refiners/tests/e2e/test_diffusion_ref

Note about this data

Expected outputs

expected_*.png files are the output of the same diffusion run with a different codebase, usually diffusers with the same settings as us (DPMSolverMultistepScheduler, VAE patched to remove randomness, same seed...).

For instance here is how we generate expected_std_random_init.png:

import torch

from diffusers import DPMSolverMultistepScheduler
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    torch_dtype=torch.float32,
).to("cuda)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)

prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"

torch.manual_seed(2)
output = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=30,
    guidance_scale=7.5,
)

output.images[0].save("std_random_init_expected.png")

Special cases:

• expected_refonly.png has been generated with Stable Diffusion web UI.
• expected_inpainting_refonly.png has been generated with refiners itself (and inspected so that it looks reasonable).

Other images

• cutecat_init.png is generated with the same Diffusers script and prompt but with seed 1234.

• kitchen_dog.png is generated with the same Diffusers script and negative prompt, seed 12, positive prompt "a small brown dog, detailed high-quality professional image, sitting on a chair, in a kitchen".

• kitchen_mask.png is made manually.

• Controlnet guides have been manually generated using open source software and models, namely:

  • Canny: opencv-python
  • Depth: https://github.com/isl-org/ZoeDepth
  • Lineart: https://github.com/lllyasviel/ControlNet-v1-1-nightly/tree/main/annotator/lineart
  • Normals: https://github.com/baegwangbin/surface_normal_uncertainty/tree/fe2b9f1
  • SAM: https://huggingface.co/spaces/mfidabel/controlnet-segment-anything

• cyberpunk_guide.png comes from Lexica.

• inpainting-mask.png, inpainting-scene.png and inpainting-target.png have been generated as follows:

  • inpainting-mask.png: negated version of a mask computed with SAM automatic mask generation using the vit_h checkpoint
  • inpainting-scene.png: cropped-to-square-and-resized version of https://unsplash.com/photos/RCz6eSVPGYU by @jannerboy62
  • inpainting-target.png: computed with convert <(convert -size 512x512 xc:white png:-) kitchen_dog.png <(convert inpainting-mask.png -negate png:-) -compose Over -composite inpainting-target.png

VAE without randomness

--- a/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
+++ b/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
@@ -524,13 +524,8 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
                 f" size of {batch_size}. Make sure the batch size matches the length of the generators."
             )

-        if isinstance(generator, list):
-            init_latents = [
-                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
-            ]
-            init_latents = torch.cat(init_latents, dim=0)
-        else:
-            init_latents = self.vae.encode(image).latent_dist.sample(generator)
+        init_latents = [self.vae.encode(image[i : i + 1]).latent_dist.mean for i in range(batch_size)]
+        init_latents = torch.cat(init_latents, dim=0)

         init_latents = self.vae.config.scaling_factor * init_latents