Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -68,44 +68,6 @@ class calculateDuration:
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print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
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def generate_image(orginal_image, prompt, adapter_names, steps, seed, image_strength, cfg_scale, width, height, progress):
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gr.Info("Start to generate images ...")
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with calculateDuration(f"Make a new generator: {seed}"):
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# txt2img_pipe.to(device)
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generator = torch.Generator(device=device).manual_seed(seed)
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with calculateDuration("Generating image"):
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# Generate image
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joint_attention_kwargs = {"scale": 1}
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if orginal_image:
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generated_image = img2img_pipe(
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prompt=prompt,
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image=orginal_image,
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strength=image_strength,
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num_inference_steps=steps,
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guidance_scale=cfg_scale,
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width=width,
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height=height,
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generator=generator,
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joint_attention_kwargs=joint_attention_kwargs
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).images[0]
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else:
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generated_image = txt2img_pipe(
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prompt=prompt,
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num_inference_steps=steps,
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guidance_scale=cfg_scale,
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width=width,
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height=height,
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max_sequence_length=512,
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generator=generator,
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joint_attention_kwargs=joint_attention_kwargs
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).images[0]
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torch.cuda.empty_cache()
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progress(99, "Generate image success!")
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return generated_image
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def upload_image_to_r2(image, account_id, access_key, secret_key, bucket_name):
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with calculateDuration("Upload images"):
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@@ -152,8 +114,6 @@ def generate_random_4_digit_string():
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def run_lora(prompt, image_url, lora_strings_json, image_strength, cfg_scale, steps, randomize_seed, seed, width, height, upload_to_r2, account_id, access_key, secret_key, bucket, progress=gr.Progress(track_tqdm=True)):
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print("run_lora", prompt, lora_strings_json, cfg_scale, steps, width, height)
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gr.Info("Starting process")
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torch.cuda.empty_cache()
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img2img_model = False
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orginal_image = None
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if image_url:
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@@ -214,18 +174,46 @@ def run_lora(prompt, image_url, lora_strings_json, image_strength, cfg_scale, s
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img2img_pipe.set_adapters(lora_names, adapter_weights=adapter_weights)
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else:
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txt2img_pipe.set_adapters(lora_names, adapter_weights=adapter_weights)
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print(txt2img_pipe.get_active_adapters())
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# Generate image
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error_message = ""
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try:
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except Exception as e:
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error_message = str(e)
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gr.Error(error_message)
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print("
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final_image = None
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if final_image:
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@@ -236,12 +224,9 @@ def run_lora(prompt, image_url, lora_strings_json, image_strength, cfg_scale, s
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result = {"status": "success", "message": "Image generated but not uploaded"}
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else:
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result = {"status": "failed", "message": error_message}
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txt2img_pipe.delete_adapters(lora_names)
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gr.Info("Completed!")
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progress(100, "Completed!")
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torch.cuda.empty_cache()
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return final_image, seed, json.dumps(result)
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# Gradio interface
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print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
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def upload_image_to_r2(image, account_id, access_key, secret_key, bucket_name):
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with calculateDuration("Upload images"):
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def run_lora(prompt, image_url, lora_strings_json, image_strength, cfg_scale, steps, randomize_seed, seed, width, height, upload_to_r2, account_id, access_key, secret_key, bucket, progress=gr.Progress(track_tqdm=True)):
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print("run_lora", prompt, lora_strings_json, cfg_scale, steps, width, height)
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gr.Info("Starting process")
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img2img_model = False
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orginal_image = None
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if image_url:
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img2img_pipe.set_adapters(lora_names, adapter_weights=adapter_weights)
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else:
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txt2img_pipe.set_adapters(lora_names, adapter_weights=adapter_weights)
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# Generate image
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error_message = ""
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try:
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gr.Info("Start to generate images ...")
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with calculateDuration(f"Make a new generator: {seed}"):
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generator = torch.Generator(device=device).manual_seed(seed)
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with calculateDuration("Generating image"):
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# Generate image
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joint_attention_kwargs = {"scale": 1}
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if orginal_image:
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final_image = img2img_pipe(
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prompt=prompt,
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image=orginal_image,
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strength=image_strength,
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num_inference_steps=steps,
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guidance_scale=cfg_scale,
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width=width,
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height=height,
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generator=generator,
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joint_attention_kwargs=joint_attention_kwargs
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).images[0]
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else:
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final_image = txt2img_pipe(
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prompt=prompt,
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num_inference_steps=steps,
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guidance_scale=cfg_scale,
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width=width,
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height=height,
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max_sequence_length=512,
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generator=generator,
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joint_attention_kwargs=joint_attention_kwargs
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).images[0]
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except Exception as e:
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error_message = str(e)
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gr.Error(error_message)
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print("fatal error", e)
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final_image = None
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if final_image:
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result = {"status": "success", "message": "Image generated but not uploaded"}
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else:
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result = {"status": "failed", "message": error_message}
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gr.Info("Completed!")
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progress(100, "Completed!")
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return final_image, seed, json.dumps(result)
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# Gradio interface
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