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# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
# %% auto 0
__all__ = ['model_name', 'revision', 'dtype', 'device', 'better_vae', 'unet_attn_slice', 'sampler_kls', 'hf_sampler',
'model_kwargs', 'num_steps', 'height', 'width', 'k_sampler', 'use_karras_sigmas', 'NEG_PROMPT',
'generation_kwargs', 'baseline_g', 'max_val', 'min_val', 'num_warmup_steps', 'warmup_init_val', 'num_cycles',
'k_decay', 'DEFAULT_COS_PARAMS', 'static_sched', 'k_sched', 'inv_k_sched', 'schedules', 'iface',
'load_model', 'cos_harness', 'compare_dynamic_guidance']
# %% app.ipynb 1
import gradio as gr
import cf_guidance
import min_diffusion
import torch
import nbdev
# %% app.ipynb 2
## MODEL SETUP
######################################
######################################
model_name = 'stabilityai/stable-diffusion-2'
revision = 'fp16'
dtype = torch.float16
device = ('cpu','cuda')[torch.cuda.is_available()]
# model parameters
better_vae = ''
unet_attn_slice = True
sampler_kls = 'dpm_multi'
hf_sampler = 'dpm_multi'
model_kwargs = {
'better_vae': better_vae,
'unet_attn_slice': unet_attn_slice,
'sampler_kls': hf_sampler,
}
def load_model():
pipeline = min_diffusion.core.MinimalDiffusion(
model_name,
device,
dtype,
revision,
**model_kwargs,
)
pipeline.load()
return pipeline
######################################
######################################
# %% app.ipynb 3
## GENERATION PARAMETERS
######################################
######################################
num_steps = 18
height, width = 768, 768
k_sampler = 'k_dpmpp_2m' #'k_dpmpp_sde'
use_karras_sigmas = True
# a good negative prompt
NEG_PROMPT = "ugly, stock photo, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy, blurred, text, watermark, grainy"
generation_kwargs = {
'num_steps': num_steps,
'height': height,
'width': width,
'k_sampler': k_sampler,
'negative_prompt': NEG_PROMPT,
'use_karras_sigmas': use_karras_sigmas,
}
######################################
######################################
# %% app.ipynb 4
## dynamicCFG SETUP
######################################
######################################
# default cosine schedule parameters
baseline_g = 9 # default, static guidance value
max_val = 9 # the max scheduled guidance scaling value
min_val = 6 # the minimum scheduled guidance value
num_warmup_steps = 0 # number of warmup steps
warmup_init_val = 0 # the intial warmup value
num_cycles = 0.5 # number of cosine cycles
k_decay = 1 # k-decay for cosine curve scaling
# group the default schedule parameters
DEFAULT_COS_PARAMS = {
'max_val': max_val,
'num_steps': num_steps,
'min_val': min_val,
'num_cycles': num_cycles,
'k_decay': k_decay,
'num_warmup_steps': num_warmup_steps,
'warmup_init_val': warmup_init_val,
}
def cos_harness(new_params: dict) -> dict:
'''Creates cosine schedules with updated parameters in `new_params`
'''
# start from the given baseline `default_params`
cos_params = dict(DEFAULT_COS_PARAMS)
# update the with the new, given parameters
cos_params.update(new_params)
# return the new cosine schedule
sched = cf_guidance.schedules.get_cos_sched(**cos_params)
return sched
# build the static schedule
static_sched = [baseline_g] * num_steps
# build the inverted kdecay schedule
k_sched = cos_harness({'k_decay': 0.2})
inv_k_sched = [max_val - g + min_val for g in k_sched]
# group the schedules
schedules = {
'cosine': {'g': inv_k_sched},
'static': {'g': static_sched},
}
######################################
######################################
# %% app.ipynb 5
def compare_dynamic_guidance(prompt):
'''
Compares the default, static Classifier-free Guidance to a dynamic schedule.
Model and sampling paramters:
Stable Diffusion 2 v-model
Half-precision
DPM++ 2M sampler, with Karras sigma schedule
18 sampling steps
(768 x 768) image
Using a generic negative prompt
Schedules:
Static guidance with scale of 9
Inverse kDecay (cosine variant) scheduled guidance
'''
# load the model
pipeline = load_model()
# stores the output images
res = []
# generate images with static and dynamic schedules
for (name,sched) in schedules.items():
# make the guidance norm
gtfm = cf_guidance.transforms.GuidanceTfm(sched)
# generate the image
with torch.autocast(device), torch.no_grad():
img = pipeline.generate(prompt, gtfm, **generation_kwargs)
# add the generated image
res.append(name)
# return the generated images
return {
'values': res,
'label': 'Cosine vs. Static CFG'
}
# %% app.ipynb 6
iface = gr.Interface(
compare_dynamic_guidance,
inputs="text",
outputs=gr.Gallery(),
title="Comparison with dynamic Classifier-free Guidance Comparison",
)
iface.launch()
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