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appAnything.py

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+import gradio as gr
+import torch
+#from torch import autocast // only for GPU
+
+from PIL import Image
+import numpy as np
+from io import BytesIO
+import os
+MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
+
+#from diffusers import StableDiffusionPipeline
+from diffusers import StableDiffusionImg2ImgPipeline
+
+print("hello")
+
+YOUR_TOKEN=MY_SECRET_TOKEN
+
+device="cpu"
+
+#prompt_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN)
+#prompt_pipe.to(device)
+
+img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    "Linaqruf/anything-v3-1",
+    use_auth_token=YOUR_TOKEN,
+    safety_checker=None,             # ← disable safety checker
+)
+img_pipe.to(device)
+
+source_img = gr.Image(sources=["upload", "webcam", "clipboard"], type="filepath", label="Add Image for Text Guided Editing and Sketch")
+gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[1], height="auto")
+
+def resize(value,img):
+  #baseheight = value
+  img = Image.open(img)
+  #hpercent = (baseheight/float(img.size[1]))
+  #wsize = int((float(img.size[0])*float(hpercent)))
+  #img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS)
+  img = img.resize((value,value), Image.Resampling.LANCZOS)
+  return img
+
+def infer(source_img, prompt, guide, steps, seed, strength):
+    generator = torch.Generator("cpu").manual_seed(seed)
+
+    source_image = Image.open(source_img).convert("RGB")
+    source_image = source_image.resize((512, 512), Image.Resampling.LANCZOS)
+
+    result = img_pipe(
+        [prompt],
+        image=source_image,
+        strength=strength,
+        guidance_scale=guide,
+        num_inference_steps=steps,
+        generator=generator
+    )
+
+    output_images = result["images"]
+    output_paths = []
+
+    for idx, img in enumerate(output_images):
+        filename = f"output_{seed}_{idx}.png"
+        save_path = os.path.join("outputs", filename)
+        os.makedirs("outputs", exist_ok=True)
+        img.save(save_path)
+        print(f"Saved image to: {save_path}")
+        output_paths.append(save_path)
+
+    # Optional: return paths or Gradio can render them too
+    return output_images
+
+print("Great ! Everything is working fine !")
+
+title="Text Guided Image Editing"
+description="<p style='text-align: center;'>Text Guided Image Editing via Stable Diffusion Image to Image using CPU and HF token. <br />Warning: CPU processing is slow... 6/7 min inference time.</p>" 
+
+gr.Interface(fn=infer, inputs=[source_img,
+    "text",
+    gr.Slider(2, 15, value = 6.7, label = 'Guidence Scale'),
+    gr.Slider(10, 50, value = 13, step = 1, label = 'Number of Iterations'),
+    gr.Slider(label = "Seed", minimum = 0, maximum = 676767, step = 67, randomize = False),
+    gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .67)],
+    outputs=gallery,title=title,description=description, allow_flagging="never", flagging_dir="flagged").queue(max_size=67).launch(enable_queue=True)

appOmniGenPipeline.py

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+import gradio as gr
+import torch
+#from torch import autocast // only for GPU
+
+from PIL import Image
+import numpy as np
+from io import BytesIO
+import os
+MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
+
+#from diffusers import StableDiffusionPipeline
+from diffusers import StableDiffusionImg2ImgPipeline
+
+print("hello")
+
+YOUR_TOKEN=MY_SECRET_TOKEN
+
+device="cpu"
+
+#prompt_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN)
+#prompt_pipe.to(device)
+
+img_pipe = OmniGenPipeline.from_pretrained(
+    "Shitao/OmniGen-v1-diffusers",
+    torch_dtype=torch.bfloat16,
+    use_auth_token=YOUR_TOKEN,
+    safety_checker=None,             # ← disable safety checker
+)
+img_pipe.to(device)
+
+source_img = gr.Image(sources=["upload", "webcam", "clipboard"], type="filepath", label="Add Image for Text Guided Editing and Sketch")
+gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[1], height="auto")
+
+def resize(value,img):
+  #baseheight = value
+  img = Image.open(img)
+  #hpercent = (baseheight/float(img.size[1]))
+  #wsize = int((float(img.size[0])*float(hpercent)))
+  #img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS)
+  img = img.resize((value,value), Image.Resampling.LANCZOS)
+  return img
+
+def infer(source_img, prompt, guide, steps, seed, strength):
+    generator = torch.Generator("cpu").manual_seed(seed)
+
+    source_image = Image.open(source_img).convert("RGB")
+    source_image = source_image.resize((512, 512), Image.Resampling.LANCZOS)
+
+    result = img_pipe(
+        [prompt],
+        image=source_image,
+        strength=strength,
+        guidance_scale=guide,
+        num_inference_steps=steps,
+        generator=generator
+    )
+
+    output_images = result["images"]
+    output_paths = []
+
+    for idx, img in enumerate(output_images):
+        filename = f"output_{seed}_{idx}.png"
+        save_path = os.path.join("outputs", filename)
+        os.makedirs("outputs", exist_ok=True)
+        img.save(save_path)
+        print(f"Saved image to: {save_path}")
+        output_paths.append(save_path)
+
+    # Optional: return paths or Gradio can render them too
+    return output_images
+
+print("Great ! Everything is working fine !")
+
+title="Text Guided Image Editing"
+description="<p style='text-align: center;'>Text Guided Image Editing via Stable Diffusion Image to Image using CPU and HF token. <br />Warning: CPU processing is slow... 6/7 min inference time.</p>" 
+
+gr.Interface(fn=infer, inputs=[source_img,
+    "text",
+    gr.Slider(2, 15, value = 6.7, label = 'Guidence Scale'),
+    gr.Slider(10, 50, value = 13, step = 1, label = 'Number of Iterations'),
+    gr.Slider(label = "Seed", minimum = 0, maximum = 676767, step = 67, randomize = False),
+    gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .67)],
+    outputs=gallery,title=title,description=description, allow_flagging="never", flagging_dir="flagged").queue(max_size=67).launch(enable_queue=True)