get samplers from backend
This commit is contained in:
parent
a2fd5bb3ea
commit
f38be37f8c
@ -37,6 +37,7 @@ from lama_cleaner.schema import (
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SwitchModelRequest,
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InpaintRequest,
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RunPluginRequest,
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SDSampler,
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)
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from lama_cleaner.file_manager import FileManager
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@ -129,6 +130,7 @@ class Api:
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self.add_api_route("/api/v1/inputimage", self.api_input_image, methods=["GET"])
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self.add_api_route("/api/v1/inpaint", self.api_inpaint, methods=["POST"])
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self.add_api_route("/api/v1/run_plugin", self.api_run_plugin, methods=["POST"])
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self.add_api_route("/api/v1/samplers", self.api_samplers, methods=["GET"])
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self.app.mount("/", StaticFiles(directory=WEB_APP_DIR, html=True), name="assets")
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# fmt: on
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@ -156,6 +158,7 @@ class Api:
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controlnetMethod=self.model_manager.controlnet_method,
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disableModelSwitch=self.config.disable_model_switch,
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isDesktop=self.config.gui,
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samplers=self.api_samplers(),
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)
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def api_input_image(self) -> FileResponse:
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@ -237,6 +240,9 @@ class Api:
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media_type=f"image/{ext}",
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)
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def api_samplers(self) -> List[str]:
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return [member.value for member in SDSampler.__members__.values()]
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def launch(self):
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self.app.include_router(self.router)
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uvicorn.run(
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@ -2,6 +2,7 @@ import json
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import os
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from typing import List
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from huggingface_hub.constants import HF_HUB_CACHE
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from loguru import logger
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from pathlib import Path
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@ -101,13 +102,11 @@ def scan_inpaint_models(model_dir: Path) -> List[ModelInfo]:
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def scan_models() -> List[ModelInfo]:
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from diffusers.utils import DIFFUSERS_CACHE
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model_dir = os.getenv("XDG_CACHE_HOME", DEFAULT_MODEL_DIR)
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available_models = []
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available_models.extend(scan_inpaint_models(model_dir))
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available_models.extend(scan_single_file_diffusion_models(model_dir))
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cache_dir = Path(DIFFUSERS_CACHE)
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cache_dir = Path(HF_HUB_CACHE)
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# logger.info(f"Scanning diffusers models in {cache_dir}")
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diffusers_model_names = []
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for it in cache_dir.glob("**/*/model_index.json"):
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@ -35,9 +35,7 @@ class Kandinsky(DiffusionInpaintModel):
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mask: [H, W, 1] 255 means area to repaint
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return: BGR IMAGE
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"""
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scheduler_config = self.model.scheduler.config
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scheduler = get_scheduler(config.sd_sampler, scheduler_config)
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self.model.scheduler = scheduler
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self.set_scheduler(config)
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generator = torch.manual_seed(config.sd_seed)
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mask = mask.astype(np.float32) / 255
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@ -1,3 +1,4 @@
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import copy
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import gc
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import math
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import random
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@ -18,10 +19,13 @@ from diffusers import (
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DPMSolverMultistepScheduler,
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UniPCMultistepScheduler,
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LCMScheduler,
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DPMSolverSinglestepScheduler,
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KDPM2DiscreteScheduler,
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KDPM2AncestralDiscreteScheduler,
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HeunDiscreteScheduler,
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)
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from huggingface_hub.utils import RevisionNotFoundError
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from diffusers.configuration_utils import FrozenDict
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from loguru import logger
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from requests import HTTPError
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from lama_cleaner.schema import SDSampler
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from torch import conv2d, conv_transpose2d
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@ -930,22 +934,41 @@ def set_seed(seed: int):
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def get_scheduler(sd_sampler, scheduler_config):
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if sd_sampler == SDSampler.ddim:
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return DDIMScheduler.from_config(scheduler_config)
