Timesteps is not constant.

[Prashant0664]

Using https://colab.research.google.com/github/deforum-art/deforum-stable-diffusion/blob/main/Deforum_Stable_Diffusion.ipynb to generate small clip.
Using ddim sampler.
First image get generated with 50 timesteps, but after that images started generating using 17 timesteps(destroying quality). How to prevent this so that timesteps remain constant.
Please help!

My settings:
`def DeforumAnimArgs():

#@markdown ####**Animation:**
animation_mode = '2D' #@param ['None', '2D', '3D', 'Video Input', 'Interpolation'] {type:'string'}
max_frames = 100 #@param {type:"number"}
border = 'replicate' #@param ['wrap', 'replicate'] {type:'string'}

#@markdown ####**Motion Parameters:**
angle = "0:(0)"#@param {type:"string"}
zoom = "0:(0)"#@param {type:"string"}
translation_x = "0:(0)"#@param {type:"string"}
translation_y = "0:(0)"#@param {type:"string"}
translation_z = "0:(1)"#@param {type:"string"}
rotation_3d_x = "0:(0)"#@param {type:"string"}
rotation_3d_y = "0:(0)"#@param {type:"string"}
rotation_3d_z = "0:(0)"#@param {type:"string"}
flip_2d_perspective = False #@param {type:"boolean"}
perspective_flip_theta = "0:(0)"#@param {type:"string"}
perspective_flip_phi = "0:(t%15)"#@param {type:"string"}
perspective_flip_gamma = "0:(0)"#@param {type:"string"}
perspective_flip_fv = "0:(53)"#@param {type:"string"}
noise_schedule = "0: (0.02)"#@param {type:"string"}
strength_schedule = "0: (0.65)"#@param {type:"string"}
contrast_schedule = "0: (1.0)"#@param {type:"string"}
hybrid_comp_alpha_schedule = "0:(1)" #@param {type:"string"}
hybrid_comp_mask_blend_alpha_schedule = "0:(0.5)" #@param {type:"string"}
hybrid_comp_mask_contrast_schedule = "0:(1)" #@param {type:"string"}
hybrid_comp_mask_auto_contrast_cutoff_high_schedule =  "0:(100)" #@param {type:"string"}
hybrid_comp_mask_auto_contrast_cutoff_low_schedule =  "0:(0)" #@param {type:"string"}

#@markdown ####**Sampler Scheduling:**
enable_schedule_samplers = False #@param {type:"boolean"}
sampler_schedule = "0:('euler'),10:('dpm2'),20:('dpm2_ancestral'),30:('heun'),40:('euler'),50:('euler_ancestral'),60:('dpm_fast'),70:('dpm_adaptive'),80:('dpmpp_2s_a'),90:('dpmpp_2m')" #@param {type:"string"}

#@markdown ####**Unsharp mask (anti-blur) Parameters:**
kernel_schedule = "0: (5)"#@param {type:"string"}
sigma_schedule = "0: (1.0)"#@param {type:"string"}
amount_schedule = "0: (0.2)"#@param {type:"string"}
threshold_schedule = "0: (0.0)"#@param {type:"string"}

#@markdown ####**Coherence:**
color_coherence = 'Match Frame 0 LAB' #@param ['None', 'Match Frame 0 HSV', 'Match Frame 0 LAB', 'Match Frame 0 RGB', 'Video Input'] {type:'string'}
color_coherence_video_every_N_frames = 1 #@param {type:"integer"}
color_force_grayscale = False #@param {type:"boolean"}
diffusion_cadence = '1' #@param ['1','2','3','4','5','6','7','8'] {type:'string'}

#@markdown ####**3D Depth Warping:**
use_depth_warping = False #@param {type:"boolean"}
midas_weight = 0.3#@param {type:"number"}
near_plane = 200
far_plane = 10000
fov = 40#@param {type:"number"}
padding_mode = 'border'#@param ['border', 'reflection', 'zeros'] {type:'string'}
sampling_mode = 'bicubic'#@param ['bicubic', 'bilinear', 'nearest'] {type:'string'}
save_depth_maps = False #@param {type:"boolean"}

#@markdown ####**Video Input:**
video_init_path ='/content/video_in.mp4'#@param {type:"string"}
extract_nth_frame = 1#@param {type:"number"}
overwrite_extracted_frames = True #@param {type:"boolean"}
use_mask_video = False #@param {type:"boolean"}
video_mask_path ='/content/video_in.mp4'#@param {type:"string"}

