refiners/tests/e2e/test_diffusion_ref/README.md

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:

• For self-attention guidance, StableDiffusionSAGPipeline has been used instead of the default pipeline.
• expected_refonly.png has been generated with Stable Diffusion web UI.
• The following references have been generated with refiners itself (and inspected so that they look reasonable):
  • expected_karras_random_init.png,
  • expected_inpainting_refonly.png,
  • expected_image_ip_adapter_woman.png,
  • expected_image_sdxl_ip_adapter_woman.png
  • expected_ip_adapter_controlnet.png
  • expected_t2i_adapter_xl_canny.png
  • expected_image_sdxl_ip_adapter_plus_woman.png
  • expected_cutecat_sdxl_ddim_random_init_sag.png
  • expected_cutecat_sdxl_euler_random_init.png
  • expected_restart.png
  • expected_freeu.png
  • expected_dropy_slime_9752.png
  • expected_sdxl_dpo_lora.png
  • expected_sdxl_multi_loras.png
  • expected_image_ip_adapter_multi.png
  • expected_controllora_CPDS.png
  • expected_controllora_PyraCanny.png
  • expected_controllora_PyraCanny+CPDS.png
  • expected_controllora_disabled.png
  • expected_style_aligned.png
  • expected_controlnet_canny_scale_decay.png
  • expected_multi_diffusion_dpm.png
  • expected_multi_upscaler.png

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 (x) 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

(x): excepted fairy_guide_canny.png which comes from TencentARC/t2i-adapter-canny-sdxl-1.0

• 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

• woman.png comes from tencent-ailab/IP-Adapter.

• statue.png comes from tencent-ailab/IP-Adapter.

• cutecat_guide_PyraCanny.png and cutecat_guide_CPDS.png were generated inside Fooocus.

• low_res_dog.png and expected_controlnet_tile.png are taken from Diffusers documentation, respectively named original.png and output.png.

• clarity_input_example.png is taken from the Replicate demo of the Clarity upscaler.

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

Textual Inversion

• expected_textual_inversion_random_init.png has been generated with StableDiffusionPipeline, e.g.:

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)
pipe.load_textual_inversion("sd-concepts-library/gta5-artwork")

prompt = "a cute cat on a <gta5-artwork>"
negative_prompt = ""

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

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