wehub-resource-sync
76 lines
2.5 KiB
Python
Executable File
76 lines
2.5 KiB
Python
Executable File
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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import torch
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from diffusion.model import gaussian_diffusion as gd
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from diffusion.model.dpm_solver import DPM_Solver, NoiseScheduleFlow, NoiseScheduleVP, model_wrapper
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def DPMS(
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model,
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condition,
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uncondition,
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cfg_scale,
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pag_scale=1.0,
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pag_applied_layers=None,
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model_type="noise", # or "x_start" or "v" or "score", "flow"
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noise_schedule="linear",
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guidance_type="classifier-free",
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model_kwargs=None,
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diffusion_steps=1000,
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schedule="VP",
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interval_guidance=None,
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condition_as_list=False, # for wan text encoder, set to true
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apg=None,
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**kwargs,
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):
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if pag_applied_layers is None:
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pag_applied_layers = []
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if model_kwargs is None:
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model_kwargs = {}
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if interval_guidance is None:
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interval_guidance = [0, 1.0]
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betas = torch.tensor(gd.get_named_beta_schedule(noise_schedule, diffusion_steps))
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## 1. Define the noise schedule.
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if schedule == "VP":
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noise_schedule = NoiseScheduleVP(schedule="discrete", betas=betas)
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elif schedule == "FLOW":
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noise_schedule = NoiseScheduleFlow(schedule="discrete_flow")
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## 2. Convert your discrete-time `model` to the continuous-time
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## noise prediction model. Here is an example for a diffusion model
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## `model` with the noise prediction type ("noise") .
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model_fn = model_wrapper(
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model,
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noise_schedule,
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model_type=model_type,
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model_kwargs=model_kwargs,
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guidance_type=guidance_type,
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pag_scale=pag_scale,
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pag_applied_layers=pag_applied_layers,
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condition=condition,
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unconditional_condition=uncondition,
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guidance_scale=cfg_scale,
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interval_guidance=interval_guidance,
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condition_as_list=condition_as_list,
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apg=apg,
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**kwargs,
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)
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## 3. Define dpm-solver and sample by multistep DPM-Solver.
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return DPM_Solver(model_fn, noise_schedule, algorithm_type="dpmsolver++")
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