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https://github.com/vladmandic/automatic
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update sampler definitions
Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
+12
-11
@@ -1,5 +1,5 @@
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# TASK: Schedulers
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## Notes
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This is a codebase for diffusion schedulers implemented for `diffusers` library and ported from `res4lyf` repository at <https://github.com/ClownsharkBatwing/RES4LYF>
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@@ -8,16 +8,17 @@ Ported schedulers codebase is in `modules/res4lyf`, do not modify any other file
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## Testing
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Current focus is on following code-paths:
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- using `epsilon` prediction type
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- using `StableDiffusionXLPipeline` pipeline for *text2image*
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All schedulers were tested using prediction type `epsilon` and `StableDiffusionXLPipeline` pipeline for *text2image*: WORKING GOOD!
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Shifting focus to testing prediction type `flow_prediction` and `ZImagePipeline` pipeline for *text2image*
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## Results
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- *ETDRKScheduler, LawsonScheduler, ABNorsettScheduler, RESSinglestepScheduler, RESSinglestepSDEScheduler, PECScheduler, etc.*:
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do NOT modify behavior and codebase for these schedulers as they produce good outputs under all circumstances
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if needed, you can use them as gold-standard references to compare other schedulers against
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- *RESUnifiedScheduler*, *DEISMultistepScheduler, RESMultistepScheduler*
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work fine with `rk_type=res_2s`, `rk_type=deis_1s` and similar single-step params,
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but with `rk_type=res_2m`, `rk_type=deis_2m` and similar multi-step params
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image looks fine in early steps, but then degrages at the final steps with what looks like too much noise
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- so far all tested schedules produce blocky/pixelated and unresolved output
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## TODO
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- focus on a single scheduler only. lets pick abnorsett_2m
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- validate config params: is this ok?
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config={'num_train_timesteps': 1000, 'beta_start': 0.0001, 'beta_end': 0.02, 'beta_schedule': 'linear', 'prediction_type': 'flow_prediction', 'variant': 'abnorsett_2m', 'use_analytic_solution': True, 'timestep_spacing': 'linspace', 'steps_offset': 0, 'use_flow_sigmas': True, 'shift': 3, 'base_shift': 0.5, 'max_shift': 1.15, 'base_image_seq_len': 256, 'max_image_seq_len': 4096}
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- check code
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@@ -97,12 +97,14 @@ except Exception as e:
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if os.environ.get('SD_SAMPLER_DEBUG', None) is not None:
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errors.display(e, 'Samplers')
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config = {
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# beta_start, beta_end are typically per-scheduler, but we don't want them as they should be taken from the model itself as those are values model was trained on
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# prediction_type is ideally set in model as well, but it maybe needed that we do auto-detect of model type in the future
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'All': { 'num_train_timesteps': 1000, 'beta_start': 0.0001, 'beta_end': 0.02, 'beta_schedule': 'linear', 'prediction_type': 'epsilon' },
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'Res4Lyf': { 'timestep_spacing': 'linspace', "steps_offset": 0, "rescale_betas_zero_snr": False, "use_karras_sigmas": False, "use_exponential_sigmas": False, "use_beta_sigmas": False, "use_flow_sigmas": False, "shift": 1, "base_shift": 0.5, "max_shift": 1.15, "use_dynamic_shifting": False },
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}
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config.update({
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'UniPC': { 'flow_shift': 1, 'predict_x0': True, 'sample_max_value': 1.0, 'solver_order': 2, 'solver_type': 'bh2', 'thresholding': False, 'use_beta_sigmas': False, 'use_exponential_sigmas': False, 'use_flow_sigmas': False, 'use_karras_sigmas': False, 'lower_order_final': True, 'timestep_spacing': 'linspace', 'final_sigmas_type': 'zero', 'rescale_betas_zero_snr': False },
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'DDIM': { 'clip_sample': False, 'set_alpha_to_one': True, 'steps_offset': 0, 'clip_sample_range': 1.0, 'sample_max_value': 1.0, 'timestep_spacing': 'leading', 'rescale_betas_zero_snr': False, 'thresholding': False },
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@@ -162,62 +164,62 @@ config = {
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'DDPM Parallel': {},
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# res4lyf
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'ABNorsett 2M': { 'variant': 'abnorsett_2m' },
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'ABNorsett 3M': { 'variant': 'abnorsett_3m' },
