test

app.py

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+import random
+import matplotlib.pyplot as plt
+from PIL import Image
+
+
+import torch
+from torch import autocast
+from diffusers import StableDiffusionPipeline, DDIMScheduler
+
+
+import gradio as gr
+
+from gradio.components import Textbox, Image
+
+import torch
+from torch import autocast
+from diffusers import StableDiffusionPipeline, DDIMScheduler
+
+pipe = StableDiffusionPipeline.from_pretrained("taltaf9133/finetuned-stable-diffusion-log", torch_dtype=torch.float32)   #.to('cuda')
+#pipe.enable_xformers_memory_efficient_attention()
+
+prompt = "tv with sofa, realistic, hd, vivid"
+negative_prompt = "bad anatomy, ugly, deformed, desfigured, distorted, blurry, low quality, low definition, lowres, out of frame, out of image, cropped, cut off, signature, watermark"
+num_samples = 1
+guidance_scale = 7.5
+num_inference_steps = 30
+height = 512
+width = 512
+
+#seed = random.randint(0, 2147483647)
+#print("Seed: {}".format(str(seed)))
+#generator = torch.Generator(device='cuda').manual_seed(seed)
+
+def predict(prompt, negative_prompt):
+    #with autocast("cuda"), torch.inference_mode():
+    img = pipe(
+            prompt,
+            negative_prompt=negative_prompt,
+            height=height, width=width,
+            num_images_per_prompt=num_samples,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            #generator=generator
+        ).images[0]
+    return img
+
+title = "Stable Diffusion Demo"
+description = "Stable diffusion demo"
+
+
+# Input from user
+neg_p = "bad anatomy, ugly, deformed, desfigured, distorted, blurry, low quality, low definition, lowres, out of frame, out of image, cropped, cut off, signature, watermark"
+in_prompt = gradio.inputs.Textbox(lines=5, placeholder=None, default="ldg with scn style", label='Enter prompt')
+in_neg_prompt = gradio.inputs.Textbox(lines=5, placeholder=None, default=neg_p, label='Enter negative prompt')
+
+# Output response
+out_response = Image(label="Generated image:")
+
+# Create the Gradio demo
+demo = gradio.Interface(fn=predict, # mapping function from input to output
+                        inputs=[in_prompt, in_neg_prompt],
+                        outputs=gradio.Image(),
+                        title=title,
+                        description=description,)
+
+# Launch the demo!
+demo.launch(debug = True, share=True)

requirements.txt

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+accelerate
+transformers
+ftfy
+bitsandbytes==0.35.0
+natsort
+safetensors
+xformers
+diffusers
+gradio