replicate

README.md

@@ -20,6 +20,8 @@ AutoML, ByteDance Inc.
 <a href='https://res-adapter.github.io/'><img src='https://img.shields.io/badge/Project-Page-green'></a> 
 <a href='https://arxiv.org/abs/2403.02084'><img src='https://img.shields.io/badge/ Paper-Arxiv-red'></a> 
 <a href='https://huggingface.co/jiaxiangc/res-adapter'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a>
+[![Replicate](https://replicate.com/cjwbw/res-adapter/badge)](https://replicate.com/cjwbw/res-adapter) 
+
 
 
 <strong> We propose ResAdapter, a plug-and-play resolution adapter for enabling diffusion models of arbitrary style domains to generate resolution-free images: no additional training, no additional inference and no style transfer.</strong>

cog.yaml

@@ -0,0 +1,25 @@
+# Configuration for Cog ⚙️
+# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md
+
+build:
+  gpu: true
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+  python_version: "3.11"
+  python_packages:
+    - torch
+    - torchvision
+    - diffusers
+    # - transformers
+    - opencv-python
+    - omegaconf
+    - tqdm
+    - safetensors
+    - hf_transfer
+    - accelerate
+    # - peft
+  run:
+    - pip install -U peft transformers
+    - curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.6.0/pget_linux_x86_64" && chmod +x /usr/local/bin/pget
+predict: "predict.py:Predictor"

predict.py

@@ -0,0 +1,139 @@
+# Prediction interface for Cog ⚙️
+# https://github.com/replicate/cog/blob/main/docs/python.md
+
+import os
+from typing import Optional
+import subprocess
+import time
+from cog import BasePredictor, Input, Path
+import torch
+from diffusers import AutoPipelineForText2Image, DPMSolverMultistepScheduler
+from huggingface_hub import hf_hub_download
+
+
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+
+MODEL_URL = "https://weights.replicate.delivery/default/res-adapter/Lykon/dreamshaper-xl-1-0.tar"
+MODEL_WEIGHTS = "pretrained/Lykon/dreamshaper-xl-1-0"
+
+
+def download_weights(url, dest, extract=True):
+    start = time.time()
+    print("downloading url: ", url)
+    print("downloading to: ", dest)
+    args = ["pget"]
+    if extract:
+        args.append("-x")
+    subprocess.check_call(args + [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_WEIGHTS):
+            download_weights(MODEL_URL, MODEL_WEIGHTS)
+        self.default_pipe = AutoPipelineForText2Image.from_pretrained(
+            MODEL_WEIGHTS, torch_dtype=torch.float16, variant="fp16"
+        )
+        self.default_pipe.scheduler = DPMSolverMultistepScheduler.from_config(
+            self.default_pipe.scheduler.config,
+            use_karras_sigmas=True,
+            algorithm_type="sde-dpmsolver++",
+        )
+        self.default_pipe = self.default_pipe.to("cuda")
+        self.default_pipe.load_lora_weights(
+            hf_hub_download(
+                repo_id="jiaxiangc/res-adapter",
+                subfolder=f"{base_model}-i",
+                filename="resolution_lora.safetensors",
+            ),
+            adapter_name="res_adapter",
+        )
+        self.default_pipe.set_adapters(["res_adapter"], adapter_weights=[1.0])
+
+    @torch.inference_mode()
+    def predict(
+        self,
+        base_model: str = Input(
+            description="Choose a stable diffusion architecture, supporint sd1.5 and sdxl.",
+            default="sdxl",
+            choices=["sd1.5", "sdxl"],
+        ),
+        model_name: str = Input(
+            description="Name of a stable diffusion model, should have either sd1.5 or sdxl architecture.",
+            default="Lykon/dreamshaper-xl-1-0",
+        ),
+        prompt: str = Input(
+            description="Input prompt",
+            default="cinematic film still, photo of a girl, cyberpunk, neonpunk, headset, city at night, sony fe 12-24mm f/2.8 gm, close up, 32k uhd, wallpaper, analog film grain, SONY headset",
+        ),
+        negative_prompt: str = Input(
+            description="Specify things to not see in the output",
+            default="ugly, deformed, noisy, blurry, nsfw, low contrast, text, BadDream, 3d, cgi, render, fake, anime, open mouth, big forehead, long neck",
+        ),
+        width: int = Input(
+            description="Width of output image",
+            default=1024,
+        ),
+        height: int = Input(
+            description="Height of output image",
+            default=1024,
+        ),
+        num_inference_steps: int = Input(
+            description="Number of denoising steps", default=25
+        ),
+        guidance_scale: float = Input(
+            description="Scale for classifier-free guidance", ge=1, le=20, default=7.5
+        ),
+        seed: int = Input(
+            description="Random seed. Leave blank to randomize the seed", default=None
+        ),
+        show_baseline: bool = Input(
+            description="Show baseline without res-adapter for comparison.",
+            default=True,
+        ),
+    ) -> 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}")
+
+        generator = torch.Generator("cuda").manual_seed(seed)
+
+        if model_name == "Lykon/dreamshaper-xl-1-0":
+            pipe = self.default_pipe
+        else:
+            pipe = AutoPipelineForText2Image.from_pretrained(
+                model_name, torch_dtype=torch.float16, variant="fp16"
+            )
+            pipe.scheduler = DPMSolverMultistepScheduler.from_config(
+                pipe.scheduler.config,
+                use_karras_sigmas=True,
+                algorithm_type="sde-dpmsolver++",
+            )
+            pipe = pipe.to("cuda")
+
+            pipe.load_lora_weights(
+                hf_hub_download(
+                    repo_id="jiaxiangc/res-adapter",
+                    subfolder=f"{base_model}-i",
+                    filename="resolution_lora.safetensors",
+                ),
+                adapter_name="res_adapter",
+            )
+            pipe.set_adapters(["res_adapter"], adapter_weights=[1.0])
+
+        image = pipe(
+            prompt,
+            negative_prompt=negative_prompt,
+            width=width,
+            height=height,
+            guidance_scale=guidance_scale,
+            generator=generator,
+            num_inference_steps=num_inference_steps,
+        ).images[0]
+
+        out_path = "/tmp/output.png"
+        image.save(out_path)
+        return Path(out_path)