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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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extra samplers from K-diffusion
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AUTOMATIC
parent
91dc8710ec
commit
c9579b51a6
55
webui.py
55
webui.py
@ -1,4 +1,6 @@
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import argparse, os, sys, glob
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from collections import namedtuple
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import torch
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import torch.nn as nn
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import numpy as np
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@ -16,7 +18,7 @@ import time
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import json
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import traceback
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import k_diffusion as K
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import k_diffusion.sampling
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from ldm.util import instantiate_from_config
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.models.diffusion.plms import PLMSSampler
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@ -60,6 +62,19 @@ css_hide_progressbar = """
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.meta-text { display:none!important; }
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"""
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SamplerData = namedtuple('SamplerData', ['name', 'constructor'])
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samplers = [
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*[SamplerData(x[0], lambda model: KDiffusionSampler(model, x[1])) for x in [
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('LMS', 'sample_lms'),
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('Heun', 'sample_heun'),
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('Euler', 'sample_euler'),
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('Euler ancestral', 'sample_euler_ancestral'),
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('DPM 2', 'sample_dpm_2'),
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('DPM 2 Ancestral', 'sample_dpm_2_ancestral'),
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] if hasattr(k_diffusion.sampling, x[1])],
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SamplerData('DDIM', lambda model: DDIMSampler(model)),
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SamplerData('PLMS', lambda model: PLMSSampler(model)),
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]
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class Options:
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@ -142,16 +157,18 @@ class CFGDenoiser(nn.Module):
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class KDiffusionSampler:
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def __init__(self, m):
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def __init__(self, m, funcname):
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self.model = m
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self.model_wrap = K.external.CompVisDenoiser(m)
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self.model_wrap = k_diffusion.external.CompVisDenoiser(m)
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self.funcname = funcname
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def sample(self, S, conditioning, batch_size, shape, verbose, unconditional_guidance_scale, unconditional_conditioning, eta, x_T):
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sigmas = self.model_wrap.get_sigmas(S)
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x = x_T * sigmas[0]
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model_wrap_cfg = CFGDenoiser(self.model_wrap)
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samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}, disable=False)
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fun = getattr(k_diffusion.sampling, self.funcname)
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samples_ddim = fun(model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}, disable=False)
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return samples_ddim, None
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@ -526,7 +543,7 @@ def get_learned_conditioning_with_embeddings(model, prompts):
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return model.get_learned_conditioning(prompts)
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def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, batch_size, n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, do_not_save_grid=False):
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def process_images(outpath, func_init, func_sample, prompt, seed, sampler_index, batch_size, n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, do_not_save_grid=False):
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"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
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assert prompt is not None
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@ -579,7 +596,7 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
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def infotext():
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return f"""
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{prompt}
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Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}
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Steps: {steps}, Sampler: {samplers[sampler_index].name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}
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""".strip() + "".join(["\n\n" + x for x in comments])
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if os.path.exists(cmd_opts.embeddings_dir):
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@ -645,17 +662,10 @@ Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{',
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return output_images, seed, infotext()
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def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int):
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def txt2img(prompt: str, ddim_steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int):
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outpath = opts.outdir or "outputs/txt2img-samples"
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if sampler_name == 'PLMS':
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sampler = PLMSSampler(model)
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elif sampler_name == 'DDIM':
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sampler = DDIMSampler(model)
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elif sampler_name == 'k-diffusion':
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sampler = KDiffusionSampler(model)
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else:
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raise Exception("Unknown sampler: " + sampler_name)
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sampler = samplers[sampler_index].constructor(model)
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def init():
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pass
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@ -670,7 +680,7 @@ def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, p
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func_sample=sample,
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prompt=prompt,
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seed=seed,
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sampler_name=sampler_name,
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sampler_index=sampler_index,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=ddim_steps,
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@ -732,7 +742,7 @@ txt2img_interface = gr.Interface(
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inputs=[
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gr.Textbox(label="Prompt", placeholder="A corgi wearing a top hat as an oil painting.", lines=1),
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gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
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gr.Radio(label='Sampling method', choices=["DDIM", "PLMS", "k-diffusion"], value="k-diffusion"),
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gr.Radio(label='Sampling method', choices=[x.name for x in samplers], value=samplers[0].name, type="index"),
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gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
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gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
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@ -756,7 +766,7 @@ txt2img_interface = gr.Interface(
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def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int):
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outpath = opts.outdir or "outputs/img2img-samples"
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sampler = KDiffusionSampler(model)
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sampler = KDiffusionSampler(model, 'sample_lms')
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assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
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@ -785,7 +795,7 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat
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xi = x0 + noise
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sigma_sched = sigmas[ddim_steps - t_enc - 1:]
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model_wrap_cfg = CFGDenoiser(sampler.model_wrap)
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samples_ddim = K.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': cfg_scale}, disable=False)
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samples_ddim = k_diffusion.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': cfg_scale}, disable=False)
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return samples_ddim
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if loopback:
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@ -800,7 +810,7 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat
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func_sample=sample,
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prompt=prompt,
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seed=seed,
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sampler_name='k-diffusion',
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sampler_index=0,
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batch_size=1,
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n_iter=1,
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steps=ddim_steps,
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@ -835,7 +845,7 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat
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func_sample=sample,
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prompt=prompt,
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seed=seed,
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sampler_name='k-diffusion',
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sampler_index=0,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=ddim_steps,
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@ -877,10 +887,10 @@ img2img_interface = gr.Interface(
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gr.Number(label='Seed'),
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gr.HTML(),
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],
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title="Stable Diffusion Image-to-Image",
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allow_flagging="never",
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)
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def run_GFPGAN(image, strength):
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image = image.convert("RGB")
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@ -904,7 +914,6 @@ gfpgan_interface = gr.Interface(
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gr.Number(label='Seed', visible=False),
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gr.HTML(),
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],
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title="GFPGAN",
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description="Fix faces on images",
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allow_flagging="never",
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)
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