Create generateColab.py

engine/generateColab.py

@@ -0,0 +1,120 @@
+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."
+    
+