README.md

---
datasets: patrickvonplaten/images
---
# Diffusers Tools

This is a collection of scripts that can be useful for various tasks related to the [diffusers library](https://github.com/huggingface/diffusers)

## 1. Test against original checkpoints

**It's very important to have visually the exact same results as the original code bases.!**

E.g. to make use `diffusers` is identical to the original [CompVis codebase](https://github.com/CompVis/stable-diffusion), you can run the following script in the original CompVis codebase:

1. Download the original [SD-1-4 checkpoint](https://huggingface.co/CompVis/stable-diffusion-v1-4) and put it in the correct folder following the instructions on: https://github.com/CompVis/stable-diffusion

2. Run the following command
```
python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --seed 0 --n_samples 1 --n_rows 1 --n_iter 1
```

and compare this to the same command in diffusers:

```python
from diffusers import DiffusionPipeline, StableDiffusionPipeline, DDIMScheduler
import torch

#  python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --seed 0 --n_samples 1 --n_rows 1 --n_iter 1
seed = 0

prompt = "a photograph of an astronaut riding a horse"
pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)

pipe = pipe.to("cuda")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
torch.manual_seed(0)
image = pipe(prompt, num_inference_steps=50).images[0]

image.save("/home/patrick_huggingface_co/images/aa_comp.png")
```

Both commands should give the following image on a V100:


## 2. Test against [k-diffusion](https://github.com/crowsonkb/k-diffusion):

You can run the following script to compare against k-diffusion.

See results [here](https://huggingface.co/datasets/patrickvonplaten/images)

```python
from diffusers import StableDiffusionKDiffusionPipeline, HeunDiscreteScheduler, StableDiffusionPipeline, DPMSolverMultistepScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler
import torch
import os

seed = 13
inference_steps = 25
#checkpoint = "CompVis/stable-diffusion-v1-4"
checkpoint = "stabilityai/stable-diffusion-2-1"
prompts = ["astronaut riding horse", "whale falling from sky", "magical forest", "highly photorealistic picture of johnny depp"]
prompts = 8 * ["highly photorealistic picture of johnny depp"]
#prompts = prompts[:1]
samplers = ["sample_dpmpp_2m", "sample_euler", "sample_heun", "sample_dpm_2", "sample_lms"]
#samplers = samplers[:1]

pipe = StableDiffusionKDiffusionPipeline.from_pretrained(checkpoint, torch_dtype=torch.float16, safety_checker=None)
pipe = pipe.to("cuda")

for i, prompt in enumerate(prompts):
    prompt_f = f"{'_'.join(prompt.split())}_{i}"
    for sampler in samplers:
        pipe.set_scheduler(sampler)
        torch.manual_seed(seed + i)
        image = pipe(prompt, num_inference_steps=inference_steps).images[0]
        checkpoint_f = f"{'--'.join(checkpoint.split('/'))}"
        os.makedirs(f"/home/patrick_huggingface_co/images/{checkpoint_f}", exist_ok=True)
        os.makedirs(f"/home/patrick_huggingface_co/images/{checkpoint_f}/{sampler}", exist_ok=True)
        image.save(f"/home/patrick_huggingface_co/images/{checkpoint_f}/{sampler}/{prompt_f}.png")


pipe = StableDiffusionPipeline(**pipe.components)
pipe = pipe.to("cuda")

for i, prompt in enumerate(prompts):
    prompt_f = f"{'_'.join(prompt.split())}_{i}"
    for sampler in samplers:
        if sampler == "sample_euler":
            pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
        elif sampler == "sample_heun":
            pipe.scheduler = HeunDiscreteScheduler.from_config(pipe.scheduler.config)
        elif sampler == "sample_dpmpp_2m":
            pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
        elif sampler == "sample_lms":
            pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
 
        torch.manual_seed(seed + i)
        image = pipe(prompt, num_inference_steps=inference_steps).images[0]
        checkpoint_f = f"{'--'.join(checkpoint.split('/'))}"
        os.makedirs("/home/patrick_huggingface_co/images/{checkpoint_f}", exist_ok=True)
        os.makedirs(f"/home/patrick_huggingface_co/images/{checkpoint_f}/{sampler}", exist_ok=True)
        image.save(f"/home/patrick_huggingface_co/images/{checkpoint_f}/{sampler}/{prompt_f}_hf.png")
```