e

app.py

@@ -5,19 +5,23 @@ import numpy as np
 from PIL import Image
 from diffusers import DiffusionPipeline
 from huggingface_hub import login
-#import os
-
-#login(token=os.environ.get('HF_KEY'))
+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image
+from datasets import load_dataset
+from diffusers import StableDiffusionImg2ImgPipeline
 
 import torch
 from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
 
 model_id = "stabilityai/stable-diffusion-2-1"
 
+device = "cuda" if torch.cuda.is_available() else "cpu"
 # Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead
 pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
 pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to("cpu")
+pipe = pipe.to(device)
 
 def resize(value,img):
     img = Image.open(img)
@@ -28,7 +32,7 @@ def infer(source_img, prompt, negative_prompt, guide, steps, seed, Strength):
     generator = torch.Generator(device).manual_seed(seed)     
     source_image = resize(768, source_img)
     source_image.save('source.png')
-    image = pipe(prompt, negative_prompt=negative_prompt, image=source_image, strength=Strength, guidance_scale=guide, num_inference_steps=steps).images[0]
+    image = pipe(prompt, negative_prompt=negative_prompt, init_image=source_image, strength=Strength, guidance_scale=guide, num_inference_steps=steps).images[0]
     return image
 
 gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label="Raw Image. Must Be .png"), gr.Textbox(label = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'), gr.Textbox(label='What you Do Not want the AI to generate.'),
@@ -36,4 +40,4 @@ gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label=
     gr.Slider(1, 25, value = 10, step = 1, label = 'Number of Iterations'),
     gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True), 
     gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)], 
-    outputs='image', title = "Stable Diffusion XL 1.0 Image to Image Pipeline CPU", description = "For more information on Stable Diffusion XL 1.0 see https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0 <br><br>Upload an Image (<b>MUST Be .PNG and 512x512 or 768x768</b>) enter a Prompt, or let it just do its Thing, then click submit. 10 Iterations takes about ~900-1200 seconds currently. For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic", article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").queue(max_size=5).launch()
+    outputs='image', title = "Stable Diffusion 2.1 Image to Image Pipeline CPU", description = "For more information on Stable Diffusion 2.1 see https://github.com/Stability-AI/stablediffusion <br><br>Upload an Image (<b>MUST Be .PNG and 512x512 or 768x768</b>) enter a Prompt, or let it just do its Thing, then click submit. 10 Iterations takes about ~900-1200 seconds currently. For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic", article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").queue(max_size=5).launch()