Upload app.py

app.py

@@ -1,142 +1,236 @@
-import gradio as gr
-import numpy as np
-import random
-#import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo" #Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-#@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
-    
-    return image, seed
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024, #Replace with defaults that work for your model
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024, #Replace with defaults that work for your model
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0, #Replace with defaults that work for your model
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2, #Replace with defaults that work for your model
-                )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
-    )
-
-demo.queue().launch()
+import gradio as gr
+import numpy as np
+from diffusers import StableDiffusionXLControlNetInpaintPipeline
+from diffusers import StableDiffusionXLImg2ImgPipeline, DPMSolverMultistepScheduler, AutoencoderTiny, StableDiffusionXLControlNetPipeline, ControlNetModel
+from diffusers.utils import load_image
+from diffusers.image_processor import IPAdapterMaskProcessor
+import torch
+import os
+from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
+from diffusers.utils import make_image_grid
+from diffusers import DPMSolverSDEScheduler
+
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+if torch.cuda.is_available():
+    torch_dtype = torch.float16
+else:
+    torch_dtype = torch.float32
+
+processor_mask = IPAdapterMaskProcessor()
+controlnets = [
+    ControlNetModel.from_pretrained(
+        "diffusers/controlnet-depth-sdxl-1.0",variant="fp16",use_safetensors=True,torch_dtype=torch.float16
+    ),
+    ControlNetModel.from_pretrained(
+        "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16, use_safetensors=True,variant="fp16"
+    ),
+]
+
+def ourhood_inference(prompt1=str,num_inference_steps=int,scaffold=int):
+
+###pro_encode = pipe_cn.encode_text(prompt)
+
+    ###pro_encode = pipe_CN.encode_text(prompt)[2]
+
+
+### function has no formats defined
+
+    scaff_dic={1:{'mask1':"https://huggingface.co/Tonioesparza/ourhood_training_dreambooth_lora_2_0/blob/main/mask_in_square_2.png",
+                  'depth_image':"https://huggingface.co/Tonioesparza/ourhood_training_dreambooth_lora_2_0/blob/main/mask_depth_noroof_square.png",
+                  'canny_image':"https://huggingface.co/Tonioesparza/ourhood_training_dreambooth_lora_2_0/blob/main/mask_depth_solo_square.png"},
+               2:{'mask1':"https://huggingface.co/Tonioesparza/ourhood_training_dreambooth_lora_2_0/blob/main/mask_in_C.png",
+                  'depth_image':"https://huggingface.co/Tonioesparza/ourhood_training_dreambooth_lora_2_0/blob/main/depth_C.png",
+                  'canny_image':"https://huggingface.co/Tonioesparza/ourhood_training_dreambooth_lora_2_0/blob/main/canny_C.png"},
+               3:{'mask1':"https://huggingface.co/Tonioesparza/ourhood_training_dreambooth_lora_2_0/blob/main/mask_in_B.png",
+                  'depth_image':"https://huggingface.co/Tonioesparza/ourhood_training_dreambooth_lora_2_0/blob/main/depth_B.png",
+                  'canny_image':"https://huggingface.co/Tonioesparza/ourhood_training_dreambooth_lora_2_0/blob/main/canny_B.png"}}
+
+
+    pipe_CN = StableDiffusionXLControlNetPipeline.from_pretrained("SG161222/RealVisXL_V5.0", torch_dtype=torch.float16,controlnet=controlnets[0], use_safetensors=True, variant='fp16')
+    pipe_CN.vae = AutoencoderTiny.from_pretrained("madebyollin/taesdxl", torch_dtype=torch.float16)
+    pipe_CN.scheduler=DPMSolverSDEScheduler.from_pretrained("SG161222/RealVisXL_V5.0",subfolder="scheduler",use_karras_sigmas=True)
+    ###pipe_CN.scheduler=DPMSolverMultistepScheduler.from_pretrained("SG161222/RealVisXL_V5.0",subfolder="scheduler",use_karras_sigmas=True)
+    ###pipe_CN.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
+    pipe_CN.to("cuda")
+
+    ##############################load loras
+
+    pipe_CN.load_lora_weights('CreativesCombined/hb8_cases_dreambooth_lora_test_1_14', weight_name='pytorch_lora_weights.safetensors',adapter_name='cases')
+    ###pipe_CN.fuse_lora()
+
+    output_height = 1024
+    output_width = 1024
+    mask1 = load_image(scaff_dic[scaffold]['mask1'])
+    masks = processor_mask.preprocess([mask1], height=output_height, width=output_width)
+    masks = [masks.reshape(1, masks.shape[0], masks.shape[2], masks.shape[3])]
+    ###ip_images init
+    ###ip_img_1 = load_image(r"C:\Users\AntonioEsparzaGlisma\PycharmProjects\hB8\Cases\a-place-to_210930_HAY_A-PLACE-TO_091-768x1024.png")
+    ###ip_images = [[ip_img_1]]
+    pipe_CN.set_ip_adapter_scale([[0.7]])
+    n_steps = num_inference_steps
+    ###precomputed depth image
+    depth_image = load_image(scaff_dic[scaffold]['depth_image'])
