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VikramSingh178
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Browse filesFormer-commit-id: 8af21e8704b199f29ac59d82fe37d872de3c90e0 [formerly 8af21e8704b199f29ac59d82fe37d872de3c90e0 [formerly 8af21e8704b199f29ac59d82fe37d872de3c90e0 [formerly 8af21e8704b199f29ac59d82fe37d872de3c90e0 [formerly 8af21e8704b199f29ac59d82fe37d872de3c90e0 [formerly 1ad46af96222172330ca4a1361e94228cdc385a4]]]]]
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api/routers/sdxl_text_to_image.py
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import config
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from fastapi import APIRouter, HTTPException
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from typing import List
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from diffusers import DiffusionPipeline
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import torch
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from functools import lru_cache
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from scripts.api_utils import accelerator
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from models.sdxl_input import InputFormat
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from async_batcher.batcher import AsyncBatcher
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from scripts.api_utils import pil_to_b64_json, pil_to_s3_json
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torch._inductor.config.conv_1x1_as_mm = True
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torch._inductor.config.coordinate_descent_tuning = True
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torch._inductor.config.epilogue_fusion = False
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router = APIRouter()
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# Load the diffusion pipeline
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@lru_cache(maxsize=1)
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def load_pipeline(model_name, adapter_name,enable_compile:bool):
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"""
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Load the diffusion pipeline with the specified model and adapter names.
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if enable_compile is True:
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pipe.unet = torch.compile(pipe.unet, mode="max-autotune")
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pipe.vae.decode = torch.compile(pipe.vae.decode, mode="max-autotune")
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pipe.fuse_qkv_projections()
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return pipe
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<<<<<<< HEAD:api/routers/sdxl_text_to_image.py
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=======
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import sys
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sys.path.append("../scripts") # Path of the scripts directory
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>>>>>>> 846a4a3 (commit):product_diffusion_api/routers/sdxl_text_to_image.py
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import config
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from fastapi import APIRouter, HTTPException
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from typing import List
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from diffusers import DiffusionPipeline
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import torch
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from functools import lru_cache
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<<<<<<< HEAD:api/routers/sdxl_text_to_image.py
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from scripts.api_utils import accelerator
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from models.sdxl_input import InputFormat
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from async_batcher.batcher import AsyncBatcher
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from scripts.api_utils import pil_to_b64_json, pil_to_s3_json
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=======
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from s3_manager import S3ManagerService
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from PIL import Image
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import io
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>>>>>>> 846a4a3 (commit):product_diffusion_api/routers/sdxl_text_to_image.py
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torch._inductor.config.conv_1x1_as_mm = True
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torch._inductor.config.coordinate_descent_tuning = True
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torch._inductor.config.epilogue_fusion = False
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router = APIRouter()
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<<<<<<< HEAD:api/routers/sdxl_text_to_image.py
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=======
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def pil_to_b64_json(image):
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image_id = str(uuid.uuid4())
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buffered = BytesIO()
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image.save(buffered, format="PNG")
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b64_image = base64.b64encode(buffered.getvalue()).decode("utf-8")
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return {"image_id": image_id, "b64_image": b64_image}
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def pil_to_s3_json(image: Image.Image,file_name) -> str:
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image_id = str(uuid.uuid4())
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s3_uploader = S3ManagerService()
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image_bytes = io.BytesIO()
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image.save(image_bytes, format="PNG")
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image_bytes.seek(0)
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unique_file_name = s3_uploader.generate_unique_file_name(file_name)
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s3_uploader.upload_file(image_bytes, unique_file_name)
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signed_url = s3_uploader.generate_signed_url(
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unique_file_name, exp=43200
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) # 12 hours
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return {"image_id": image_id, "url": signed_url}
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>>>>>>> 846a4a3 (commit):product_diffusion_api/routers/sdxl_text_to_image.py
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# Load the diffusion pipeline
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@lru_cache(maxsize=1)
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<<<<<<< HEAD:api/routers/sdxl_text_to_image.py
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def load_pipeline(model_name, adapter_name,enable_compile:bool):
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"""
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Load the diffusion pipeline with the specified model and adapter names.
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if enable_compile is True:
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pipe.unet = torch.compile(pipe.unet, mode="max-autotune")
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pipe.vae.decode = torch.compile(pipe.vae.decode, mode="max-autotune")
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=======
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def load_pipeline(model_name, adapter_name,adapter_name_2):
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pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype= torch.bfloat16 ).to(
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"cuda"
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)
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pipe.load_lora_weights(adapter_name)
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pipe.load_lora_weights(adapter_name_2)
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pipe.set_adapters([adapter_name, adapter_name_2], adapter_weights=[0.7, 0.5])
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pipe.fuse_lora()
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pipe.unload_lora_weights()
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pipe.unet.to(memory_format=torch.channels_last)
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pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead")
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>>>>>>> 846a4a3 (commit):product_diffusion_api/routers/sdxl_text_to_image.py
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pipe.fuse_qkv_projections()
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return pipe
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scripts/output/README.md
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---
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license: creativeml-openrail-m
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library_name: diffusers
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tags:
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- stable-diffusion-xl
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- stable-diffusion-xl-diffusers
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- text-to-image
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- diffusers
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- diffusers-training
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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inference: true
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---
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<!-- This model card has been generated automatically according to the information the training script had access to. You
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should probably proofread and complete it, then remove this comment. -->
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SDXL Product Images
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## Intended uses & limitations
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#### How to use
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```python
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# TODO: add an example code snippet for running this diffusion pipeline
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```
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#### Limitations and bias
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[TODO: provide examples of latent issues and potential remediations]
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## Training details
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[TODO: describe the data used to train the model]
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scripts/output/pytorch_lora_weights.safetensors.REMOVED.git-id
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-
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55f1b147723f81304a3217185314d068b6d49439
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