Delete for_colab/engine/generate.py

for_colab/engine/generate.py

@@ -1,120 +0,0 @@
-import random
-import requests
-import torch
-import time
-import gradio as gr
-from io import BytesIO
-from PIL import Image
-import imageio
-from dotenv import load_dotenv
-import os
-
-load_dotenv("config.txt")
-
-path_to_base_model = "models/checkpoint/gpu-model/base/dreamdrop-v1.safetensors"
-path_to_inpaint_model = "models/checkpoint/gpu-model/inpaint/dreamdrop-inpainting.safetensors"
-
-xl = os.getenv("xl")
-
-if xl == "True":
-    from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline, StableDiffusionXLInpaintPipeline
-    pipe_t2i = StableDiffusionXLPipeline.from_single_file(path_to_base_model, torch_dtype=torch.float16, use_safetensors=True)
-    pipe_t2i = pipe_t2i.to("cuda")
-
-    pipe_i2i = StableDiffusionXLImg2ImgPipeline.from_single_file(path_to_base_model, torch_dtype=torch.float16, use_safetensors=True)
-    pipe_i2i = pipe_i2i.to("cuda")
-
-    pipe_inpaint = StableDiffusionXLInpaintPipeline.from_single_file(path_to_inpaint_model, torch_dtype=torch.float16, use_safetensors=True)
-    pipe_inpaint = pipe_inpaint.to("cuda")
-else:
-    from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipeline
-    pipe_t2i = StableDiffusionPipeline.from_single_file(path_to_base_model, torch_dtype=torch.float16, use_safetensors=True)
-    pipe_t2i = pipe_t2i.to("cuda")
-
-    pipe_i2i = StableDiffusionImg2ImgPipeline.from_single_file(path_to_base_model, torch_dtype=torch.float16, use_safetensors=True)
-    pipe_i2i = pipe_i2i.to("cuda")
-
-    pipe_inpaint = StableDiffusionInpaintPipeline.from_single_file(path_to_inpaint_model, torch_dtype=torch.float16, use_safetensors=True)
-    pipe_inpaint = pipe_inpaint.to("cuda")
-
-
-pipe_t2i.load_lora_weights(pretrained_model_name_or_path_or_dict="models/lora", weight_name="epic_noiseoffset.safetensors")
-pipe_t2i.fuse_lora(lora_scale=0.1)
-
-pipe_i2i.load_lora_weights(pretrained_model_name_or_path_or_dict="models/lora", weight_name="epic_noiseoffset.safetensors")
-pipe_i2i.fuse_lora(lora_scale=0.1)
-
-pipe_inpaint.load_lora_weights(pretrained_model_name_or_path_or_dict="models/lora", weight_name="epic_noiseoffset.safetensors")
-pipe_inpaint.fuse_lora(lora_scale=0.1)
-
-
-def gpugen(prompt, mode, guidance, width, height, num_images, i2i_strength, inpaint_strength, i2i_change, inpaint_change, init=None, inpaint_image=None, progress = gr.Progress(track_tqdm=True)):
-    if mode == "Fast":
-        steps = 30
-    elif mode == "High Quality":
-        steps = 45
-    else:
-        steps = 20
-    results = []
-    seed = random.randint(1, 9999999)
-    if not i2i_change and not inpaint_change:
-        num = random.randint(100, 99999)
-        start_time = time.time()
-        for _ in range(num_images):
-            image = pipe_t2i(
-                prompt=prompt,
-                negative_prompt="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
-                num_inference_steps=steps,
-                guidance_scale=guidance,
-                width=width, height=height,
-                seed=seed,
-            ).images
-            image[0].save(f"outputs/{num}_txt2img_gpu{_}.jpg")
-            results.append(image[0])
-        end_time = time.time()
-        execution_time = end_time - start_time
-        return results, f"Time taken: {execution_time} sec."
-    elif inpaint_change and not i2i_change:
-        imageio.imwrite("output_image.png", inpaint_image["mask"])
-
-        num = random.randint(100, 99999)
-        start_time = time.time()
-        for _ in range(num_images):
-            image = pipe_inpaint(
-                prompt=prompt,
-                image=inpaint_image["image"],
-                mask_image=inpaint_image["mask"],
-                negative_prompt="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
-                num_inference_steps=steps,
-                guidance_scale=guidance,
-                strength=inpaint_strength,
-                width=width, height=height,
-                seed=seed,
-            ).images
-            image[0].save(f"outputs/{num}_inpaint_gpu{_}.jpg")
-            results.append(image[0])
-        end_time = time.time()
-        execution_time = end_time - start_time
-        return results, f"Time taken: {execution_time} sec."
-    
-    else:
-        num = random.randint(100, 99999)
-        start_time = time.time()
-        for _ in range(num_images):
-            image = pipe_i2i(
-                prompt=prompt,
-                negative_prompt="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
-                image=init,
-                num_inference_steps=steps,
-                guidance_scale=guidance,
-                width=width, height=height,
-                strength=i2i_strength,
-                seed=seed,
-            ).images
-            image[0].save(f"outputs/{num}_img2img_gpu{_}.jpg")
-            results.append(image[0])
-        end_time = time.time()
-        execution_time = end_time - start_time
-        return results, f"Time taken: {execution_time} sec."
-    
-