+    canny_image = load_image(scaff_dic[scaffold]['canny_image'])
+    images_CN = [depth_image, canny_image]
+
+    neg1 = 'text,watermark'
+    prompt2 = 'Photorealistic rendering, of an OurHood privacy booth, with a silken oak frame, hickory stained melange polyester fabric, windows'
+    neg2 = 'curtains, pillows'
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+
+    results = pipe_CN(
+            prompt=prompt1,
+            ###ip_adapter_image=ip_images,
+            negative_prompt=neg1,
+            num_inference_steps=n_steps,
+            num_images_per_prompt=1,
+            generator=generator,
+            denoising_end=0.9,
+            image=images_CN[0],
+            output_type="latent",
+            control_guidance_end=0.25,
+            controlnet_conditioning_scale=0.5,
+        ).images[0]
+
+    refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0",text_encoder_2=pipe_CN.text_encoder_2,vae=pipe_CN.vae,torch_dtype=torch.float16,use_safetensors=True,variant="fp16")
+    refiner.to("cuda")
+
+    del pipe_CN
+    torch.cuda.empty_cache()
+
+    image = refiner(
+        prompt=prompt1,
+        num_inference_steps=n_steps,
+        denoising_start=0.8,
+        image=results).images[0]
+
+    del refiner
+    torch.cuda.empty_cache()
+
+    pipe_IN = StableDiffusionXLControlNetInpaintPipeline.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1",controlnet=controlnets, torch_dtype=torch.float16, variant="fp16").to("cuda")
+    pipe_IN.load_lora_weights('Tonioesparza/ourhood_training_dreambooth_lora_2_0', weight_name='pytorch_lora_weights.safetensors',adapter_name='ourhood')
+    pipe_IN.to("cuda")
+
+    image = pipe_IN(
+            prompt=prompt2,
+            negative_prompt=neg2,
+            image=image,
+            mask_image=mask1,
+            num_inference_steps=n_steps,
+            strength=0.95,
+            control_guidance_end=[0.3,0.9],
+            controlnet_conditioning_scale=[0.3, 0.45],
+            control_image=images_CN,
+            generator=generator,
+        ).images[0]
+
+    image.show()
+    del pipe_IN
+    torch.cuda.empty_cache()
+
+    return image
+
+
+
+
+"""
+image = refiner(
+    prompt=prompt,
+    num_inference_steps=40,
+    denoising_start=0.8,
+    image=image,
+).images[0]
+"""
+
+#@spaces.GPU #[uncomment to use ZeroGPU]
+
+examples = [
+    "A photograph, of an Ourhood privacy booth, front view, in a warehouse eventspace environment, in the style of event photography, silken oak frame, checkered warm grey exterior fabric, checkered warm grey interior fabric, curtains, diner seating, pillows",
+    "A photograph, of an Ourhood privacy booth, side view, in a warehouse eventspace environment, in the style of event photography, silken oak frame, taupe exterior fabric",
+    "A photograph, of an Ourhood privacy booth, close-up, in a HolmrisB8_HQ office environment, in the style of makeshift photoshoot, silken oak frame, taupe exterior fabric, taupe interior fabric, pillows",
+    "A rendering, of an Ourhood privacy booth, front view, in a Nordic atrium environment, in the style of Keyshot, silken oak frame, taupe exterior fabric, taupe interior fabric, diner seating"]
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 640px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""
+        # HB8-Ourhood inference test
+        """)
+        
+        with gr.Row():
+            
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            
+            run_button = gr.Button("Run", scale=0)
+
+        
+        result = gr.Image(label="Result", show_label=False)
+
+        with gr.Accordion("Advanced Settings", open=False):
+            
+            perspective = gr.Slider(
+                label="perspective",
+                minimum=1,
+                maximum=3,
+                step=1,
+                value=1,
+            )
+            
+            seed = gr.Slider(
+                label="tracking number (seed)",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+            
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+            with gr.Row():
+
+                fracc = gr.Slider(
+                    label="¨seed",
+                    minimum=0,
+                    maximum=9999,
+                    step=1,
+                    value=0, #Replace with defaults that work for your model
+                )
+                
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=35,
+                    maximum=50,
+                    step=1,
+                    value=35, #Replace with defaults that work for your model
+                )
+        
+        gr.Examples(
+            examples = examples,
+            inputs = [prompt]
+        )
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn = ourhood_inference,
+        inputs = [prompt, num_inference_steps, perspective],
+        outputs = [result]
+    )
+
+demo.queue().launch()
+