get samplers from backend

lama_cleaner/api.py

@@ -37,6 +37,7 @@ from lama_cleaner.schema import (
     SwitchModelRequest,
     InpaintRequest,
     RunPluginRequest,
+    SDSampler,
 )
 from lama_cleaner.file_manager import FileManager
 
@@ -129,6 +130,7 @@ class Api:
         self.add_api_route("/api/v1/inputimage", self.api_input_image, methods=["GET"])
         self.add_api_route("/api/v1/inpaint", self.api_inpaint, methods=["POST"])
         self.add_api_route("/api/v1/run_plugin", self.api_run_plugin, methods=["POST"])
+        self.add_api_route("/api/v1/samplers", self.api_samplers, methods=["GET"])
         self.app.mount("/", StaticFiles(directory=WEB_APP_DIR, html=True), name="assets")
         # fmt: on
 
@@ -156,6 +158,7 @@ class Api:
             controlnetMethod=self.model_manager.controlnet_method,
             disableModelSwitch=self.config.disable_model_switch,
             isDesktop=self.config.gui,
+            samplers=self.api_samplers(),
         )
 
     def api_input_image(self) -> FileResponse:
@@ -237,6 +240,9 @@ class Api:
             media_type=f"image/{ext}",
         )
 
+    def api_samplers(self) -> List[str]:
+        return [member.value for member in SDSampler.__members__.values()]
+
     def launch(self):
         self.app.include_router(self.router)
         uvicorn.run(

lama_cleaner/download.py

@@ -2,6 +2,7 @@ import json
 import os
 from typing import List
 
+from huggingface_hub.constants import HF_HUB_CACHE
 from loguru import logger
 from pathlib import Path
 
@@ -101,13 +102,11 @@ def scan_inpaint_models(model_dir: Path) -> List[ModelInfo]:
 
 
 def scan_models() -> List[ModelInfo]:
-    from diffusers.utils import DIFFUSERS_CACHE
-
     model_dir = os.getenv("XDG_CACHE_HOME", DEFAULT_MODEL_DIR)
     available_models = []
     available_models.extend(scan_inpaint_models(model_dir))
     available_models.extend(scan_single_file_diffusion_models(model_dir))
-    cache_dir = Path(DIFFUSERS_CACHE)
+    cache_dir = Path(HF_HUB_CACHE)
     # logger.info(f"Scanning diffusers models in {cache_dir}")
     diffusers_model_names = []
     for it in cache_dir.glob("**/*/model_index.json"):

lama_cleaner/model/kandinsky.py

@@ -35,9 +35,7 @@ class Kandinsky(DiffusionInpaintModel):
         mask: [H, W, 1] 255 means area to repaint
         return: BGR IMAGE
         """
-        scheduler_config = self.model.scheduler.config
+        self.set_scheduler(config)
-        scheduler = get_scheduler(config.sd_sampler, scheduler_config)
-        self.model.scheduler = scheduler
 
         generator = torch.manual_seed(config.sd_seed)
         mask = mask.astype(np.float32) / 255

lama_cleaner/model/utils.py

@@ -1,3 +1,4 @@
+import copy
 import gc
 import math
 import random
@@ -18,10 +19,13 @@ from diffusers import (
     DPMSolverMultistepScheduler,
     UniPCMultistepScheduler,
     LCMScheduler,
+    DPMSolverSinglestepScheduler,
+    KDPM2DiscreteScheduler,
+    KDPM2AncestralDiscreteScheduler,
+    HeunDiscreteScheduler,
 )
-from huggingface_hub.utils import RevisionNotFoundError
+from diffusers.configuration_utils import FrozenDict
 from loguru import logger
-from requests import HTTPError
 
 from lama_cleaner.schema import SDSampler
 from torch import conv2d, conv_transpose2d
@@ -930,22 +934,41 @@ def set_seed(seed: int):
 
