Spaces:
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add: initial files.
Browse files- README.md +2 -2
- app.py +205 -0
- requirements.txt +5 -0
README.md
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---
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title: Evaluate
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colorFrom: gray
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colorTo: indigo
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sdk: gradio
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---
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title: Evaluate StableDiffusionPipeline with Different Schedulers
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emoji: ⏰
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colorFrom: gray
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colorTo: indigo
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sdk: gradio
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app.py
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import importlib
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from functools import partial
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from typing import List
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import gradio as gr
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import numpy as np
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import torch
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from diffusers import StableDiffusionPipeline
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from PIL import Image
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from torchmetrics.functional.multimodal import clip_score
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from torchmetrics.image.inception import InceptionScore
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SEED = 0
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WEIGHT_DTYPE = torch.float16
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inception_score_fn = InceptionScore(normalize=True)
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torch.manual_seed(SEED)
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clip_score_fn = partial(clip_score, model_name_or_path="openai/clip-vit-base-patch16")
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def make_grid(images, rows, cols):
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w, h = images[0].size
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grid = Image.new("RGB", size=(cols * w, rows * h))
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for i, image in enumerate(images):
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grid.paste(image, box=(i % cols * w, i // cols * h))
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return grid
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# Copied from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L814
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def numpy_to_pil(images):
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"""
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Convert a numpy image or a batch of images to a PIL image.
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"""
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if images.ndim == 3:
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images = images[None, ...]
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images = (images * 255).round().astype("uint8")
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if images.shape[-1] == 1:
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# special case for grayscale (single channel) images
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pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
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else:
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pil_images = [Image.fromarray(image) for image in images]
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return pil_images
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def prepare_report(scheduler_name: str, results: dict):
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image_grid = results["images"]
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scores = results["scores"]
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img_str = ""
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image_name = f"{scheduler_name}_images.png"
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image_grid.save(image_name)
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img_str = f"![img_grid_{scheduler_name}](./{image_name})\n"
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report_str = f"""
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\n\n## {scheduler_name}
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### Sample images
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{img_str}
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### Scores
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{scores}
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\n\n
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"""
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return report_str
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def initialize_pipeline(checkpoint: str):
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sd_pipe = StableDiffusionPipeline.from_pretrained(
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checkpoint, torch_dtype=WEIGHT_DTYPE
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)
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sd_pipe = sd_pipe.to("cuda")
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original_scheduler_config = sd_pipe.scheduler.config
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return sd_pipe, original_scheduler_config
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def get_scheduler(scheduler_name):
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schedulers_lib = importlib.import_module("diffusers", package="schedulers")
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scheduler_abs = getattr(schedulers_lib, scheduler_name)
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return scheduler_abs
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def get_latents(num_images_per_prompt: int, seed=SEED):
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generator = torch.manual_seed(seed)
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latents = np.random.RandomState(seed).standard_normal(
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(num_images_per_prompt, 4, 64, 64)
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)
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latents = torch.from_numpy(latents).to(device="cuda", dtype=WEIGHT_DTYPE)
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return latents
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def compute_metrics(images: np.ndarray, prompts: List[str]):
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inception_score_fn.update(torch.from_numpy(images).permute(0, 3, 1, 2))
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inception_score = inception_score_fn.compute()
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images_int = (images * 255).astype("uint8")
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clip_score = clip_score_fn(
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torch.from_numpy(images_int).permute(0, 3, 1, 2), prompts
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).detach()
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return {
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"inception_score (⬆️)": {
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"mean": round(float(inception_score[0]), 4),
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"std": round(float(inception_score[1]), 4),
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},
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"clip_score (⬆️)": round(float(clip_score), 4),
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}
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def run(
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prompt: str,
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num_images_per_prompt: int,
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num_inference_steps: int,
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checkpoint: str,
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schedulers_to_test: List[str],
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):
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all_images = {}
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sd_pipeline, original_scheduler_config = initialize_pipeline(checkpoint)
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latents = get_latents(num_images_per_prompt)
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prompts = [prompt] * num_images_per_prompt
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images = sd_pipeline(
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prompts,
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latents=latents,
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num_inference_steps=num_inference_steps,
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output_type="numpy",
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).images
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original_scheduler_name = original_scheduler_config._class_name
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all_images.update(
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{
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original_scheduler_name: {
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"images": make_grid(numpy_to_pil(images), 1, num_images_per_prompt),
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"scores": compute_metrics(images, prompts),
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}
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}
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)
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print("First scheduler complete.")
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for scheduler_name in schedulers_to_test:
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if scheduler_name == original_scheduler_name:
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continue
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scheduler_cls = get_scheduler(scheduler_name)
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current_scheduler = scheduler_cls.from_config(original_scheduler_config)
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sd_pipeline.scheduler = current_scheduler
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cur_scheduler_images = sd_pipeline(
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prompts, num_inference_steps=num_inference_steps, output_type="numpy"
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).images
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all_images.update(
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{
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scheduler_name: {
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"images": make_grid(
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numpy_to_pil(cur_scheduler_images), 1, num_images_per_prompt
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),
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"scores": compute_metrics(cur_scheduler_images, prompts),
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}
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}
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)
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print(f"{scheduler_name} complete.")
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output_str = ""
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for scheduler_name in all_images:
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print(f"scheduler_name: {scheduler_name}")
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output_str += prepare_report(scheduler_name, all_images[scheduler_name])
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print(output_str)
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return output_str
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demo = gr.Interface(
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run,
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inputs=[
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gr.Text(max_lines=1, placeholder="a painting of a dog"),
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gr.Slider(3, 10, value=3),
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gr.Slider(10, 100, value=50),
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gr.Dropdown(
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[
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"CompVis/stable-diffusion-v1-4",
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"runwayml/stable-diffusion-v1-5",
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"stabilityai/stable-diffusion-2-base",
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],
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value="CompVis/stable-diffusion-v1-4",
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multiselect=False,
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),
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gr.Dropdown(
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[
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"EulerDiscreteScheduler",
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"PNDMScheduler",
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"LMSDiscreteScheduler",
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"DPMSolverMultistepScheduler",
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"DDIMScheduler",
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],
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value=["LMSDiscreteScheduler"],
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multiselect=True,
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),
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],
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outputs=[gr.Markdown().style()],
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allow_flagging=False,
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)
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demo.launch()
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requirements.txt
ADDED
@@ -0,0 +1,5 @@
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torchmetrics[image]
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transformers
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diffusers
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accelerate
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numpy
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