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'ABNorsett 4M': { 'variant': 'abnorsett_4m' },
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'Lawson 2S A': { 'variant': 'lawson2a_2s' },
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'Lawson 2S B': { 'variant': 'lawson2b_2s' },
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'Lawson 4S': { 'variant': 'lawson4_4s' },
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'ETD-RK 2S': { 'variant': 'etdrk2_2s' },
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'ETD-RK 3S A': { 'variant': 'etdrk3_a_3s' },
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'ETD-RK 3S B': { 'variant': 'etdrk3_b_3s' },
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'ETD-RK 4S A': { 'variant': 'etdrk4_4s' },
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'ETD-RK 4S B': { 'variant': 'etdrk4_4s_alt' },
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'RES-Unified 2M': { 'rk_type': 'res_2m' },
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'RES-Unified 3M': { 'rk_type': 'res_3m' },
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'RES-Unified 2S': { 'rk_type': 'res_2s' },
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'RES-Unified 3S': { 'rk_type': 'res_3s' },
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'RES-Singlestep 2S': { 'variant': 'res_2s' },
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'RES-Singlestep 3S': { 'variant': 'res_3s' },
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'RES-Multistep 2M': { 'variant': 'res_2m' },
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'RES-Multistep 3M': { 'variant': 'res_3m' },
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'RES-SDE 2S': { 'variant': 'res_2s' },
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'RES-SDE 3S': { 'variant': 'res_3s' },
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'DEIS-Multistep': { 'order': 2 },
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'DEIS-Unified 1S': { 'rk_type': 'deis_1s' },
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'DEIS-Unified 2M': { 'rk_type': 'deis_2m' },
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'PEC 423': { 'variant': 'pec423_2h2s' },
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'PEC 433': { 'variant': 'pec433_2h3s' },
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'Sigmoid Sigma': { 'profile': 'sigmoid' },
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'Sine Sigma': { 'profile': 'sine' },
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'Easing Sigma': { 'profile': 'easing' },
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'Arcsine Sigma': { 'profile': 'arcsine' },
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'Smoothstep Sigma': { 'profile': 'smoothstep' },
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'Langevin Dynamics': { },
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'Euclidean Flow': { 'metric_type': 'euclidean' },
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'Hyperbolic Flow': { 'metric_type': 'hyperbolic' },
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'Spherical Flow': { 'metric_type': 'spherical' },
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'Lorentzian Flow': { 'metric_type': 'lorentzian' },
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'Linear-RK 2': { 'variant': 'rk2' },
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'Linear-RK 3': { 'variant': 'rk3' },
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'Linear-RK 4': { 'variant': 'rk4' },
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'Linear-RK Euler': { 'variant': 'euler' },
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'Linear-RK Heun': { 'variant': 'heun'},
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'Linear-RK Ralston': { 'variant': 'ralston'},
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'Lobatto 2': { 'variant': 'lobatto_iiia_2s' },
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'Lobatto 3': { 'variant': 'lobatto_iiia_3s' },
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'Lobatto 4': { 'variant': 'lobatto_iiia_4s' },
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'Radau IIA 2': { 'variant': 'radau_iia_2s' },
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'Radau IIA 3': { 'variant': 'radau_iia_3s' },
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'Gauss-Legendre 2S': { 'variant': 'gauss-legendre_2s' },
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'Gauss-Legendre 3S': { 'variant': 'gauss-legendre_3s' },
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'Gauss-Legendre 4S': { 'variant': 'gauss-legendre_4s' },
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'Runge-Kutta 4/4': { },
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'Runge-Kutta 5/7': { },
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'Runge-Kutta 6/7': { },
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'Specialized-RK 3S': { 'variant': 'ssprk3_3s' },
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'Specialized-RK 4S': { 'variant': 'ssprk4_4s' },
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}
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'ABNorsett 2M': { 'variant': 'abnorsett_2m', **config['Res4Lyf'] },
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'ABNorsett 3M': { 'variant': 'abnorsett_3m', **config['Res4Lyf'] },
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'ABNorsett 4M': { 'variant': 'abnorsett_4m', **config['Res4Lyf'] },
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'Lawson 2S A': { 'variant': 'lawson2a_2s', **config['Res4Lyf'] },
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'Lawson 2S B': { 'variant': 'lawson2b_2s', **config['Res4Lyf'] },
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'Lawson 4S': { 'variant': 'lawson4_4s', **config['Res4Lyf'] },
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'ETD-RK 2S': { 'variant': 'etdrk2_2s', **config['Res4Lyf'] },