 
 def get_scheduler(sd_sampler, scheduler_config):
-    if sd_sampler == SDSampler.ddim:
+    # https://github.com/huggingface/diffusers/issues/4167
-        return DDIMScheduler.from_config(scheduler_config)
+    keys_to_pop = ["use_karras_sigmas", "algorithm_type"]
-    elif sd_sampler == SDSampler.pndm:
+    scheduler_config = dict(scheduler_config)
-        return PNDMScheduler.from_config(scheduler_config)
+    for it in keys_to_pop:
-    elif sd_sampler == SDSampler.k_lms:
+        scheduler_config.pop(it, None)
-        return LMSDiscreteScheduler.from_config(scheduler_config)
+
-    elif sd_sampler == SDSampler.k_euler:
+    # fmt: off
-        return EulerDiscreteScheduler.from_config(scheduler_config)
+    samplers = {
-    elif sd_sampler == SDSampler.k_euler_a:
+        SDSampler.dpm_plus_plus_2m: [DPMSolverMultistepScheduler],
-        return EulerAncestralDiscreteScheduler.from_config(scheduler_config)
+        SDSampler.dpm_plus_plus_2m_karras: [DPMSolverMultistepScheduler, dict(use_karras_sigmas=True)],
-    elif sd_sampler == SDSampler.dpm_plus_plus:
+        SDSampler.dpm_plus_plus_2m_sde: [DPMSolverMultistepScheduler, dict(algorithm_type="sde-dpmsolver++")],
-        return DPMSolverMultistepScheduler.from_config(scheduler_config)
+        SDSampler.dpm_plus_plus_2m_sde_karras: [DPMSolverMultistepScheduler, dict(algorithm_type="sde-dpmsolver++", use_karras_sigmas=True)],
-    elif sd_sampler == SDSampler.uni_pc:
+        SDSampler.dpm_plus_plus_sde: [DPMSolverSinglestepScheduler],
-        return UniPCMultistepScheduler.from_config(scheduler_config)
+        SDSampler.dpm_plus_plus_sde_karras: [DPMSolverSinglestepScheduler, dict(use_karras_sigmas=True)],
-    elif sd_sampler == SDSampler.lcm:
+        SDSampler.dpm2: [KDPM2DiscreteScheduler],
-        return LCMScheduler.from_config(scheduler_config)
+        SDSampler.dpm2_karras: [KDPM2DiscreteScheduler, dict(use_karras_sigmas=True)],
+        SDSampler.dpm2_a: [KDPM2AncestralDiscreteScheduler],
+        SDSampler.dpm2_a_karras: [KDPM2AncestralDiscreteScheduler, dict(use_karras_sigmas=True)],
+        SDSampler.euler: [EulerDiscreteScheduler],
+        SDSampler.euler_a: [EulerAncestralDiscreteScheduler],
+        SDSampler.heun: [HeunDiscreteScheduler],
+        SDSampler.lms: [LMSDiscreteScheduler],
+        SDSampler.lms_karras: [LMSDiscreteScheduler, dict(use_karras_sigmas=True)],
+        SDSampler.ddim: [DDIMScheduler],
+        SDSampler.pndm: [PNDMScheduler],
+        SDSampler.uni_pc: [UniPCMultistepScheduler],
+        SDSampler.lcm: [LCMScheduler],
+    }
+    # fmt: on
+    if sd_sampler in samplers:
+        if len(samplers[sd_sampler]) == 2:
+            scheduler_cls, kwargs = samplers[sd_sampler]
+        else:
+            scheduler_cls, kwargs = samplers[sd_sampler][0], {}
+        return scheduler_cls.from_config(scheduler_config, **kwargs)
     else:
         raise ValueError(sd_sampler)
 

lama_cleaner/schema.py

@@ -40,15 +40,26 @@ class LDMSampler(str, Enum):
 
 
 class SDSampler(str, Enum):
-    ddim = "ddim"
+    dpm_plus_plus_2m = "DPM++ 2M"
-    pndm = "pndm"
+    dpm_plus_plus_2m_karras = "DPM++ 2M Karras"
-    k_lms = "k_lms"
+    dpm_plus_plus_2m_sde = "DPM++ 2M SDE"
-    k_euler = "k_euler"
+    dpm_plus_plus_2m_sde_karras = "DPM++ 2M SDE Karras"
-    k_euler_a = "k_euler_a"
+    dpm_plus_plus_sde = "DPM++ SDE"
-    dpm_plus_plus = "dpm++"
+    dpm_plus_plus_sde_karras = "DPM++ SDE Karras"
-    uni_pc = "uni_pc"
+    dpm2 = "DPM2"
+    dpm2_karras = "DPM2 Karras"
+    dpm2_a = "DPM2 a"
+    dpm2_a_karras = "DPM2 a Karras"
+    euler = "Euler"
+    euler_a = "Euler a"
+    heun = "Heun"
+    lms = "LMS"
+    lms_karras = "LMS Karras"
 