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elif sd_sampler == SDSampler.pndm:
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return PNDMScheduler.from_config(scheduler_config)
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elif sd_sampler == SDSampler.k_lms:
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return LMSDiscreteScheduler.from_config(scheduler_config)
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elif sd_sampler == SDSampler.k_euler:
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return EulerDiscreteScheduler.from_config(scheduler_config)
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elif sd_sampler == SDSampler.k_euler_a:
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return EulerAncestralDiscreteScheduler.from_config(scheduler_config)
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elif sd_sampler == SDSampler.dpm_plus_plus:
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return DPMSolverMultistepScheduler.from_config(scheduler_config)
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elif sd_sampler == SDSampler.uni_pc:
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return UniPCMultistepScheduler.from_config(scheduler_config)
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elif sd_sampler == SDSampler.lcm:
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return LCMScheduler.from_config(scheduler_config)
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# https://github.com/huggingface/diffusers/issues/4167
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keys_to_pop = ["use_karras_sigmas", "algorithm_type"]
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scheduler_config = dict(scheduler_config)
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for it in keys_to_pop:
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scheduler_config.pop(it, None)
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# fmt: off
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samplers = {
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SDSampler.dpm_plus_plus_2m: [DPMSolverMultistepScheduler],
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SDSampler.dpm_plus_plus_2m_karras: [DPMSolverMultistepScheduler, dict(use_karras_sigmas=True)],
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SDSampler.dpm_plus_plus_2m_sde: [DPMSolverMultistepScheduler, dict(algorithm_type="sde-dpmsolver++")],
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SDSampler.dpm_plus_plus_2m_sde_karras: [DPMSolverMultistepScheduler, dict(algorithm_type="sde-dpmsolver++", use_karras_sigmas=True)],
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SDSampler.dpm_plus_plus_sde: [DPMSolverSinglestepScheduler],
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SDSampler.dpm_plus_plus_sde_karras: [DPMSolverSinglestepScheduler, dict(use_karras_sigmas=True)],
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SDSampler.dpm2: [KDPM2DiscreteScheduler],
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SDSampler.dpm2_karras: [KDPM2DiscreteScheduler, dict(use_karras_sigmas=True)],
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SDSampler.dpm2_a: [KDPM2AncestralDiscreteScheduler],
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SDSampler.dpm2_a_karras: [KDPM2AncestralDiscreteScheduler, dict(use_karras_sigmas=True)],
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SDSampler.euler: [EulerDiscreteScheduler],
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SDSampler.euler_a: [EulerAncestralDiscreteScheduler],
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SDSampler.heun: [HeunDiscreteScheduler],
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SDSampler.lms: [LMSDiscreteScheduler],
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SDSampler.lms_karras: [LMSDiscreteScheduler, dict(use_karras_sigmas=True)],
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SDSampler.ddim: [DDIMScheduler],
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SDSampler.pndm: [PNDMScheduler],
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SDSampler.uni_pc: [UniPCMultistepScheduler],
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SDSampler.lcm: [LCMScheduler],
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}
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# fmt: on
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if sd_sampler in samplers:
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if len(samplers[sd_sampler]) == 2:
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scheduler_cls, kwargs = samplers[sd_sampler]
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else:
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scheduler_cls, kwargs = samplers[sd_sampler][0], {}
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return scheduler_cls.from_config(scheduler_config, **kwargs)
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else:
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raise ValueError(sd_sampler)
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@ -40,15 +40,26 @@ class LDMSampler(str, Enum):
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class SDSampler(str, Enum):
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ddim = "ddim"
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pndm = "pndm"
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k_lms = "k_lms"
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k_euler = "k_euler"
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k_euler_a = "k_euler_a"
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dpm_plus_plus = "dpm++"
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uni_pc = "uni_pc"
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dpm_plus_plus_2m = "DPM++ 2M"
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dpm_plus_plus_2m_karras = "DPM++ 2M Karras"
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dpm_plus_plus_2m_sde = "DPM++ 2M SDE"
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dpm_plus_plus_2m_sde_karras = "DPM++ 2M SDE Karras"
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dpm_plus_plus_sde = "DPM++ SDE"
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dpm_plus_plus_sde_karras = "DPM++ SDE Karras"
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dpm2 = "DPM2"
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dpm2_karras = "DPM2 Karras"
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dpm2_a = "DPM2 a"
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dpm2_a_karras = "DPM2 a Karras"
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euler = "Euler"
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euler_a = "Euler a"
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heun = "Heun"
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lms = "LMS"
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lms_karras = "LMS Karras"
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lcm = "lcm"
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ddim = "DDIM"
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pndm = "PNDM"