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'ETD-RK 3S A': { 'variant': 'etdrk3_a_3s', **config['Res4Lyf'] },
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'ETD-RK 3S B': { 'variant': 'etdrk3_b_3s', **config['Res4Lyf'] },
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'ETD-RK 4S A': { 'variant': 'etdrk4_4s', **config['Res4Lyf'] },
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'ETD-RK 4S B': { 'variant': 'etdrk4_4s_alt', **config['Res4Lyf'] },
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'RES-Unified 2M': { 'rk_type': 'res_2m', **config['Res4Lyf'] },
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'RES-Unified 3M': { 'rk_type': 'res_3m', **config['Res4Lyf'] },
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'RES-Unified 2S': { 'rk_type': 'res_2s', **config['Res4Lyf'] },
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'RES-Unified 3S': { 'rk_type': 'res_3s', **config['Res4Lyf'] },
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'RES-Singlestep 2S': { 'variant': 'res_2s', **config['Res4Lyf'] },
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'RES-Singlestep 3S': { 'variant': 'res_3s', **config['Res4Lyf'] },
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'RES-Multistep 2M': { 'variant': 'res_2m', **config['Res4Lyf'] },
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'RES-Multistep 3M': { 'variant': 'res_3m', **config['Res4Lyf'] },
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'RES-SDE 2S': { 'variant': 'res_2s', **config['Res4Lyf'] },
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'RES-SDE 3S': { 'variant': 'res_3s', **config['Res4Lyf'] },
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'DEIS-Multistep': { 'order': 2, **config['Res4Lyf'] },
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'DEIS-Unified 1S': { 'rk_type': 'deis_1s', **config['Res4Lyf'] },
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'DEIS-Unified 2M': { 'rk_type': 'deis_2m', **config['Res4Lyf'] },
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'PEC 423': { 'variant': 'pec423_2h2s', **config['Res4Lyf'] },
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'PEC 433': { 'variant': 'pec433_2h3s', **config['Res4Lyf'] },
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'Sigmoid Sigma': { 'profile': 'sigmoid', **config['Res4Lyf'] },
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'Sine Sigma': { 'profile': 'sine', **config['Res4Lyf'] },
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'Easing Sigma': { 'profile': 'easing', **config['Res4Lyf'] },
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'Arcsine Sigma': { 'profile': 'arcsine', **config['Res4Lyf'] },
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'Smoothstep Sigma': { 'profile': 'smoothstep', **config['Res4Lyf'] },
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'Langevin Dynamics': { **config['Res4Lyf'] },
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'Euclidean Flow': { 'metric_type': 'euclidean', **config['Res4Lyf'] },
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'Hyperbolic Flow': { 'metric_type': 'hyperbolic', **config['Res4Lyf'] },
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'Spherical Flow': { 'metric_type': 'spherical', **config['Res4Lyf'] },
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'Lorentzian Flow': { 'metric_type': 'lorentzian', **config['Res4Lyf'] },
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'Linear-RK 2': { 'variant': 'rk2', **config['Res4Lyf'] },
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'Linear-RK 3': { 'variant': 'rk3', **config['Res4Lyf'] },
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'Linear-RK 4': { 'variant': 'rk4', **config['Res4Lyf'] },
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'Linear-RK Euler': { 'variant': 'euler', **config['Res4Lyf'] },
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'Linear-RK Heun': { 'variant': 'heun', **config['Res4Lyf'] },
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'Linear-RK Ralston': { 'variant': 'ralston', **config['Res4Lyf'] },
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'Lobatto 2': { 'variant': 'lobatto_iiia_2s', **config['Res4Lyf'] },
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'Lobatto 3': { 'variant': 'lobatto_iiia_3s', **config['Res4Lyf'] },
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'Lobatto 4': { 'variant': 'lobatto_iiia_4s', **config['Res4Lyf'] },
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'Radau IIA 2': { 'variant': 'radau_iia_2s', **config['Res4Lyf'] },
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'Radau IIA 3': { 'variant': 'radau_iia_3s', **config['Res4Lyf'] },
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'Gauss-Legendre 2S': { 'variant': 'gauss-legendre_2s', **config['Res4Lyf'] },
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'Gauss-Legendre 3S': { 'variant': 'gauss-legendre_3s', **config['Res4Lyf'] },
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'Gauss-Legendre 4S': { 'variant': 'gauss-legendre_4s', **config['Res4Lyf'] },
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'Runge-Kutta 4/4': { **config['Res4Lyf'] },
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'Runge-Kutta 5/7': { **config['Res4Lyf'] },
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'Runge-Kutta 6/7': { **config['Res4Lyf'] },
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'Specialized-RK 3S': { 'variant': 'ssprk3_3s', **config['Res4Lyf'] },
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'Specialized-RK 4S': { 'variant': 'ssprk4_4s', **config['Res4Lyf'] },
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})
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samplers_data_diffusers = [
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SamplerData('Default', None, [], {}),
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