Add Replicate demo and API

README.md

@@ -7,8 +7,10 @@
 </div>
 
 <p align="center">
-Experience the CogView3-Plus-3B model online on <a href="https://huggingface.co/spaces/THUDM-HF-SPACE/CogView3-Plus-3B-Space" target="_blank"> 🤗 Huggingface Space</a>
-</p>
+Try out the CogView3-Plus-3B model online on <a href="https://huggingface.co/spaces/THUDM-HF-SPACE/CogView3-Plus-3B-Space" target="_blank"> 🤗 Huggingface Space</a> and 
+<a href="https://replicate.com/chenxwh/cogview3" target="_blank">
+    <img src="https://replicate.com/chenxwh/cogview3/badge" alt="Replicate">
+</p> 
 <p align="center">
 📚 Check out the <a href="https://arxiv.org/abs/2403.05121" target="_blank">paper</a>
 </p>
@@ -21,7 +23,7 @@ Experience the CogView3-Plus-3B model online on <a href="https://huggingface.co/
 
 ## Project Updates
 
-- 🔥🔥 ```2024/10/13```: We have adapted and open-sourced the **CogView-3Plus-3B** model in the [diffusers](https://github.com/huggingface/diffusers) version. You can [experience it online](https://huggingface.co/spaces/THUDM-HF-SPACE/CogView3-Plus-3B-Space).
+- 🔥🔥 ```2024/10/13```: We have adapted and open-sourced the **CogView-3Plus-3B** model in the [diffusers](https://github.com/huggingface/diffusers) version. You can try it out on [Spaces](https://huggingface.co/spaces/THUDM-HF-SPACE/CogView3-Plus-3B-Space) or [Replicate](https://replicate.com/chenxwh/cogview3/).
 - 🔥 ```2024/9/29```: We have open-sourced **CogView3** and **CogView-3Plus-3B**. **CogView3** is a text-to-image system based on cascaded diffusion, utilizing a relay diffusion framework. **CogView-3Plus** is a series of newly developed text-to-image models based on Diffusion Transformers.
 
 ## Model Introduction

cog.yaml

@@ -0,0 +1,28 @@
+# Configuration for Cog ⚙️
+# Reference: https://cog.run/yaml
+
+build:
+  # set to true if your model requires a GPU
+  gpu: true
+
+  # a list of ubuntu apt packages to install
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+
+  # python version in the form '3.11' or '3.11.4'
+  python_version: "3.11"
+
+  # a list of packages in the format <package-name>==<version>
+  python_packages:
+    - accelerate>=1.0.0
+    - sentencepiece>=0.2.0
+    - torch
+    - transformers>=4.45.2
+    - openai>=1.51.2
+  run:
+    - pip install git+https://github.com/huggingface/diffusers.git
+    - curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.8.2/pget_linux_x86_64" && chmod +x /usr/local/bin/pget
+
+# predict.py defines how predictions are run on your model
+predict: "predict.py:Predictor"

predict.py

@@ -0,0 +1,142 @@
+# Prediction interface for Cog ⚙️
+# https://cog.run/python
+
+import os
+import subprocess
+import time
+from cog import BasePredictor, Input, Path
+
+from diffusers import CogView3PlusPipeline
+import torch
+
+
+MODEL_URL = "https://weights.replicate.delivery/default/THUDM/CogView3/model_cache.tar"
+MODEL_CACHE = "model_cache"
+
+
+def download_weights(url, dest):
+    start = time.time()
+    print("downloading url: ", url)
+    print("downloading to: ", dest)
+    subprocess.check_call(["pget", "-x", url, dest], close_fds=False)
+    print("downloading took: ", time.time() - start)
+
+
+class Predictor(BasePredictor):
+    def setup(self) -> None:
+        """Load the model into memory to make running multiple predictions efficient"""
+
+        if not os.path.exists(MODEL_CACHE):
+            download_weights(MODEL_URL, MODEL_CACHE)
+
+        self.pipe = CogView3PlusPipeline.from_pretrained(
+            MODEL_CACHE, torch_dtype=torch.bfloat16  # from THUDM/CogView3-Plus-3B
+        ).to("cuda")
+        self.pipe.enable_model_cpu_offload()
+        self.pipe.vae.enable_slicing()
+        self.pipe.vae.enable_tiling()
+
+    def predict(
+        self,
+        prompt: str = Input(
+            description="Input prompt",
+            default="a photo of an astronaut riding a horse on mars",
+        ),
+        negative_prompt: str = Input(
+            description="Specify things to not see in the output",
+            default="",
+        ),
+        width: int = Input(
+            description="Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits",
+            choices=[
+                512,
+                576,
+                640,
+                704,
+                768,
+                832,
+                896,
+                960,
+                1024,
+                1088,
+                1152,
+                1216,
+                1280,
+                1344,
+                1408,
+                1472,
+                1536,
+                1600,
+                1664,
+                1728,
+                1792,
+                1856,
+                1920,
+                1984,
+                2048,
+            ],
+            default=1024,
+        ),
+        height: int = Input(
+            description="Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits",
+            choices=[
+                512,
+                576,
+                640,
+                704,
+                768,
+                832,
+                896,
+                960,
+                1024,
+                1088,
+                1152,
+                1216,
+                1280,
+                1344,
+                1408,
+                1472,
+                1536,
+                1600,
+                1664,
+                1728,
+                1792,
+                1856,
+                1920,
+                1984,
+                2048,
+            ],
+            default=1024,
+        ),
+        num_inference_steps: int = Input(
+            description="Number of denoising steps", ge=1, le=500, default=50
+        ),
+        guidance_scale: float = Input(
+            description="Scale for classifier-free guidance", ge=1, le=20, default=7
+        ),
+        seed: int = Input(
+            description="Random seed. Leave blank to randomize the seed", default=None
+        ),
+    ) -> Path:
+        """Run a single prediction on the model"""
+        if seed is None:
+            seed = int.from_bytes(os.urandom(2), "big")
+        print(f"Using seed: {seed}")
+
+        image = self.pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_images_per_prompt=1,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=torch.Generator().manual_seed(seed),
+        ).images[0]
+
+        torch.cuda.empty_cache()
+
+        out_path = "/tmp/out.png"
+
+        image.save(out_path)
+        return Path(out_path)