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uni_pc = "UniPC"
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lcm = "LCM"
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class FREEUConfig(BaseModel):
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@ -143,7 +154,7 @@ class InpaintRequest(BaseModel):
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le=1.0,
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)
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sd_mask_blur: int = Field(
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33,
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11,
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description="Blur the edge of mask area. The higher the number the smoother blend with the original image",
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)
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sd_strength: float = Field(
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@ -268,6 +279,7 @@ class ServerConfigResponse(BaseModel):
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controlnetMethod: Optional[str]
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disableModelSwitch: bool
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isDesktop: bool
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samplers: List[str]
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class SwitchModelRequest(BaseModel):
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@ -49,7 +49,7 @@ def test_outpainting(name, device, rect):
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extender_width=rect[2],
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extender_height=rect[3],
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sd_guidance_scale=8.0,
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sd_sampler=SDSampler.dpm_plus_plus,
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sd_sampler=SDSampler.dpm_plus_plus_2m,
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)
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assert_equal(
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@ -92,7 +92,7 @@ def test_kandinsky_outpainting(name, device, rect):
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extender_width=rect[2],
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extender_height=rect[3],
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sd_guidance_scale=7,
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sd_sampler=SDSampler.dpm_plus_plus,
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sd_sampler=SDSampler.dpm_plus_plus_2m,
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)
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assert_equal(
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@ -136,7 +136,7 @@ def test_powerpaint_outpainting(name, device, rect):
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extender_width=rect[2],
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extender_height=rect[3],
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sd_guidance_scale=8.0,
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sd_sampler=SDSampler.dpm_plus_plus,
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sd_sampler=SDSampler.dpm_plus_plus_2m,
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powerpaint_task="outpainting",
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)
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@ -1,5 +1,7 @@
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import os
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from loguru import logger
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from lama_cleaner.tests.utils import check_device, get_config, assert_equal
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
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@ -17,21 +19,7 @@ save_dir.mkdir(exist_ok=True, parents=True)
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@pytest.mark.parametrize("device", ["cuda", "mps"])
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@pytest.mark.parametrize(
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"sampler",
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[
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SDSampler.ddim,
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SDSampler.pndm,
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SDSampler.k_lms,
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SDSampler.k_euler,
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SDSampler.k_euler_a,
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SDSampler.lcm,
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],
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)
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def test_runway_sd_1_5_all_samplers(
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device,
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sampler,
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):
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def test_runway_sd_1_5_all_samplers(device):
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sd_steps = check_device(device)
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model = ModelManager(
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name="runwayml/stable-diffusion-inpainting",
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@ -39,22 +27,37 @@ def test_runway_sd_1_5_all_samplers(
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disable_nsfw=True,
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sd_cpu_textencoder=False,
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)
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cfg = get_config(
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strategy=HDStrategy.ORIGINAL,
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prompt="a fox sitting on a bench",
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sd_steps=sd_steps,
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)
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cfg.sd_sampler = sampler
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name = f"device_{device}_{sampler}"
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all_samplers = [member.value for member in SDSampler.__members__.values()]
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print(all_samplers)
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for sampler in all_samplers:
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print(f"Testing sampler {sampler}")
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if (
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sampler
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in [SDSampler.dpm2_karras, SDSampler.dpm2_a_karras, SDSampler.lms_karras]
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and device == "mps"
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):
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# diffusers 0.25.0 still has bug on these sampler on mps, wait main branch released to fix it
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logger.warning(
