IP Adpater examples

server/pipelines/IPcompositionHyperSD15.py

@@ -0,0 +1,211 @@
+from diffusers import (
+    DiffusionPipeline,
+    TCDScheduler,
+)
+from compel import Compel
+import torch
+from transformers import CLIPVisionModelWithProjection
+from huggingface_hub import hf_hub_download
+
+try:
+    import intel_extension_for_pytorch as ipex  # type: ignore
+except:
+    pass
+
+from config import Args
+from pydantic import BaseModel, Field
+from PIL import Image
+
+model_id = "runwayml/stable-diffusion-v1-5"
+ip_adapter_model = "ostris/ip-composition-adapter"
+file_name = "ip_plus_composition_sd15.safetensors"
+
+default_prompt = "Portrait of The Terminator with , glare pose, detailed, intricate, full of colour, cinematic lighting, trending on artstation, 8k, hyperrealistic, focused, extreme details, unreal engine 5 cinematic, masterpiece"
+default_negative_prompt = "blurry, low quality, render, 3D, oversaturated"
+page_content = """
+<h1 class="text-3xl font-bold">Hyper-SD Unified + IP Adpater Composition</h1>
+<h3 class="text-xl font-bold">Image-to-Image ControlNet</h3>
+
+"""
+
+
+class Pipeline:
+    class Info(BaseModel):
+        name: str = "controlnet+SDXL+Turbo"
+        title: str = "SDXL Turbo + Controlnet"
+        description: str = "Generates an image from a text prompt"
+        input_mode: str = "image"
+        page_content: str = page_content
+
+    class InputParams(BaseModel):
+        prompt: str = Field(
+            default_prompt,
+            title="Prompt",
+            field="textarea",
+            id="prompt",
+        )
+        negative_prompt: str = Field(
+            default_negative_prompt,
+            title="Negative Prompt",
+            field="textarea",
+            id="negative_prompt",
+            hide=True,
+        )
+        seed: int = Field(
+            2159232, min=0, title="Seed", field="seed", hide=True, id="seed"
+        )
+        steps: int = Field(
+            2, min=1, max=15, title="Steps", field="range", hide=True, id="steps"
+        )
+        width: int = Field(
+            512, min=2, max=15, title="Width", disabled=True, hide=True, id="width"
+        )
+        height: int = Field(
+            512, min=2, max=15, title="Height", disabled=True, hide=True, id="height"
+        )
+        guidance_scale: float = Field(
+            0.0,
+            min=0,
+            max=10,
+            step=0.001,
+            title="Guidance Scale",
+            field="range",
+            hide=True,
+            id="guidance_scale",
+        )
+        ip_adapter_scale: float = Field(
+            0.8,
+            min=0.0,
+            max=1.0,
+            step=0.001,
+            title="IP Adapter Scale",
+            field="range",
+            hide=True,
+            id="ip_adapter_scale",
+        )
+        eta: float = Field(
+            1.0,
+            min=0,
+            max=1.0,
+            step=0.001,
+            title="Eta",
+            field="range",
+            hide=True,
+            id="eta",
+        )
+
+    def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype):
+        image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+            "h94/IP-Adapter",
+            subfolder="models/image_encoder",
+            torch_dtype=torch.float16,
+        ).to(device)
+
+        if args.safety_checker:
+            self.pipe = DiffusionPipeline.from_pretrained(
+                model_id,
+                # vae=vae,
+                torch_dtype=torch_dtype,
+                image_encoder=image_encoder,
+                variant="fp16",
+            )
+        else:
+            self.pipe = DiffusionPipeline.from_pretrained(
+                model_id,
+                safety_checker=None,
+                torch_dtype=torch_dtype,
+                image_encoder=image_encoder,
+                variant="fp16",
+            )
+
+        self.pipe.load_ip_adapter(
+            ip_adapter_model,
+            subfolder="",
+            weight_name=[file_name],
+            image_encoder_folder=None,
+        )
+
+        self.pipe.load_lora_weights(
+            hf_hub_download("ByteDance/Hyper-SD", "Hyper-SD15-1step-lora.safetensors")
+        )
+        self.pipe.fuse_lora()
+
+        self.pipe.scheduler = TCDScheduler.from_config(self.pipe.scheduler.config)
+        self.pipe.set_ip_adapter_scale([0.8])
+
+        #         if args.compile:
+        # pipe.unet = oneflow_compile(pipe.unet, options=compile_options)
+        # pipe.vae.decoder = oneflow_compile(pipe.vae.decoder, options=compile_options)
+
+        if args.sfast:
+            from sfast.compilers.stable_diffusion_pipeline_compiler import (
+                compile,
+                CompilationConfig,
+            )
+
+            config = CompilationConfig.Default()
+            # config.enable_xformers = True
+            config.enable_triton = True
+            config.enable_cuda_graph = True
+            # cofig.
+            self.pipe = compile(self.pipe, config=config)
+
+        self.pipe.set_progress_bar_config(disable=True)
+        self.pipe.to(device=device)
+        if device.type != "mps":
+            self.pipe.unet.to(memory_format=torch.channels_last)
+
+        if args.compel:
+            self.compel_proc = Compel(
+                tokenizer=self.pipe.tokenizer,
+                text_encoder=self.pipe.text_encoder,
+                truncate_long_prompts=False,
+            )
+
+        if args.torch_compile:
+            self.pipe.unet = torch.compile(
+                self.pipe.unet, mode="reduce-overhead", fullgraph=True
+            )
+            self.pipe.vae = torch.compile(
+                self.pipe.vae, mode="reduce-overhead", fullgraph=True
+            )
+            self.pipe(
+                prompt="warmup",
+                image=[Image.new("RGB", (768, 768))],
+            )
+
+    def predict(self, params: "Pipeline.InputParams") -> Image.Image:
+        generator = torch.manual_seed(params.seed)
+        self.pipe.set_ip_adapter_scale([params.ip_adapter_scale])
+
+        prompt_embeds = None
+        prompt = params.prompt
+        if hasattr(self, "compel_proc"):
+            prompt_embeds = self.compel_proc(prompt)
+            prompt = None
+
+        steps = params.steps
+
+        results = self.pipe(
+            prompt=prompt,
+            prompt_embeds=prompt_embeds,
+            generator=generator,
+            num_inference_steps=steps,
+            guidance_scale=params.guidance_scale,
+            width=params.width,
+            eta=params.eta,
+            height=params.height,
+            ip_adapter_image=[params.image],
+            output_type="pil",
+        )
+
+        nsfw_content_detected = (
+            results.nsfw_content_detected[0]
+            if "nsfw_content_detected" in results
+            else False
+        )
+        if nsfw_content_detected:
+            return None
+        result_image = results.images[0]
+
+        return result_image

