mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-24 23:28:45 +00:00
114 lines
4.5 KiB
Markdown
114 lines
4.5 KiB
Markdown
# 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](#vae-without-randomness), same seed...).
|
|
|
|
For instance here is how we generate `expected_std_random_init.png`:
|
|
|
|
```py
|
|
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](https://github.com/AUTOMATIC1111/stable-diffusion-webui).
|
|
- `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](https://lexica.art/prompt/5ba40855-0d0c-4322-8722-51115985f573).
|
|
|
|
- `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](https://github.com/facebookresearch/segment-anything) 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
|
|
|
|
```diff
|
|
--- 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.:
|
|
|
|
```py
|
|
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")
|
|
```
|