feat: add img2img local test
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@ -46,3 +46,4 @@ local-tests/
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test/aigc_webui_inference_images/*.sh
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test/aigc_webui_inference_images/*.sh
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test/aigc_webui_inference_images/nohup.out
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test/aigc_webui_inference_images/nohup.out
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test/aigc_webui_inference_images/.env
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test/aigc_webui_inference_images/.env
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test/**/.env
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@ -0,0 +1,166 @@
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import json
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import requests
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import io
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import base64
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from PIL import Image, PngImagePlugin
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import time
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import os
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from gradio.processing_utils import encode_pil_to_base64
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from dotenv import load_dotenv
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load_dotenv()
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start_time = time.time()
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url = "http://127.0.0.1:8080"
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#with open("0013.jpg", "rb") as img:
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# init_img = base64.b64encode(img.read())
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#print(init_img.decode('utf-8'))
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#print('!!!!!!!!!!!')
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init_img = encode_pil_to_base64(Image.open("test.png"))
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mode = 0
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init_images = [init_img]
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#sketch = None
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#init_img_with_mask = None
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#inpaint_color_sketch = None
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#inpaint_color_sketch_orig = None
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#init_img_inpaint = None
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#init_mask_inpaint = None
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#seed_enable_extras = False
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#selected_scale_tab = 0
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#"img2img_batch_input_dir": img2img_batch_input_dir,
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#"img2img_batch_output_dir": img2img_batch_output_dir,
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#"img2img_batch_inpaint_mask_dir": img2img_batch_inpaint_mask_dir,
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mask = None
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mask_blur = 4
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mask_alpha = 0
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inpainting_fill = 0
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image_cfg_scale = 1.5
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resize_mode = 0
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inpaint_full_res = False
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inpaint_full_res_padding = 32
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inpainting_mask_invert = 0
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initial_noise_multiplier = None
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include_init_images = False
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img2img_batch_input_dir = ''
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img2img_batch_output_dir = ''
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img2img_batch_inpaint_mask_dir = ''
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task='image-to-image'
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username='test'
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checkpoint_info = json.loads(os.environ['checkpoint_info'])
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models = json.loads(os.environ["models"])
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enable_hr='true'
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denoising_strength=0.7
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firstphase_width=0
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firstphase_height=0
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hr_scale=2.0
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hr_upscaler="ESRGAN_4x"
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hr_second_pass_steps=0
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hr_resize_x=0
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hr_resize_y=0
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prompt='a dancing boy'
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styles=[]
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seed=-1
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subseed=-1
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subseed_strength=0.0
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seed_resize_from_h=0
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seed_resize_from_w=0
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sampler_name=None
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batch_size=1
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n_iter=1
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steps=20
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cfg_scale=7.0
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width=512
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height=512
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restore_faces=False
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tiling=False
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do_not_save_samples=False
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do_not_save_grid=False
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negative_prompt=''
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eta=1.0
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s_min_uncond=0.0
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s_churn=0.0
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s_tmax=1.0
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s_tmin=0.0
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s_noise=1.0
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override_settings={}
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sampler_index='Euler a'
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script_args=[10, "", ["anyloraCheckpoint_bakedvaeFtmseFp16NOT.safetensors", "abyssorangemix3AOM3_aom3a1b.safetensors", "aniflatmixAnimeFlatColorStyle_v20.safetensors", "babes_20.safetensors", "chilloutmix_NiPrunedFp32Fix.safetensors", "clarity_2.safetensors", "deliberate_v2.safetensors", "dosmix_.safetensors", "dreamshaper_5BakedVae.safetensors", "gameIconInstitute_v21.safetensors", "icerealistic_v21.safetensors", "kotosmix_v10.safetensors", "latexGenerator15By_v09.safetensors", "my_style_132.safetensors", "piying_4000_lora.safetensors", "realisticVisionV20_v20.safetensors", "revAnimated_v122.safetensors", "test1.safetensors", "test2.safetensors", "v2-1_768-ema-pruned.safetensors", "v2-inference-v.safetensors"], 0, "", "", 0, "", "", True, False, False, False, 0]
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#script_name='X/Y/Z plot'
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payload = {
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"endpoint_name": checkpoint_info['sagemaker_endpoint'],
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"task": "image-to-image",
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"username": "test",
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"checkpoint_info":checkpoint_info,
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"models":{
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"space_free_size": 2e10,
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"Stable-diffusion": models['Stable-diffusion'],
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"ControlNet": [],
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"hypernetworks": models['hypernetworks'],
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"Lora": models['Lora'],
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"embeddings": models["embeddings"]
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},
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"img2img_payload": {
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"init_images": init_images,
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"mask": mask,
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"mask_blur": mask_blur,
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"initial_noise_multiplier":initial_noise_multiplier,
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"inpainting_fill": inpainting_fill,
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"image_cfg_scale": image_cfg_scale,
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"resize_mode": resize_mode,
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"inpaint_full_res": inpaint_full_res,
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"inpaint_full_res_padding": inpaint_full_res_padding,
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"inpainting_mask_invert": inpainting_mask_invert,
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"include_init_images": include_init_images,
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"denoising_strength": denoising_strength,
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"prompt": prompt,
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"styles": styles,
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"seed": seed,
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"subseed": subseed,
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"subseed_strength": subseed_strength,
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"seed_resize_from_h": seed_resize_from_h,
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"seed_resize_from_w": seed_resize_from_w,
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"sampler_index": sampler_index,
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"batch_size": batch_size,
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"n_iter": n_iter,
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"steps": steps,
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"cfg_scale": cfg_scale,
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"width": width,
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"height": height,
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"restore_faces": restore_faces,
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"tiling": tiling,
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"negative_prompt": negative_prompt,
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"eta": eta,
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"s_churn": s_churn,
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"s_tmax": s_tmax,
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"s_tmin": s_tmin,
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"s_noise": s_noise,
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"override_settings": override_settings,
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},
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}
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response = requests.post(url=f'{url}/sdapi/v1/img2img', json=payload['img2img_payload'])
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print(f"run time is {time.time()-start_time}")
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print(f"response is {response}")
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r = response.json()
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id = 0
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for i in r['images']:
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image = Image.open(io.BytesIO(base64.b64decode(i.split(",",1)[0])))
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png_payload = {
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"image": "data:image/png;base64," + i
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}
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response2 = requests.post(url=f'{url}/sdapi/v1/png-info', json=png_payload)
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pnginfo = PngImagePlugin.PngInfo()
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pnginfo.add_text("parameters", response2.json().get("info"))
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image.save('output_%d.png'%id, pnginfo=pnginfo)
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id += 1
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