#@markdown ####**Hybrid Video for 2D/3D Animation Mode:**
hybrid_generate_inputframes = False #@param {type:"boolean"}
hybrid_use_first_frame_as_init_image = True #@param {type:"boolean"}
hybrid_motion = "None" #@param ['None','Optical Flow','Perspective','Affine']
hybrid_motion_use_prev_img = False #@param {type:"boolean"}
hybrid_flow_method = "DIS Medium" #@param ['DenseRLOF','DIS Medium','Farneback','SF']
hybrid_composite = False #@param {type:"boolean"}
hybrid_comp_mask_type = "None" #@param ['None', 'Depth', 'Video Depth', 'Blend', 'Difference']
hybrid_comp_mask_inverse = False #@param {type:"boolean"}
hybrid_comp_mask_equalize = "None" #@param  ['None','Before','After','Both']
hybrid_comp_mask_auto_contrast = False #@param {type:"boolean"}
hybrid_comp_save_extra_frames = False #@param {type:"boolean"}
hybrid_use_video_as_mse_image = False #@param {type:"boolean"}

#@markdown ####**Interpolation:**
interpolate_key_frames = False #@param {type:"boolean"}
interpolate_x_frames = 20 #@param {type:"number"}

#@markdown ####**Resume Animation:**
resume_from_timestring = False #@param {type:"boolean"}
resume_timestring = "20220829210106" #@param {type:"string"}

return locals()`

Load Settings:
`
#@markdown Load Settings
override_settings_with_file = False #@param {type:"boolean"}
settings_file = "custom" #@param ["custom", "512x512_aesthetic_0.json","512x512_aesthetic_1.json","512x512_colormatch_0.json","512x512_colormatch_1.json","512x512_colormatch_2.json","512x512_colormatch_3.json"]
custom_settings_file = "/content/drive/MyDrive/Settings.txt"#@param {type:"string"}

def DeforumArgs():
#@markdown Image Settings
W = 512 #@param
H = 512 #@param
W, H = map(lambda x: x - x % 64, (W, H)) # resize to integer multiple of 64
bit_depth_output = 8 #@param [8, 16, 32] {type:"raw"}

#@markdown **Sampling Settings**
seed = -1 #@param
sampler = 'ddim' #@param ["klms","dpm2","dpm2_ancestral","heun","euler","euler_ancestral","plms", "ddim", "dpm_fast", "dpm_adaptive", "dpmpp_2s_a", "dpmpp_2m"]
steps = 50 #@param
scale = 10 #@param
ddim_eta = 0.0 #@param
dynamic_threshold = None
static_threshold = None   

#@markdown **Save & Display Settings**
save_samples = True #@param {type:"boolean"}
save_settings = True #@param {type:"boolean"}
display_samples = True #@param {type:"boolean"}
save_sample_per_step = False #@param {type:"boolean"}
show_sample_per_step = False #@param {type:"boolean"}

#@markdown **Batch Settings**
n_batch = 1 #@param
n_samples = 1 #@param
batch_name = "StableFun" #@param {type:"string"}
filename_format = "{timestring}_{index}_{prompt}.png" #@param ["{timestring}_{index}_{seed}.png","{timestring}_{index}_{prompt}.png"]
seed_behavior = "iter" #@param ["iter","fixed","random","ladder","alternate"]
seed_iter_N = 1 #@param {type:'integer'}
make_grid = False #@param {type:"boolean"}
grid_rows = 2 #@param 
outdir = get_output_folder(root.output_path, batch_name)

#@markdown **Init Settings**
use_init = False #@param {type:"boolean"}
strength = 0.65 #@param {type:"number"}
strength_0_no_init = True # Set the strength to 0 automatically when no init image is used
init_image = "https://cdn.pixabay.com/photo/2022/07/30/13/10/green-longhorn-beetle-7353749_1280.jpg" #@param {type:"string"}
add_init_noise = False #@param {type:"boolean"}
init_noise = 0.01 #@param
# Whiter areas of the mask are areas that change more
use_mask = False #@param {type:"boolean"}
use_alpha_as_mask = False # use the alpha channel of the init image as the mask
mask_file = "https://www.filterforge.com/wiki/images/archive/b/b7/20080927223728%21Polygonal_gradient_thumb.jpg" #@param {type:"string"}
invert_mask = False #@param {type:"boolean"}
# Adjust mask image, 1.0 is no adjustment. Should be positive numbers.
mask_brightness_adjust = 1.0  #@param {type:"number"}
mask_contrast_adjust = 1.0  #@param {type:"number"}
# Overlay the masked image at the end of the generation so it does not get degraded by encoding and decoding
overlay_mask = True  # {type:"boolean"}
# Blur edges of final overlay mask, if used. Minimum = 0 (no blur)
mask_overlay_blur = 5 # {type:"number"}

