+    image = generate_image(
+        device=device,
+        dtype=_dtype,
+        model=ovis_image,
+        prompt=prompt,
+        autoencoder=autoencoder,
+        ovis_tokenizer=ovis_tokenizer,
+        ovis_encoder=ovis_encoder,
+        img_height=img_height,
+        img_width=img_width,
+        denoising_steps=steps,
+        cfg_scale=guidance_scale,
+        seed=seed,
+    )
+    # bring into PIL format and save
+    image = image.clamp(-1, 1)
+    image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+    image = (image * 255).round().astype("uint8")
+    
+    return image[0]
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    "Solar punk vehicle in a bustling city",
+    "An anthropomorphic cat riding a Harley Davidson in Arizona with sunglasses and a leather jacket",
+    "An elderly woman poses for a high fashion photoshoot in colorful, patterned clothes with a cyberpunk 2077 vibe",
 ]
 
-css = """
+css="""
 #col-container {
     margin: 0 auto;
-    max-width: 640px;
+    max-width: 520px;
 }
 """
 
-with gr.Blocks() as demo:
+with gr.Blocks(css=css) as demo:
+    
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
+        gr.Markdown(f"""# Ovis-Image
+[[code](https://github.com/AIDC-AI/Ovis-Image)] [[model](https://huggingface.co/AIDC-AI/Ovis-Image-7B)]
+        """)
+        
         with gr.Row():
+            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
                 max_lines=1,
-                placeholder="Enter your prompt",
+                placeholder="Enter your prompt here",
                 container=False,
             )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
+            
+            run_button = gr.Button("Run", scale=0)
+        
         result = gr.Image(label="Result", show_label=False)
-
+        
         with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=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():
-                width = gr.Slider(
-                    label="Width",
+
+                img_height = gr.Slider(
+                    label="Image Height",
                     minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
+                    maximum=2048,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,
                 )
-
-                height = gr.Slider(
-                    label="Height",
+  
+                img_width = gr.Slider(
+                    label="Image Width",
                     minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
+                    maximum=2048,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,
                 )
-
+                        
             with gr.Row():
+
                 guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
+                    label="Guidance Scale",
+                    minimum=1,
+                    maximum=14,
                     step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                    value=5.0,
                 )
-
+  
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=50,
+                    maximum=100,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=50,
                 )
 
-        gr.Examples(examples=examples, inputs=[prompt])
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=42,
+            )
+        
+        gr.Examples(
+            examples = examples,
+            fn = generate,
+            inputs = [prompt],
+            outputs = [result],
+            cache_examples="lazy"
+        )
+
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
+        fn = generate,
+        inputs = [prompt, img_height, img_width, seed, num_inference_steps, guidance_scale],
+        outputs = [result]
     )
 
-if __name__ == "__main__":
-    demo.launch()
+if __name__ == '__main__':
+    demo.launch()

requirements.txt

@@ -1,6 +1,4 @@
-accelerate
-diffusers
-invisible_watermark
-torch
-transformers
-xformers
+torch==2.6.0
+transformers >= 4.53.0
+einops
+safetensors

setup.py

@@ -0,0 +1,9 @@
+from setuptools import setup, find_packages
+import os
+
+setup(
+    name='ovis_image',
+    version='1.0.0',
+    packages=find_packages(include=['ovis_image']),
+    # include any other necessary details here
+)