Create app.py

triton
accelerate
transformers
ftfy
bitsandbytes==0.35.0
natsort
safetensors
xformers

app.py

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+import torch
+from torch import autocast
+from diffusers import StableDiffusionPipeline, DDIMScheduler
+from IPython.display import display
+
+model_path = WEIGHTS_DIR             # If you want to use previously trained model saved in gdrive, replace this with the full path of model in gdrive
+
+pipe = StableDiffusionPipeline.from_pretrained(model_path, safety_checker=None, torch_dtype=torch.float16).to("cuda")
+pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+pipe.enable_xformers_memory_efficient_attention()
+g_cuda = None
+
+#@markdown Can set random seed here for reproducibility.
+g_cuda = torch.Generator(device='cuda')
+seed = 52362 #@param {type:"number"}
+g_cuda.manual_seed(seed)
+
+#@title Run for generating images.
+
+prompt = "photo of zwx dog in a bucket" #@param {type:"string"}
+negative_prompt = "" #@param {type:"string"}
+num_samples = 4 #@param {type:"number"}
+guidance_scale = 7.5 #@param {type:"number"}
+num_inference_steps = 24 #@param {type:"number"}
+height = 512 #@param {type:"number"}
+width = 512 #@param {type:"number"}
+
+with autocast("cuda"), torch.inference_mode():
+    images = pipe(
+        prompt,
+        height=height,
+        width=width,
+        negative_prompt=negative_prompt,
+        num_images_per_prompt=num_samples,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        generator=g_cuda
+    ).images
+
+for img in images:
+    display(img)