import spaces
import gradio as gr
import numpy as np
import random
import torch
from diffusers import DDPMScheduler, StableDiffusionPipeline, DDIMScheduler, UNet2DConditionModel
import p2p, generation, inversion
model_id = 'runwayml/stable-diffusion-v1-5'
dtype=torch.float16
device = "cuda" if torch.cuda.is_available() else "cpu"
# Reverse
# -----------------------------
pipe_reverse = StableDiffusionPipeline.from_pretrained(model_id,
scheduler=DDIMScheduler.from_pretrained(model_id,
subfolder="scheduler"),
).to(device=device, dtype=dtype)
unet = UNet2DConditionModel.from_pretrained("dbaranchuk/sd15-cfg-distill-unet").to(device)
pipe_reverse.unet = unet
pipe_reverse.load_lora_weights("dbaranchuk/icd-lora-sd15",
weight_name='reverse-259-519-779-999.safetensors')
pipe_reverse.fuse_lora()
pipe_reverse.to(device)
# -----------------------------
# Forward
# -----------------------------
pipe_forward = StableDiffusionPipeline.from_pretrained(model_id,
scheduler=DDIMScheduler.from_pretrained(model_id,
subfolder="scheduler"),
).to(device=device, dtype=dtype)
unet = UNet2DConditionModel.from_pretrained("dbaranchuk/sd15-cfg-distill-unet").to(device)
pipe_forward.unet = unet
pipe_forward.load_lora_weights("dbaranchuk/icd-lora-sd15",
weight_name='forward-19-259-519-779.safetensors')
pipe_forward.fuse_lora()
pipe_forward.to(device)
# -----------------------------
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
@spaces.GPU(duration=30)
def infer(image_path, input_prompt, edited_prompt, guidance, tau,
crs, srs, amplify_factor, amplify_word,
blend_orig, blend_edited, is_replacement):
tokenizer = pipe_forward.tokenizer
noise_scheduler = DDPMScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5", subfolder="scheduler", )
NUM_REVERSE_CONS_STEPS = 4
REVERSE_TIMESTEPS = [259, 519, 779, 999]
NUM_FORWARD_CONS_STEPS = 4
FORWARD_TIMESTEPS = [19, 259, 519, 779]
NUM_DDIM_STEPS = 50
solver = generation.Generator(
model=pipe_forward,
noise_scheduler=noise_scheduler,
n_steps=NUM_DDIM_STEPS,
forward_cons_model=pipe_forward,
forward_timesteps=FORWARD_TIMESTEPS,
reverse_cons_model=pipe_reverse,
reverse_timesteps=REVERSE_TIMESTEPS,
num_endpoints=NUM_REVERSE_CONS_STEPS,
num_forward_endpoints=NUM_FORWARD_CONS_STEPS,
max_forward_timestep_index=49,
start_timestep=19)
p2p.NUM_DDIM_STEPS = NUM_DDIM_STEPS
p2p.tokenizer = tokenizer
p2p.device = 'cuda'
prompt = [input_prompt]
(image_gt, image_rec), ddim_latent, uncond_embeddings = inversion.invert(
# Playing params
image_path=image_path,
prompt=prompt,
# Fixed params
is_cons_inversion=True,
w_embed_dim=512,
inv_guidance_scale=0.0,
stop_step=50,
solver=solver,
seed=10500)
p2p.NUM_DDIM_STEPS = 4
p2p.tokenizer = tokenizer
p2p.device = 'cuda'
prompts = [input_prompt,
edited_prompt
]
# Playing params
cross_replace_steps = {'default_': crs, }
self_replace_steps = srs
blend_word = (((blend_orig,), (blend_edited,)))
eq_params = {"words": (amplify_word,), "values": (amplify_factor,)}
controller = p2p.make_controller(prompts,
is_replacement, # (is_replacement) True if only one word is changed
cross_replace_steps,
self_replace_steps,
blend_word,
eq_params)
tau = tau
image, _ = generation.runner(
# Playing params
guidance_scale=guidance-1,
tau1=tau, # Dynamic guidance if tau < 1.0
tau2=tau,
# Fixed params
model=pipe_reverse,
is_cons_forward=True,
w_embed_dim=512,
solver=solver,
prompt=prompts,
controller=controller,
num_inference_steps=50,
generator=None,
latent=ddim_latent,
uncond_embeddings=uncond_embeddings,
return_type='image')
image = generation.to_pil_images(image[1, :, :, :])
return image
css="""
#col-container {
margin: 0 auto;
max-width: 1024px;
}
"""
if torch.cuda.is_available():
power_device = "GPU"
else:
power_device = "CPU"
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(
f"""
# ⚡ Invertible Consistency Distillation ⚡
# ⚡ Text-guided image editing with 8-step iCD-SD1.5 ⚡
This is a demo for [Invertible Consistency Distillation](https://yandex-research.github.io/invertible-cd/),
a diffusion distillation method proposed in [Invertible Consistency Distillation for Text-Guided Image Editing in Around 7 Steps](https://arxiv.org/abs/2406.14539)
by [Yandex Research](https://github.com/yandex-research).