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"skip dpm2_karras on mps, diffusers does not support it on mps. TypeError: Cannot convert a MPS Tensor to float64 dtype as the MPS framework doesn't support float64. Please use float32 instead."
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)
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continue
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cfg = get_config(
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strategy=HDStrategy.ORIGINAL,
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prompt="a fox sitting on a bench",
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sd_steps=sd_steps,
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sd_sampler=sampler,
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)
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assert_equal(
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model,
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cfg,
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f"runway_sd_{name}.png",
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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)
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name = f"device_{device}_{sampler}"
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assert_equal(
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model,
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cfg,
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f"runway_sd_{name}.png",
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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)
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@pytest.mark.parametrize("device", ["cuda", "mps", "cpu"])
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@ -171,7 +174,7 @@ def test_runway_norm_sd_model(device, strategy, sampler):
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@pytest.mark.parametrize("device", ["cuda"])
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@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
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@pytest.mark.parametrize("sampler", [SDSampler.k_euler_a])
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@pytest.mark.parametrize("sampler", [SDSampler.dpm_plus_plus_2m])
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def test_runway_sd_1_5_cpu_offload(device, strategy, sampler):
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sd_steps = check_device(device)
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model = ModelManager(
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@ -3,7 +3,9 @@ import cv2
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import pytest
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import torch
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from lama_cleaner.helper import encode_pil_to_base64
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from lama_cleaner.schema import LDMSampler, HDStrategy, InpaintRequest, SDSampler
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from PIL import Image
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current_dir = Path(__file__).parent.absolute().resolve()
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save_dir = current_dir / "result"
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@ -21,7 +23,7 @@ def check_device(device: str) -> int:
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def assert_equal(
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model,
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config,
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config: InpaintRequest,
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gt_name,
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fx: float = 1,
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fy: float = 1,
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@ -29,6 +31,8 @@ def assert_equal(
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mask_p=current_dir / "mask.png",
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):
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img, mask = get_data(fx=fx, fy=fy, img_p=img_p, mask_p=mask_p)
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config.image = encode_pil_to_base64(Image.fromarray(img), 95, {})[0]
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config.mask = encode_pil_to_base64(Image.fromarray(mask), 95, {})[0]
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print(f"Input image shape: {img.shape}")
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res = model(img, mask, config)
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ok = cv2.imwrite(
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@ -72,4 +76,4 @@ def get_config(**kwargs):
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hd_strategy_resize_limit=200,
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)
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data.update(**kwargs)
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return InpaintRequest(**data)
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return InpaintRequest(image="", mask="", **data)
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@ -1,20 +1,18 @@
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torch>=2.0.0
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typer
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opencv-python
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diffusers==0.25.0
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transformers==4.34.1
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safetensors
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controlnet-aux==0.0.3
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fastapi==0.108.0
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python-multipart
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simple-websocket
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flask_cors
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flaskwebgui==0.3.5
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typer
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pydantic
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rich
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loguru
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yacs
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diffusers==0.23.0
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transformers==4.34.1
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gradio
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piexif==1.1.3
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safetensors
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omegaconf
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controlnet-aux==0.0.3
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werkzeug==2.2.2
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omegaconf
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@ -5,7 +5,7 @@ from typing import Dict, List, Union