server/pipelines/IPcompositionHyperSDXL.py

@@ -0,0 +1,227 @@
+from diffusers import (
+    StableDiffusionXLPipeline,
+    AutoencoderKL,
+    TCDScheduler,
+)
+from compel import Compel, ReturnedEmbeddingsType
+import torch
+from transformers import CLIPVisionModelWithProjection
+from huggingface_hub import hf_hub_download
+
+try:
+    import intel_extension_for_pytorch as ipex  # type: ignore
+except:
+    pass
+
+from config import Args
+from pydantic import BaseModel, Field
+from PIL import Image
+
+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+taesd_model = "madebyollin/taesdxl"
+ip_adapter_model = "ostris/ip-composition-adapter"
+file_name = "ip_plus_composition_sdxl.safetensors"
+
+default_prompt = "Portrait of The Terminator with , glare pose, detailed, intricate, full of colour, cinematic lighting, trending on artstation, 8k, hyperrealistic, focused, extreme details, unreal engine 5 cinematic, masterpiece"
+default_negative_prompt = "blurry, low quality, render, 3D, oversaturated"
+page_content = """
+<h1 class="text-3xl font-bold">Hyper-SDXL Unified + IP Adpater Composition</h1>
+<h3 class="text-xl font-bold">Image-to-Image ControlNet</h3>
+
+"""
+
+
+class Pipeline:
+    class Info(BaseModel):
+        name: str = "controlnet+SDXL+Turbo"
+        title: str = "SDXL Turbo + Controlnet"
+        description: str = "Generates an image from a text prompt"
+        input_mode: str = "image"
+        page_content: str = page_content
+
+    class InputParams(BaseModel):
+        prompt: str = Field(
+            default_prompt,
+            title="Prompt",
+            field="textarea",
+            id="prompt",
+        )
+        negative_prompt: str = Field(
+            default_negative_prompt,
+            title="Negative Prompt",
+            field="textarea",
+            id="negative_prompt",
+            hide=True,
+        )
+        seed: int = Field(
+            2159232, min=0, title="Seed", field="seed", hide=True, id="seed"
+        )
+        steps: int = Field(
+            2, min=1, max=15, title="Steps", field="range", hide=True, id="steps"
+        )
+        width: int = Field(
+            1024, min=2, max=15, title="Width", disabled=True, hide=True, id="width"
+        )
+        height: int = Field(
+            1024, min=2, max=15, title="Height", disabled=True, hide=True, id="height"
+        )
+        guidance_scale: float = Field(
+            0.0,
+            min=0,
+            max=10,
+            step=0.001,
+            title="Guidance Scale",
+            field="range",
+            hide=True,
+            id="guidance_scale",
+        )
+        ip_adapter_scale: float = Field(
+            0.8,
+            min=0.0,
+            max=1.0,
+            step=0.001,
+            title="IP Adapter Scale",
+            field="range",
+            hide=True,
+            id="ip_adapter_scale",
+        )
+        eta: float = Field(
+            1.0,
+            min=0,
+            max=1.0,
+            step=0.001,
+            title="Eta",
+            field="range",
+            hide=True,
+            id="eta",
+        )
+
+    def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype):
+        vae = AutoencoderKL.from_pretrained(
+            "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype
+        )
+        image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+            "h94/IP-Adapter",
+            subfolder="models/image_encoder",
+            torch_dtype=torch.float16,
+        ).to(device)
+
+        if args.safety_checker:
+            self.pipe = StableDiffusionXLPipeline.from_pretrained(
+                model_id,
+                # vae=vae,
+                torch_dtype=torch_dtype,
+                image_encoder=image_encoder,
+                variant="fp16",
+            )
+        else:
+            self.pipe = StableDiffusionXLPipeline.from_pretrained(
+                model_id,
+                safety_checker=None,
+                torch_dtype=torch_dtype,
+                vae=vae,
+                image_encoder=image_encoder,
+                variant="fp16",
+            )
+        self.pipe.load_ip_adapter(
+            ip_adapter_model,
+            subfolder="",
+            weight_name=[file_name],
+            image_encoder_folder=None,
+        )
+
+        self.pipe.load_lora_weights(
+            hf_hub_download("ByteDance/Hyper-SD", "Hyper-SDXL-1step-lora.safetensors")
+        )
+        self.pipe.fuse_lora()
+
+        self.pipe.scheduler = TCDScheduler.from_config(self.pipe.scheduler.config)
+        self.pipe.set_ip_adapter_scale([0.8])
+
+        if args.sfast:
+            from sfast.compilers.stable_diffusion_pipeline_compiler import (
+                compile,
+                CompilationConfig,
+            )
+
+            config = CompilationConfig.Default()
+            # config.enable_xformers = True
+            config.enable_triton = True
+            config.enable_cuda_graph = True
+            self.pipe = compile(self.pipe, config=config)
+
+        self.pipe.set_progress_bar_config(disable=True)
+        self.pipe.to(device=device)
+        if device.type != "mps":
+            self.pipe.unet.to(memory_format=torch.channels_last)
+
+        if args.compel:
+            self.pipe.compel_proc = Compel(
+                tokenizer=[self.pipe.tokenizer, self.pipe.tokenizer_2],
+                text_encoder=[self.pipe.text_encoder, self.pipe.text_encoder_2],
+                returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
+                requires_pooled=[False, True],
+            )
+
+        if args.torch_compile:
+            self.pipe.unet = torch.compile(
+                self.pipe.unet, mode="reduce-overhead", fullgraph=True
+            )
+            self.pipe.vae = torch.compile(
+                self.pipe.vae, mode="reduce-overhead", fullgraph=True
+            )
+            self.pipe(
+                prompt="warmup",
+                image=[Image.new("RGB", (768, 768))],
+            )
+
+    def predict(self, params: "Pipeline.InputParams") -> Image.Image:
+        generator = torch.manual_seed(params.seed)
+        self.pipe.set_ip_adapter_scale([params.ip_adapter_scale])
+
+        prompt = params.prompt
+        negative_prompt = params.negative_prompt
+        prompt_embeds = None
+        pooled_prompt_embeds = None
+        negative_prompt_embeds = None
+        negative_pooled_prompt_embeds = None
+        if hasattr(self.pipe, "compel_proc"):
+            _prompt_embeds, pooled_prompt_embeds = self.pipe.compel_proc(
+                [params.prompt, params.negative_prompt]
+            )
+            prompt = None
+            negative_prompt = None
+            prompt_embeds = _prompt_embeds[0:1]
+            pooled_prompt_embeds = pooled_prompt_embeds[0:1]
+            negative_prompt_embeds = _prompt_embeds[1:2]
+            negative_pooled_prompt_embeds = pooled_prompt_embeds[1:2]
+
+        steps = params.steps
+
+        results = self.pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            generator=generator,
+            num_inference_steps=steps,
+            guidance_scale=params.guidance_scale,
+            width=params.width,
+            eta=params.eta,
+            height=params.height,
+            ip_adapter_image=[params.image],
+            output_type="pil",
+        )
+
+        nsfw_content_detected = (
+            results.nsfw_content_detected[0]
+            if "nsfw_content_detected" in results
+            else False
+        )
+        if nsfw_content_detected:
+            return None
+        result_image = results.images[0]
+
+        return result_image