-    lcm = "lcm"
+    ddim = "DDIM"
+    pndm = "PNDM"
+    uni_pc = "UniPC"
+    lcm = "LCM"
 
 
 class FREEUConfig(BaseModel):
@@ -143,7 +154,7 @@ class InpaintRequest(BaseModel):
         le=1.0,
     )
     sd_mask_blur: int = Field(
-        33,
+        11,
         description="Blur the edge of mask area. The higher the number the smoother blend with the original image",
     )
     sd_strength: float = Field(
@@ -268,6 +279,7 @@ class ServerConfigResponse(BaseModel):
     controlnetMethod: Optional[str]
     disableModelSwitch: bool
     isDesktop: bool
+    samplers: List[str]
 
 
 class SwitchModelRequest(BaseModel):

lama_cleaner/tests/test_outpainting.py

@@ -49,7 +49,7 @@ def test_outpainting(name, device, rect):
         extender_width=rect[2],
         extender_height=rect[3],
         sd_guidance_scale=8.0,
-        sd_sampler=SDSampler.dpm_plus_plus,
+        sd_sampler=SDSampler.dpm_plus_plus_2m,
     )
 
     assert_equal(
@@ -92,7 +92,7 @@ def test_kandinsky_outpainting(name, device, rect):
         extender_width=rect[2],
         extender_height=rect[3],
         sd_guidance_scale=7,
-        sd_sampler=SDSampler.dpm_plus_plus,
+        sd_sampler=SDSampler.dpm_plus_plus_2m,
     )
 
     assert_equal(
@@ -136,7 +136,7 @@ def test_powerpaint_outpainting(name, device, rect):
         extender_width=rect[2],
         extender_height=rect[3],
         sd_guidance_scale=8.0,
-        sd_sampler=SDSampler.dpm_plus_plus,
+        sd_sampler=SDSampler.dpm_plus_plus_2m,
         powerpaint_task="outpainting",
     )
 

lama_cleaner/tests/test_sd_model.py

@@ -1,5 +1,7 @@
 import os
 
+from loguru import logger
+
 from lama_cleaner.tests.utils import check_device, get_config, assert_equal
 
 os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
@@ -17,21 +19,7 @@ save_dir.mkdir(exist_ok=True, parents=True)
 
 
 @pytest.mark.parametrize("device", ["cuda", "mps"])
-@pytest.mark.parametrize(
+def test_runway_sd_1_5_all_samplers(device):
-    "sampler",
-    [
-        SDSampler.ddim,
-        SDSampler.pndm,
-        SDSampler.k_lms,
-        SDSampler.k_euler,
-        SDSampler.k_euler_a,
-        SDSampler.lcm,
-    ],
-)
-def test_runway_sd_1_5_all_samplers(
-    device,
-    sampler,
-):
     sd_steps = check_device(device)
     model = ModelManager(
         name="runwayml/stable-diffusion-inpainting",
@@ -39,12 +27,27 @@ def test_runway_sd_1_5_all_samplers(
         disable_nsfw=True,
         sd_cpu_textencoder=False,
     )
+
+    all_samplers = [member.value for member in SDSampler.__members__.values()]
+    print(all_samplers)
+    for sampler in all_samplers:
+        print(f"Testing sampler {sampler}")
+        if (
+            sampler
+            in [SDSampler.dpm2_karras, SDSampler.dpm2_a_karras, SDSampler.lms_karras]
+            and device == "mps"
+        ):
+            # diffusers 0.25.0 still has bug on these sampler on mps, wait main branch released to fix it
+            logger.warning(
+                "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."
+            )
+            continue
         cfg = get_config(
             strategy=HDStrategy.ORIGINAL,
             prompt="a fox sitting on a bench",
             sd_steps=sd_steps,
+            sd_sampler=sampler,
         )
-    cfg.sd_sampler = sampler
 
         name = f"device_{device}_{sampler}"
 