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import torch
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from diffusers.utils import is_safetensors_available
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from huggingface_hub.constants import HF_HUB_CACHE
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if is_safetensors_available():
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import safetensors.torch
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@ -16,7 +16,6 @@ from diffusers import DiffusionPipeline, __version__
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from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
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from diffusers.utils import (
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CONFIG_NAME,
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DIFFUSERS_CACHE,
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ONNX_WEIGHTS_NAME,
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WEIGHTS_NAME,
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)
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@ -96,7 +95,7 @@ class CheckpointMergerPipeline(DiffusionPipeline):
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"""
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# Default kwargs from DiffusionPipeline
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cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
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cache_dir = kwargs.pop("cache_dir", HF_HUB_CACHE)
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resume_download = kwargs.pop("resume_download", False)
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force_download = kwargs.pop("force_download", False)
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proxies = kwargs.pop("proxies", None)
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@ -246,7 +245,6 @@ class CheckpointMergerPipeline(DiffusionPipeline):
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print(f"Skipping {attr}: not present in 2nd or 3d model")
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continue
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try:
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module = getattr(final_pipe, attr)
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if isinstance(
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@ -267,7 +265,7 @@ class CheckpointMergerPipeline(DiffusionPipeline):
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else torch.load(checkpoint_path_1, map_location="cpu")
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)
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if attr in ['vae', 'text_encoder']:
|
||||
if attr in ["vae", "text_encoder"]:
|
||||
print(f"Direct use theta1 {attr}: {checkpoint_path_1}")
|
||||
update_theta_0(theta_1)
|
||||
del theta_1
|
||||
@ -348,7 +346,7 @@ pipe = CheckpointMergerPipeline.from_pretrained("runwayml/stable-diffusion-inpai
|
||||
merged_pipe = pipe.merge(
|
||||
[
|
||||
"runwayml/stable-diffusion-inpainting",
|
||||
#"SG161222/Realistic_Vision_V1.4",
|
||||
# "SG161222/Realistic_Vision_V1.4",
|
||||
"dreamlike-art/dreamlike-diffusion-1.0",
|
||||
"runwayml/stable-diffusion-v1-5",
|
||||
],
|
||||
@ -358,4 +356,6 @@ merged_pipe = pipe.merge(
|
||||
)
|
||||
|
||||
merged_pipe = merged_pipe.to(torch.float16)
|
||||
merged_pipe.save_pretrained("dreamlike-diffusion-1.0-inpainting", safe_serialization=True)
|
||||
merged_pipe.save_pretrained(
|
||||
"dreamlike-diffusion-1.0-inpainting", safe_serialization=True
|
||||
)
|
||||
|
@ -11,7 +11,7 @@ import {
|
||||
SelectValue,
|
||||
} from "../ui/select"
|
||||
import { Textarea } from "../ui/textarea"
|
||||
import { ExtenderDirection, PowerPaintTask, SDSampler } from "@/lib/types"
|
||||
import { ExtenderDirection, PowerPaintTask } from "@/lib/types"
|
||||
import { Separator } from "../ui/separator"
|
||||
import { Button, ImageUploadButton } from "../ui/button"
|
||||
import { Slider } from "../ui/slider"
|
||||
@ -42,6 +42,7 @@ const ExtenderButton = ({
|
||||
|
||||
const DiffusionOptions = () => {
|
||||
const [
|
||||
samplers,
|
||||
settings,
|
||||
paintByExampleFile,
|
||||
isProcessing,
|
||||
@ -51,6 +52,7 @@ const DiffusionOptions = () => {
|
||||
updateExtenderByBuiltIn,
|
||||
updateExtenderDirection,
|
||||
] = useStore((state) => [
|
||||
state.serverConfig.samplers,
|
||||
state.settings,
|
||||
state.paintByExampleFile,
|
||||
state.getIsProcessing(),
|
||||
@ -652,16 +654,16 @@ const DiffusionOptions = () => {
|
||||
<Select
|
||||
defaultValue={settings.sdSampler}
|
||||
value={settings.sdSampler}
|
||||
onValueChange={(value: SDSampler) => {
|
||||
onValueChange={(value) => {
|
||||
updateSettings({ sdSampler: value })
|
||||
}}
|
||||
>
|
||||
<SelectTrigger className="w-[120px]">
|
||||
<SelectTrigger className="w-[180px]">
|
||||
<SelectValue placeholder="Select sampler" />
|
||||
</SelectTrigger>
|
||||
<SelectContent align="end">
|
||||
<SelectGroup>
|
||||
{Object.values(SDSampler).map((sampler) => (
|
||||
{samplers.map((sampler) => (
|
||||
<SelectItem key={sampler} value={sampler}>
|
||||
{sampler}
|
||||
</SelectItem>
|
||||
|
@ -194,3 +194,8 @@ export async function getGenInfo(file: File): Promise<GenInfo> {
|
||||
const res = await api.post(`/gen-info`, fd)
|
||||
return res.data
|
||||
}
|
||||
|
||||
export async function getSamplers(): Promise<string[]> {
|
||||
const res = await api.post("/samplers")
|
||||
return res.data
|
||||
}
|
||||
|
@ -14,7 +14,6 @@ import {
|
||||
PluginParams,
|
||||
Point,
|
||||
PowerPaintTask,
|
||||
SDSampler,
|
||||
ServerConfig,
|
||||
Size,
|
||||
SortBy,
|
||||
@ -85,7 +84,7 @@ export type Settings = {
|
||||
sdStrength: number
|
||||
sdSteps: number
|
||||
sdGuidanceScale: number
|
||||
sdSampler: SDSampler
|
||||
sdSampler: string
|
||||
sdMatchHistograms: boolean
|
||||
sdScale: number
|
||||
|
||||
@ -274,6 +273,7 @@ const defaultValues: AppState = {
|
||||
controlnetMethod: "lllyasviel/control_v11p_sd15_canny",
|
||||
disableModelSwitch: false,
|
||||
isDesktop: false,
|
||||
samplers: ["DPM++ 2M"],
|
||||
},
|
||||
settings: {
|
||||
model: {
|
||||
@ -310,7 +310,7 @@ const defaultValues: AppState = {
|
||||
sdStrength: 1.0,
|
||||
sdSteps: 50,
|
||||
sdGuidanceScale: 7.5,
|
||||
sdSampler: SDSampler.uni_pc,
|
||||
sdSampler: "DPM++ 2M",
|
||||
sdMatchHistograms: false,
|
||||
sdScale: 100,
|
||||
p2pImageGuidanceScale: 1.5,
|
||||
|
@ -14,6 +14,7 @@ export interface ServerConfig {
|
||||
controlnetMethod: string
|
||||
disableModelSwitch: boolean
|
||||
isDesktop: boolean
|
||||
samplers: string[]
|
||||
}
|
||||
|
||||
export interface GenInfo {
|
||||
@ -82,17 +83,6 @@ export interface Rect {
|
||||
height: number
|
||||
}
|
||||
|
||||
export enum SDSampler {
|
||||
ddim = "ddim",
|
||||
pndm = "pndm",
|
||||
klms = "k_lms",
|
||||
kEuler = "k_euler",
|
||||
kEulerA = "k_euler_a",
|
||||
dpmPlusPlus = "dpm++",
|
||||
uni_pc = "uni_pc",
|
||||
// lcm = "lcm",
|
||||
}
|
||||
|
||||
export interface FreeuConfig {
|
||||
s1: number
|
||||
s2: number
|
||||
|
Loading…
Reference in New Issue
Block a user