Update app.py

app.py

@@ -113,8 +113,8 @@ def load_and_prepare_model():
     #unetX = UNet2DConditionModel.from_pretrained('ford442/RealVisXL_V5.0_BF16',subfolder='unet').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
     #sched = EulerAncestralDiscreteScheduler.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler',beta_schedule="scaled_linear", steps_offset=1,timestep_spacing="trailing"))
     #sched = EulerAncestralDiscreteScheduler.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler', steps_offset=1,timestep_spacing="trailing")
-     #sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1,use_karras_sigmas=True)
-    sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear")
+    sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1,use_karras_sigmas=True)
+    #sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear")
     #sched = DPMSolverSDEScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler')
     #pipeX = StableDiffusionXLPipeline.from_pretrained("SG161222/RealVisXL_V5.0").to(torch.bfloat16)
     #pipeX = StableDiffusionXLPipeline.from_pretrained("ford442/Juggernaut-XI-v11-fp32",use_safetensors=True)
@@ -130,7 +130,6 @@ def load_and_prepare_model():
         token=HF_TOKEN,
        # custom_pipeline="lpw_stable_diffusion_xl",
         #use_safetensors=True,
-      #  use_auth_token=HF_TOKEN,
      #  vae=AutoencoderKL.from_pretrained("BeastHF/MyBack_SDXL_Juggernaut_XL_VAE/MyBack_SDXL_Juggernaut_XL_VAE_V10(version_X).safetensors",repo_type='model',safety_checker=None),
      #   vae=AutoencoderKL.from_pretrained("stabilityai/sdxl-vae",repo_type='model',safety_checker=None, torch_dtype=torch.float32),
        # vae=AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16",repo_type='model',safety_checker=None),
@@ -232,7 +231,7 @@ def randomize_seed_fn() -> int:
     seed = random.randint(0, MAX_SEED)
     return seed
 
-def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp,denoise):
+def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp):
     filename= f'tst_A_{timestamp}.txt'
     with open(filename, "w") as f:
         f.write(f"Realvis 5.0 (Tester A) \n")
@@ -240,7 +239,6 @@ def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp,denoise):
         f.write(f"Prompt: {prompt} \n")
         f.write(f"Steps: {num_inference_steps} \n")
         f.write(f"Guidance Scale: {guidance_scale} \n")
-        f.write(f"Denoise Strength: {denoise} \n")
         f.write(f"SPACE SETUP: \n")
         f.write(f"Use Model Dtype: no \n")
         f.write(f"Model Scheduler: Euler_a all_custom before cuda \n")
@@ -263,7 +261,6 @@ def generate_30(
     guidance_scale: float = 4,
     num_inference_steps: int = 125,
     use_resolution_binning: bool = True, 
-    denoise: float = 0.3,
     lora_scale: float = 0.5,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
@@ -276,7 +273,6 @@ def generate_30(
         "prompt": [prompt],
         "negative_prompt": [negative_prompt],
         "negative_prompt_2": [neg_prompt_2],
-        "strength": denoise,
         "width": width,
         "height": height,
         "guidance_scale": guidance_scale,
@@ -290,7 +286,7 @@ def generate_30(
     images = []
     pipe.scheduler.set_timesteps(num_inference_steps,device)
     timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-    uploadNote(prompt,num_inference_steps,guidance_scale,timestamp,denoise)
+    uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
     batch_options = options.copy()
     rv_image = pipe(**batch_options).images[0]
     sd_image_path = f"rv50_A_{timestamp}.png"
@@ -321,7 +317,6 @@ def generate_60(
     guidance_scale: float = 4,
     num_inference_steps: int = 250,
     use_resolution_binning: bool = True, 
-    denoise: float = 0.3,
     lora_scale: float = 0.5,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
@@ -334,7 +329,6 @@ def generate_60(
         "prompt": [prompt],
         "negative_prompt": [negative_prompt],
         "negative_prompt_2": [neg_prompt_2],
-        "strength": denoise,
         "width": width,
         "height": height,
         "guidance_scale": guidance_scale,
@@ -348,7 +342,7 @@ def generate_60(
     images = []
     pipe.scheduler.set_timesteps(num_inference_steps,device)
     timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-    uploadNote(prompt,num_inference_steps,guidance_scale,timestamp,denoise)
+    uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
     batch_options = options.copy()
     rv_image = pipe(**batch_options).images[0]
     sd_image_path = f"rv50_A_{timestamp}.png"
@@ -379,7 +373,6 @@ def generate_90(
     guidance_scale: float = 4,
     num_inference_steps: int = 250,
     use_resolution_binning: bool = True, 
-    denoise: float = 0.3,
     lora_scale: float = 0.5,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
@@ -392,7 +385,6 @@ def generate_90(
         "prompt": [prompt],
         "negative_prompt": [negative_prompt],
         "negative_prompt_2": [neg_prompt_2],
-        "strength": denoise,
         "width": width,
         "height": height,
         "guidance_scale": guidance_scale,
@@ -406,7 +398,7 @@ def generate_90(
     images = []
     pipe.scheduler.set_timesteps(num_inference_steps,device)
     timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-    uploadNote(prompt,num_inference_steps,guidance_scale,timestamp,denoise)
+    uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
     batch_options = options.copy()
     rv_image = pipe(**batch_options).images[0]
     sd_image_path = f"rv50_A_{timestamp}.png"
@@ -494,13 +486,6 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
                     value="('deformed', 'distorted', 'disfigured':1.3),'not photorealistic':1.5, 'poorly drawn', 'bad anatomy', 'wrong anatomy', 'extra limb', 'missing limb', 'floating limbs', 'poorly drawn hands', 'poorly drawn feet', 'poorly drawn face':1.3, 'out of frame', 'extra limbs', 'bad anatomy', 'bad art', 'beginner',  'distorted face','amateur'",
                     visible=True,
                 )
-        denoise = gr.Slider(
-            label="Denoising Strength",
-            minimum=0.0,
-            maximum=1.0,
-            step=0.01,
-            value=0.3,
-        )
         lora_scale = gr.Slider(
             label="LORA Scale (Skin)",
             minimum=0.0,
@@ -568,7 +553,6 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             height,
             guidance_scale,
             num_inference_steps,
-            denoise,
             lora_scale,
         ],
         outputs=[result],
@@ -590,7 +574,6 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             height,
             guidance_scale,
             num_inference_steps,
-            denoise,
             lora_scale,
         ],
         outputs=[result],
@@ -612,7 +595,6 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             height,
             guidance_scale,
             num_inference_steps,
-            denoise,
             lora_scale,
         ],
         outputs=[result],