server/pipelines/controlnetHyperSDXL.py

@@ -20,7 +20,8 @@ from pydantic import BaseModel, Field
 from PIL import Image
 import math
 
-controlnet_model = "diffusers/controlnet-canny-sdxl-1.0"
+# controlnet_model = "diffusers/controlnet-canny-sdxl-1.0"
+controlnet_model = "xinsir/controlnet-canny-sdxl-1.0"
 model_id = "stabilityai/stable-diffusion-xl-base-1.0"
 taesd_model = "madebyollin/taesdxl"
 
@@ -192,7 +193,7 @@ class Pipeline:
             )
 
             config = CompilationConfig.Default()
-            config.enable_xformers = True
+            # config.enable_xformers = True
             config.enable_triton = True
             config.enable_cuda_graph = True
             self.pipe = compile(self.pipe, config=config)

server/requirements.txt

@@ -1,7 +1,7 @@
-diffusers==0.28.0
+diffusers==0.28.2
 transformers==4.41.1
 --extra-index-url https://download.pytorch.org/whl/cu121;
-torch==2.2.0
+torch==2.2.2
 fastapi==0.111.0
 uvicorn[standard]==0.30.0
 Pillow==10.3.0
@@ -12,8 +12,8 @@ peft==0.11.1
 xformers; sys_platform != 'darwin' or platform_machine != 'arm64'
 markdown2
 safetensors
-stable_fast @ https://github.com/chengzeyi/stable-fast/releases/download/v1.0.4/stable_fast-1.0.4+torch220cu121-cp310-cp310-manylinux2014_x86_64.whl ; sys_platform != 'darwin' or platform_machine != 'arm64'
-oneflow @ https://github.com/siliconflow/oneflow_releases/releases/download/community_cu121/oneflow-0.9.1.dev20240316+cu121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl ; sys_platform != 'darwin' or platform_machine != 'arm64'
+stable_fast @ https://github.com/chengzeyi/stable-fast/releases/download/v1.0.5/stable_fast-1.0.5+torch222cu121-cp310-cp310-manylinux2014_x86_64.whl ; sys_platform != 'darwin' or platform_machine != 'arm64'
+oneflow @ https://github.com/siliconflow/oneflow_releases/releases/download/community_cu121/oneflow-0.9.1.dev20240515+cu121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl ; sys_platform != 'darwin' or platform_machine != 'arm64'
 onediff @ git+https://github.com/siliconflow/onediff.git@main#egg=onediff ; sys_platform != 'darwin' or platform_machine != 'arm64'
 setuptools
 mpmath==1.3.0