Currently running on {power_device}
"""
)
gr.Markdown(
"**Please** check the examples to catch the intuition behind the hyperparameters, which are quite important for successful editing. A short description:
1. *Dynamic guidance tau*. Controls the interval where guidance is applied: if t < tau, then guidance is turned on for t < tau."
" Lower tau values provide better reference preservation. We commonly use tau=0.6 and tau=0.8.
"
"2. *Cross replace steps (crs)* and *self replace steps (srs)*. Controls the time step interval "
"where the cross- and self-attention maps are replaced. Higher values lead to better preservation of the reference image. "
"The optimal values depend on the particular image. "
"Mostly, we use crs and srs from 0.2 to 0.6.
"
"3. *Amplify word* and *Amplify factor*. Define the word that needs to be enhanced in the edited image.
"
"4. *Blended word*. Specifies the object used for making local edits. That is, edit only selected objects.
"
"5. *Is replacement*. You can set True, if you replace only one word in the original prompt. But False also works in these cases."
)
gr.Markdown(
"Feel free to check out our [image generation demo](https://huggingface.co/spaces/dbaranchuk/iCD-image-generation) as well."
)
gr.Markdown(
"If you enjoy the space, feel free to give a ⭐ to the Github Repo. [](https://github.com/yandex-research/invertible-cd)"
)
with gr.Row():
input_prompt = gr.Text(
label="Origial prompt",
max_lines=1,
placeholder="Enter your prompt",
)
prompt = gr.Text(
label="Edited prompt",
max_lines=1,
placeholder="Enter your prompt",
)
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Input image", height=512, width=512, show_label=False)
with gr.Column():
result = gr.Image(label="Result", height=512, width=512, show_label=False)
with gr.Accordion("Advanced Settings", open=True):
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=1.0,
maximum=20.0,
step=1.0,
value=20.0,
)
tau = gr.Slider(
label="Dynamic guidance tau",
minimum=0.0,
maximum=1.0,
step=0.2,
value=0.8,
)
with gr.Row():
crs = gr.Slider(
label="Cross replace steps",
minimum=0.0,
maximum=1.0,
step=0.1,
value=0.4
)
srs = gr.Slider(
label="Self replace steps",
minimum=0.0,
maximum=1.0,
step=0.1,
value=0.4,
)
with gr.Row():
amplify_word = gr.Text(
label="Amplify word",
max_lines=1,
placeholder="Enter your word",
)
amplify_factor = gr.Slider(
label="Amplify factor",
minimum=0.0,
maximum=30,
step=1.0,
value=1,
)
with gr.Row():
blend_orig = gr.Text(
label="Blended word 1",
max_lines=1,
placeholder="Enter your word",)
blend_edited = gr.Text(
label="Blended word 2",
max_lines=1,
placeholder="Enter your word",)
with gr.Row():
is_replacement = gr.Checkbox(label="Is replacement?", value=False)
with gr.Row():
run_button = gr.Button("Edit", scale=0)
with gr.Row():
examples = [
[
"examples/orig_3.jpg", #input_image
"a photo of a basket of apples", #src_prompt
"a photo of a basket of oranges", #tgt_prompt
20, #guidance_scale
0.6, #tau
0.4, #crs
0.6, #srs
