Update some dependencies. Repair some codes.
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hodanov
parent
4fe518038d
commit
e1639039d7
@ -5,7 +5,7 @@ RUN apt-get update \
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&& apt-get autoremove -y \
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&& apt-get clean -y \
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&& rm -rf /var/lib/apt/lists/* \
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&& pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu117 --no-cache-dir \
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&& pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu121 --no-cache-dir \
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&& mkdir -p /vol/cache/esrgan \
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&& wget --progress=dot:giga https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P /vol/cache/esrgan \
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&& wget --progress=dot:giga https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth -P /vol/cache/esrgan \
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@ -2,10 +2,10 @@ from __future__ import annotations
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import stable_diffusion_1_5
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import stable_diffusion_xl
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from setup import stub
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from setup import app
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@stub.function(gpu="A10G")
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@app.function(gpu="A10G")
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def main():
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stable_diffusion_1_5.SD15
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stable_diffusion_xl.SDXLTxt2Img
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@ -1,21 +1,21 @@
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invisible_watermark
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accelerate
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diffusers[torch]==0.24.0
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onnxruntime==1.16.3
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safetensors==0.4.1
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torch==2.1.0
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transformers==4.39.1
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xformers==0.0.22.post7
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diffusers[torch]==0.27.2
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onnxruntime==1.17.3
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safetensors==0.4.3
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torch==2.2.2
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transformers==4.39.3
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xformers==0.0.25.post1
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realesrgan==0.3.0
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basicsr>=1.4.2
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facexlib>=0.3.0
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gfpgan>=1.3.8
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scipy==1.12.0
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scipy==1.13.0
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opencv-python
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Pillow
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pillow-avif-plugin
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torchvision
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torchvision==0.17.2
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tqdm
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controlnet_aux
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@ -3,7 +3,7 @@ from __future__ import annotations
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import os
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import diffusers
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from modal import Image, Mount, Secret, Stub
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from modal import App, Image, Mount, Secret
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BASE_CACHE_PATH = "/vol/cache"
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BASE_CACHE_PATH_LORA = "/vol/cache/lora"
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@ -58,7 +58,7 @@ def download_model(name: str, model_url: str, token: str):
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cache_path = os.path.join(BASE_CACHE_PATH, name)
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pipe = diffusers.StableDiffusionPipeline.from_single_file(
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pretrained_model_link_or_path=model_url,
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use_auth_token=token,
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token=token,
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cache_dir=cache_path,
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)
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pipe.save_pretrained(cache_path, safe_serialization=True)
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@ -131,12 +131,12 @@ def build_image():
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)
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stub = Stub("stable-diffusion-cli")
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app = App("stable-diffusion-cli")
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base_stub = Image.from_dockerfile(
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path="Dockerfile",
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context_mount=Mount.from_local_file("requirements.txt"),
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)
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stub.image = base_stub.dockerfile_commands(
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app.image = base_stub.dockerfile_commands(
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"FROM base",
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"COPY config.yml /",
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context_mount=Mount.from_local_file("config.yml"),
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@ -10,11 +10,11 @@ from setup import (
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BASE_CACHE_PATH_CONTROLNET,
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BASE_CACHE_PATH_LORA,
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BASE_CACHE_PATH_TEXTUAL_INVERSION,
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stub,
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app,
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)
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@stub.cls(
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@app.cls(
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gpu="A10G",
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secrets=[Secret.from_dotenv(__file__)],
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)
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@ -187,15 +187,16 @@ class SD15:
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generated_images.extend(fixed_by_controlnet)
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base_images = fixed_by_controlnet
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if upscaler != "":
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upscaled = self._upscale(
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base_images=base_images,
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half_precision=False,
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tile=700,
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upscaler=upscaler,
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use_face_enhancer=use_face_enhancer,
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)
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generated_images.extend(upscaled)
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# TODO: Upscaler stopped working due to update of dependent packages. Replace with diffusers upscaler.