@@ -171,7 +174,7 @@ def test_runway_norm_sd_model(device, strategy, sampler):
 
 @pytest.mark.parametrize("device", ["cuda"])
 @pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
-@pytest.mark.parametrize("sampler", [SDSampler.k_euler_a])
+@pytest.mark.parametrize("sampler", [SDSampler.dpm_plus_plus_2m])
 def test_runway_sd_1_5_cpu_offload(device, strategy, sampler):
     sd_steps = check_device(device)
     model = ModelManager(

lama_cleaner/tests/utils.py

@@ -3,7 +3,9 @@ import cv2
 import pytest
 import torch
 
+from lama_cleaner.helper import encode_pil_to_base64
 from lama_cleaner.schema import LDMSampler, HDStrategy, InpaintRequest, SDSampler
+from PIL import Image
 
 current_dir = Path(__file__).parent.absolute().resolve()
 save_dir = current_dir / "result"
@@ -21,7 +23,7 @@ def check_device(device: str) -> int:
 
 def assert_equal(
     model,
-    config,
+    config: InpaintRequest,
     gt_name,
     fx: float = 1,
     fy: float = 1,
@@ -29,6 +31,8 @@ def assert_equal(
     mask_p=current_dir / "mask.png",
 ):
     img, mask = get_data(fx=fx, fy=fy, img_p=img_p, mask_p=mask_p)
+    config.image = encode_pil_to_base64(Image.fromarray(img), 95, {})[0]
+    config.mask = encode_pil_to_base64(Image.fromarray(mask), 95, {})[0]
     print(f"Input image shape: {img.shape}")
     res = model(img, mask, config)
     ok = cv2.imwrite(
@@ -72,4 +76,4 @@ def get_config(**kwargs):
         hd_strategy_resize_limit=200,
     )
     data.update(**kwargs)
-    return InpaintRequest(**data)
+    return InpaintRequest(image="", mask="", **data)

requirements.txt

@@ -1,20 +1,18 @@
 torch>=2.0.0
-typer
 opencv-python
+diffusers==0.25.0
+transformers==4.34.1
+safetensors
+controlnet-aux==0.0.3
 fastapi==0.108.0
 python-multipart
 simple-websocket
-flask_cors
 flaskwebgui==0.3.5
+typer
 pydantic
 rich
 loguru
 yacs
-diffusers==0.23.0
-transformers==4.34.1
 gradio
 piexif==1.1.3
-safetensors
 omegaconf
-controlnet-aux==0.0.3
-werkzeug==2.2.2

scripts/tool.py

@@ -5,7 +5,7 @@ from typing import Dict, List, Union
 import torch
 
 from diffusers.utils import is_safetensors_available
-
+from huggingface_hub.constants import HF_HUB_CACHE
 
 if is_safetensors_available():
     import safetensors.torch
@@ -16,7 +16,6 @@ from diffusers import DiffusionPipeline, __version__
 from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
 from diffusers.utils import (
     CONFIG_NAME,
-    DIFFUSERS_CACHE,
     ONNX_WEIGHTS_NAME,
     WEIGHTS_NAME,
 )
@@ -96,7 +95,7 @@ class CheckpointMergerPipeline(DiffusionPipeline):
 
         """
         # Default kwargs from DiffusionPipeline
-        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
+        cache_dir = kwargs.pop("cache_dir", HF_HUB_CACHE)
         resume_download = kwargs.pop("resume_download", False)
         force_download = kwargs.pop("force_download", False)
         proxies = kwargs.pop("proxies", None)
@@ -246,7 +245,6 @@ class CheckpointMergerPipeline(DiffusionPipeline):
                     print(f"Skipping {attr}: not present in 2nd or 3d model")
                     continue
 
-
                 try:
                     module = getattr(final_pipe, attr)
                     if isinstance(
@@ -267,7 +265,7 @@ class CheckpointMergerPipeline(DiffusionPipeline):
                         else torch.load(checkpoint_path_1, map_location="cpu")
                     )
 
-                    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
@@ -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
+)

web_app/src/components/SidePanel/DiffusionOptions.tsx

@@ -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>

web_app/src/lib/api.ts

@@ -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
+}

web_app/src/lib/states.ts

@@ -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,

web_app/src/lib/types.ts

@@ -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