1, #amplify factor
'oranges', # amplify word
'', #orig blend
'oranges', #edited blend
False #replacement
],
[
"examples/orig_3.jpg", #input_image
"a photo of a basket of apples", #src_prompt
"a photo of a basket of puppies", #tgt_prompt
20, #guidance_scale
0.6, #tau
0.4, #crs
0.1, #srs
2, #amplify factor
'puppies', # amplify word
'', #orig blend
'puppies', #edited blend
True #replacement
],
[
"examples/orig_3.jpg", #input_image
"a photo of a basket of apples", #src_prompt
"a photo of a basket of apples under snowfall", #tgt_prompt
20, #guidance_scale
0.6, #tau
0.4, #crs
0.4, #srs
30, #amplify factor
'snowfall', # amplify word
'', #orig blend
'snowfall', #edited blend
False #replacement
],
[
"examples/orig_1.jpg", #input_image
"a photo of an owl", #src_prompt
"a photo of an yellow owl", #tgt_prompt
20, #guidance_scale
0.6, #tau
0.9, #crs
0.9, #srs
20, #amplify factor
'yellow', # amplify word
'owl', #orig blend
'yellow', #edited blend
False #replacement
],
[
"examples/orig_1.jpg", #input_image
"a photo of an owl", #src_prompt
"an anime-style painting of an owl", #tgt_prompt
20, #guidance_scale
0.8, #tau
0.6, #crs
0.3, #srs
10, #amplify factor
'anime-style', # amplify word
'painting', #orig blend
'anime-style', #edited blend
False #replacement
],
[
"examples/orig_1.jpg", #input_image
"a photo of an owl", #src_prompt
"a photo of an owl underwater with many fishes nearby", #tgt_prompt
20, #guidance_scale
0.8, #tau
0.4, #crs
0.4, #srs
18, #amplify factor
'fishes', # amplify word
'', #orig blend
'fishes', #edited blend
False #replacement
],
[
"examples/orig_2.jpg", #input_image
"a photograph of a teddy bear sitting on a wall", #src_prompt
"a photograph of a teddy bear sitting on a wall surrounded by roses", #tgt_prompt
20, #guidance_scale
0.6, #tau
0.4, #crs
0.1, #srs
25, #amplify factor
'roses', # amplify word
'', #orig blend
'roses', #edited blend
False #replacement
],
[
"examples/orig_2.jpg", #input_image
"a photograph of a teddy bear sitting on a wall", #src_prompt
"a photograph of a wooden bear sitting on a wall", #tgt_prompt
20, #guidance_scale
0.8, #tau
0.5, #crs
0.5, #srs
14, #amplify factor
'wooden', # amplify word
'', #orig blend
'wooden', #edited blend
True #replacement
],
[
"examples/orig_2.jpg", #input_image
"a photograph of a teddy bear sitting on a wall", #src_prompt
"a photograph of a teddy rabbit sitting on a wall", #tgt_prompt
20, #guidance_scale
0.8, #tau
0.4, #crs
0.4, #srs
3, #amplify factor
'rabbit', # amplify word
'', #orig blend
'rabbit', #edited blend
True #replacement
],
]
gr.Examples(
examples = examples,
inputs =[input_image, input_prompt, prompt,
guidance_scale, tau, crs, srs, amplify_factor, amplify_word,
blend_orig, blend_edited, is_replacement],
outputs=[
result
],
fn=infer, cache_examples=True
)
run_button.click(
fn = infer,
inputs=[input_image, input_prompt, prompt,
guidance_scale, tau, crs, srs, amplify_factor, amplify_word,
blend_orig, blend_edited, is_replacement],
outputs = [result]
)
demo.queue().launch()