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# if upscaler != "":
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# upscaled = self._upscale(
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# base_images=base_images,
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# half_precision=False,
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# tile=700,
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# upscaler=upscaler,
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# use_face_enhancer=use_face_enhancer,
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# )
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# generated_images.extend(upscaled)
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image_output = []
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for image in generated_images:
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@ -5,10 +5,10 @@ import os
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import PIL.Image
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from modal import Secret, enter, method
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from setup import BASE_CACHE_PATH, BASE_CACHE_PATH_CONTROLNET, stub
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from setup import BASE_CACHE_PATH, app
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@stub.cls(
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@app.cls(
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gpu="A10G",
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secrets=[Secret.from_dotenv(__file__)],
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)
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@ -39,13 +39,13 @@ class SDXLTxt2Img:
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variant="fp16",
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)
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# self.refiner_cache_path = self.cache_path + "-refiner"
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# self.refiner = diffusers.StableDiffusionXLImg2ImgPipeline.from_pretrained(
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# self.refiner_cache_path,
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# torch_dtype=torch.float16,
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# use_safetensors=True,
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# variant="fp16",
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# )
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self.refiner_cache_path = self.cache_path + "-refiner"
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self.refiner = diffusers.StableDiffusionXLImg2ImgPipeline.from_pretrained(
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self.refiner_cache_path,
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torch_dtype=torch.float16,
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use_safetensors=True,
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variant="fp16",
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)
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# controlnets = config.get("controlnets")
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# if controlnets is not None:
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@ -94,12 +94,10 @@ class SDXLTxt2Img:
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n_prompt: str,
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height: int = 1024,
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width: int = 1024,
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batch_size: int = 1,
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steps: int = 30,
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seed: int = 1,
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upscaler: str = "",
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use_face_enhancer: bool = False,
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fix_by_controlnet_tile: bool = False,
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output_format: str = "png",
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) -> list[bytes]:
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"""
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@ -119,37 +117,33 @@ class SDXLTxt2Img:
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).images
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base_images = generated_images
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# for image in base_images:
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# image = self._resize_image(image=image, scale_factor=2)
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# self.refiner.to("cuda")
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# refined_images = self.refiner(
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# prompt=prompt,
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# negative_prompt=n_prompt,
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# num_inference_steps=steps,
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# strength=0.1,
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# # guidance_scale=7.5,
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# generator=generator,
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# image=image,
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# ).images
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# generated_images.extend(refined_images)
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# base_images = refined_images
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for image in base_images:
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image = self._resize_image(image=image, scale_factor=2)
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self.refiner.to("cuda")
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refined_images = self.refiner(
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prompt=prompt,
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negative_prompt=n_prompt,
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num_inference_steps=steps,
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strength=0.1,
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# guidance_scale=7.5,
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generator=generator,
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image=image,
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).images
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generated_images.extend(refined_images)
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base_images = refined_images
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"""
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Fix the generated images by the control_v11f1e_sd15_tile when `fix_by_controlnet_tile` is `True`.
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https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile
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"""
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# if fix_by_controlnet_tile:
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# max_embeddings_multiples = self._count_token(p=prompt, n=n_prompt)
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# print("========================確認用========================")
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# print("Step1")
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# self.controlnet_pipe.to("cuda")
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# self.controlnet_pipe.enable_vae_tiling()
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# self.controlnet_pipe.enable_xformers_memory_efficient_attention()
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# print("Step2")
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# for image in base_images:
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# image = self._resize_image(image=image, scale_factor=2)
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# print("Step3")
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# with torch.autocast("cuda"):
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# print("Step4")
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# fixed_by_controlnet = self.controlnet_pipe(
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# prompt=prompt * batch_size,
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# negative_prompt=n_prompt * batch_size,
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@ -160,7 +154,6 @@ class SDXLTxt2Img:
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# generator=generator,
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# image=image,
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# ).images
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# print("Step5")
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# generated_images.extend(fixed_by_controlnet)
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# base_images = fixed_by_controlnet
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@ -3,11 +3,11 @@ import time
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import modal
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import util
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stub = modal.Stub("run-stable-diffusion-cli")
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stub.run_inference = modal.Function.from_name("stable-diffusion-cli", "SD15.run_txt2img_inference")
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app = modal.App("run-stable-diffusion-cli")
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app.run_inference = modal.Function.from_name("stable-diffusion-cli", "SD15.run_txt2img_inference")
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@stub.local_entrypoint()
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@app.local_entrypoint()
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def main(
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prompt: str,
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n_prompt: str,
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@ -33,7 +33,7 @@ def main(
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if seed == -1:
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seed_generated = util.generate_seed()
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start_time = time.time()
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images = stub.run_inference.remote(
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images = app.run_inference.remote(
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prompt=prompt,
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n_prompt=n_prompt,
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height=height,
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@ -3,16 +3,18 @@ import time
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import modal
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import util
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stub = modal.Stub("run-stable-diffusion-cli")
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stub.run_inference = modal.Function.from_name("stable-diffusion-cli", "SDXLTxt2Img.run_inference")
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app = modal.Stub("run-stable-diffusion-cli")
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app.run_inference = modal.Function.from_name("stable-diffusion-cli", "SDXLTxt2Img.run_inference")
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@stub.local_entrypoint()
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@app.local_entrypoint()
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def main(
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prompt: str,
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n_prompt: str,
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height: int = 1024,
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width: int = 1024,
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samples: int = 5,
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steps: int = 20,
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seed: int = -1,
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upscaler: str = "",
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use_face_enhancer: str = "False",
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@ -29,10 +31,12 @@ def main(
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if seed == -1:
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seed_generated = util.generate_seed()
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start_time = time.time()
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images = stub.run_inference.remote(
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images = app.run_inference.remote(
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prompt=prompt,
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n_prompt=n_prompt,
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height=height,
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width=width,
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steps=steps,
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seed=seed_generated,
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upscaler=upscaler,
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use_face_enhancer=use_face_enhancer == "True",
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