#@markdown **Exposure/Contrast Conditional Settings**
mean_scale = 0 #@param {type:"number"}
var_scale = 0 #@param {type:"number"}
exposure_scale = 0 #@param {type:"number"}
exposure_target = 0.5 #@param {type:"number"}

#@markdown **Color Match Conditional Settings**
colormatch_scale = 0 #@param {type:"number"}
colormatch_image = "https://www.saasdesign.io/wp-content/uploads/2021/02/palette-3-min-980x588.png" #@param {type:"string"}
colormatch_n_colors = 4 #@param {type:"number"}
ignore_sat_weight = 0 #@param {type:"number"}

#@markdown **CLIP\Aesthetics Conditional Settings**
clip_name = 'ViT-L/14' #@param ['ViT-L/14', 'ViT-L/14@336px', 'ViT-B/16', 'ViT-B/32']
clip_scale = 0 #@param {type:"number"}
aesthetics_scale = 0 #@param {type:"number"}
cutn = 1 #@param {type:"number"}
cut_pow = 0.0001 #@param {type:"number"}

#@markdown **Other Conditional Settings**
init_mse_scale = 0 #@param {type:"number"}
init_mse_image = "https://cdn.pixabay.com/photo/2022/07/30/13/10/green-longhorn-beetle-7353749_1280.jpg" #@param {type:"string"}
blue_scale = 0 #@param {type:"number"}

#@markdown **Conditional Gradient Settings**
gradient_wrt = 'x0_pred' #@param ["x", "x0_pred"]
gradient_add_to = 'both' #@param ["cond", "uncond", "both"]
decode_method = 'linear' #@param ["autoencoder","linear"]
grad_threshold_type = 'dynamic' #@param ["dynamic", "static", "mean", "schedule"]
clamp_grad_threshold = 0.2 #@param {type:"number"}
clamp_start = 0.2 #@param
clamp_stop = 0.01 #@param
grad_inject_timing = list(range(1,10)) #@param

#@markdown **Speed vs VRAM Settings**
cond_uncond_sync = True #@param {type:"boolean"}
precision = 'autocast' 
C = 4
f = 8

cond_prompt = ""
cond_prompts = ""
uncond_prompt = ""
uncond_prompts = ""
timestring = ""
init_latent = None
init_sample = None
init_sample_raw = None
mask_sample = None
init_c = None
seed_internal = 0

return locals()

args_dict = DeforumArgs()
anim_args_dict = DeforumAnimArgs()

if override_settings_with_file:
load_args(args_dict, anim_args_dict, settings_file, custom_settings_file, verbose=False)

args = SimpleNamespace(**args_dict)
anim_args = SimpleNamespace(**anim_args_dict)

args.timestring = time.strftime('%Y%m%d%H%M%S')
args.strength = max(0.0, min(1.0, args.strength))

Load clip model if using clip guidance

if (args.clip_scale > 0) or (args.aesthetics_scale > 0):
root.clip_model = clip.load(args.clip_name, jit=False)[0].eval().requires_grad_(False).to(root.device)
if (args.aesthetics_scale > 0):
root.aesthetics_model = load_aesthetics_model(args, root)

if args.seed == -1:
args.seed = random.randint(0, 2**32 - 1)
if not args.use_init:
args.init_image = None
if args.sampler == 'plms' and (args.use_init or anim_args.animation_mode != 'None'):
print(f"Init images aren't supported with PLMS yet, switching to KLMS")
args.sampler = 'klms'
if args.sampler != 'ddim':
args.ddim_eta = 0

if anim_args.animation_mode == 'None':
anim_args.max_frames = 1
elif anim_args.animation_mode == 'Video Input':
args.use_init = True

clean up unused memory

gc.collect()
torch.cuda.empty_cache()

get prompts

cond, uncond = Prompts(prompt=prompts,neg_prompt=neg_prompts).as_dict()

dispatch to appropriate renderer

if anim_args.animation_mode == '2D' or anim_args.animation_mode == '3D':
render_animation(root, anim_args, args, cond, uncond)
elif anim_args.animation_mode == 'Video Input':
render_input_video(root, anim_args, args, cond, uncond)
elif anim_args.animation_mode == 'Interpolation':
render_interpolation(root, anim_args, args, cond, uncond)
else:
render_image_batch(root, args, cond, uncond)
`