starting from dream-templates base, updated all libs

cog.yaml

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+build:
+  gpu: true
+  cuda: "11.6"
+  python_version: "3.10"
+  python_packages:
+    - "diffusers==0.16.1"
+      # - "opencv-contrib-python==4.6.0.66"
+    # - "torchvision==0.15.1"
+      # - "timm==0.6.12"
+    - "torch==2.0.1"
+      # - "ftfy==6.1.1"
+    - "scipy==1.10.1"
+    - "transformers==4.29.2"
+    - "accelerate==0.19.0"
+  run:
+    # - "pip install git+https://github.com/patrickvonplaten/controlnet_aux.git@dfb604120af6e5acf21936f79aecb046d0faaa29"
+    - "pip install compel==1.0.4"
+predict: "predict.py:Predictor"

predict.py

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+import datetime
+import random
+
+MARKER = "anotherjesse-sd-timings-%s" % random.randint(0, 1000000)
+
+print(MARKER, "start:", datetime.datetime.now())
+
+from typing import Iterator
+
+from cog import BasePredictor, Input, Path
+import os
+import torch
+from diffusers import (
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    HeunDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    StableDiffusionPipeline,
+    UniPCMultistepScheduler,
+)
+
+MODEL_CACHE = "diffusers-cache"
+BASE_MODEL_PATH = "./weights"
+
+print(MARKER, "finished imports:", datetime.datetime.now())
+
+
+class Predictor(BasePredictor):
+    def setup(self):
+        """Load the model into memory to make running multiple predictions efficient"""
+        print(MARKER, "Loading pipeline...", datetime.datetime.now())
+
+        if not os.path.exists(BASE_MODEL_PATH):
+            self.real = False
+            return
+
+        print(MARKER, "Loading txt2img...")
+        self.txt2img_pipe = StableDiffusionPipeline.from_pretrained(
+            BASE_MODEL_PATH,
+            torch_dtype=torch.float16,
+            local_files_only=True,
+        ).to("cuda")
+
+        self.safety_checker = self.txt2img_pipe.safety_checker
+        self.real = True
+
+        print(MARKER, "loaded  pipeline...", datetime.datetime.now())
+
+    def make_scheduler(self, name, config):
+        return {
+            "DDIM": DDIMScheduler.from_config(config),
+            "DPMSolverMultistep": DPMSolverMultistepScheduler.from_config(config),
+            "HeunDiscrete": HeunDiscreteScheduler.from_config(config),
+            "K_EULER_ANCESTRAL": EulerAncestralDiscreteScheduler.from_config(config),
+            "K_EULER": EulerDiscreteScheduler.from_config(config),
+            "KLMS": LMSDiscreteScheduler.from_config(config),
+            "PNDM": PNDMScheduler.from_config(config),
+            "UniPCMultistep": UniPCMultistepScheduler.from_config(config),
+        }[name]
+
+    def predict(
+        self,
+        prompt: str = Input(
+            description="Input prompt",
+            default="photo of cjw person",
+        ),
+        negative_prompt: str = Input(
+            description="Specify things to not see in the output",
+            default=None,
+        ),
+        width: int = Input(
+            description="Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits",
+            choices=[128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024],
+            default=512,
+        ),
+        height: int = Input(
+            description="Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits",
+            choices=[128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024],
+            default=512,
+        ),
+        num_outputs: int = Input(
+            description="Number of images to output.",
+            ge=1,
+            le=10,
+            default=1,
+        ),
+        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=7.5
+        ),
+        prompt_strength: float = Input(
+            description="Prompt strength when using init image. 1.0 corresponds to full destruction of information in init image",
+            default=0.8,
+        ),
+        scheduler: str = Input(
+            default="DPMSolverMultistep",
+            choices=[
+                "DDIM",
+                "DPMSolverMultistep",
+                "HeunDiscrete",
+                "K_EULER_ANCESTRAL",
+                "K_EULER",
+                "KLMS",
+                "PNDM",
+                "UniPCMultistep",
+            ],
+            description="Choose a scheduler.",
+        ),
+        disable_safety_check: bool = Input(
+            description="Disable safety check. Use at your own risk!", default=False
+        ),
+        seed: int = Input(
+            description="Random seed. Leave blank to randomize the seed", default=None
+        ),
+    ) -> Iterator[Path]:
+        """Run a single prediction on the model"""
+
+        if not self.real:
+            raise RuntimeError("This is a template, not a real model - add weights")
+
+        print(MARKER, "Using txt2img pipeline")
+        pipe = self.txt2img_pipe
+        extra_kwargs = {
+            "width": width,
+            "height": height,
+        }
+
+        if seed is None:
+            seed = int.from_bytes(os.urandom(2), "big")
+        print(MARKER, f"Using seed: {seed}")
+
+        if width * height > 786432:
+            raise ValueError(
+                "Maximum size is 1024x768 or 768x1024 pixels, because of memory limits. Please select a lower width or height."
+            )
+
+        pipe.scheduler = self.make_scheduler(scheduler, pipe.scheduler.config)
+
+        if disable_safety_check:
+            pipe.safety_checker = None
+        else:
+            pipe.safety_checker = self.safety_checker
+
+        result_count = 0
+        for idx in range(num_outputs):
+            this_seed = seed + idx
+            generator = torch.Generator("cuda").manual_seed(this_seed)
+            output = pipe(
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                guidance_scale=guidance_scale,
+                generator=generator,
+                num_inference_steps=num_inference_steps,
+                **extra_kwargs,
+            )
+
+            if output.nsfw_content_detected and output.nsfw_content_detected[0]:
+                continue
+
+            output_path = f"/tmp/seed-{this_seed}.png"
+            output.images[0].save(output_path)
+            yield Path(output_path)
+            result_count += 1
+
+        if result_count == 0:
+            raise Exception(
+                f"NSFW content detected. Try running it again, or try a different prompt."
+            )